The following is the list of all requirements across all the tools. It is helpful to view an abstract hierarchy of the tools, showing R2D at the top and the components at the bottom. Each application pulls in many of the requirements from the others. For Example PBE, builds upon EE-UQ. It has it;’s own requirements, i.e. DL, but includes the loading, modeling and analysis requirements from EE-UQ. It specializes the UQ requirement, in that it only incorporates the sampling option.
R2D¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
R2D |
Ability to perform regional simulation allowing communities to evaluate resilience and perform what-if types of analysis for natural hazard events |
GC |
M |
InProgress |
R2D1 |
Include Various Hazards |
GC |
M |
InProgress |
R2D1.1 |
Ability to perform simulations for ground shaking due to earthquakes using methods defined in EL1 |
GC |
M |
Implemented |
R2D1.2 |
Ability to perform simulations for wave action due to earthquake induced tsunami using methods defined in HL1 |
GC |
M |
|
R2D1.3 |
Ability to perform simulations for wind action due to hurricane using methods defined in WL1 |
GC |
M |
InProgress |
R2D1.4 |
Ability to perform simulations for wave action due to hurricane storm surge using methods defined in HL1 |
GC |
M |
|
R2D1.5 |
Ability to perform multi-hazard simulations: wind + storm surge, rain, wind and water borne debris |
GC |
M |
|
R2D1.6 |
Ability to utilize machine learning ensemble techniques in hazard simulation |
GC |
M |
|
R2D1.7 |
Ability to incorporate surrogate models in hazard simulation |
SP |
M |
|
R2D1.8 |
Ability to incorporate multi-scale models in hazard simulation |
SP |
M |
|
R2D1.9 |
Ability to incorporate ground deformation hazards for pipes, roadways, and other infrastructure |
SP |
M |
|
R2D2 |
Include Different Asset Types |
GC |
M |
InProgress |
R2D2.1 |
Ability to incorporate building assets |
GC |
M |
Implemented |
R2D2.1.1 |
Ability to incorporate multi-fidelity building model asset descriptions |
GC |
M |
|
R2D2.2 |
Ability to incorporate transportation networks |
GC |
M |
|
R2D2.3 |
Ability to incorporate utility networks |
GC |
M |
|
R2D2.3.1 |
Methods to overcome national security issues with certain utility data |
SP |
M |
|
R2D2.4 |
Ability to incorporate surrogate models in asset modeling |
SP |
M |
|
R2D3 |
Include Different Analysis options |
GC |
M |
InProgress |
R2D3.1 |
Ability to include multi-scale nonlinear models |
GC |
M |
Implemented |
R2D4 |
Include Different Damage & Loss options |
GC |
M |
InProgress |
R2D4.1 |
Ability to include building-level earthquake damage and loss assessment from HAZUS |
SP |
M |
Implemented |
R2D4.2 |
Ability to include high-resolution earthquake damage and loss assessment for buildings from FEMA P58 |
SP |
M |
Implemented |
R2D4.3 |
Ability to include building-level wind damage and loss assessment from HAZUS |
SP |
M |
|
R2D4.4 |
Ability to include building-level water damage and loss assessment from HAZUS |
SP |
M |
|
R2D4.5 |
Ability to include earthquake damage and loss assessment for transportation networks from HAZUS |
SP |
M |
|
R2D4.6 |
Ability to include earthquake damage and loss assessment for buried pipelines from HAZUS |
SP |
M |
|
R2D4.7 |
Ability to include earthquake damage and loss assessment for power lines from HAZUS |
SP |
M |
|
R2D4.8 |
Ability to include high-resolution wind damage and loss assessment for buildings |
SP |
M |
|
R2D4.9 |
Ability to include high-resolution water damage and loss assessment for buildings |
SP |
M |
|
R2D4.10 |
Ability to include high-resolution damage and loss assessment for transportation networks |
SP |
M |
|
R2D4.11 |
Ability to include high-resolution damage and loss assessment for buried pipelines |
SP |
M |
|
R2D5 |
Include Different Response/Recovery options |
GC |
M |
|
R2D5.1 |
Response/Recovery options for households |
SP |
M |
|
R2D5.2 |
Response/Recovery options for infrastructure |
SP |
M |
|
R2D5.3 |
Response/Recovery options for business operations |
SP |
M |
|
R2D5.4 |
Response/Recovery and Effect on Environment |
SP |
M |
|
R2D5.4.1 |
CO2 emissions from demolition and repair |
SP |
M |
|
R2D6 |
Present results using GIS so communities can visualize hazard impacts |
GC |
M |
Implemented |
R2D6.1 |
Ability to use popular ArcGIS for visualization |
SP |
M |
Implemented |
R2D6.2 |
Ability to include open-source ArcGIS alternatives |
SP |
P |
|
R2D6.3 |
Ability to capture uncertainty of results in visualization |
SP |
P |
|
R2D6.4 |
Features to visualize environmental impact |
SP |
P |
|
R2D7 |
Software Features |
GC |
M |
InProgress |
R2D7.1 |
Ability to include a formal treatment of uncertainty and randomness |
GC |
M |
Implemented |
R2D7.2 |
Ability to utilize HPC resources in regional simulations that enables repeated simulation for stochastic modeling |
GC |
M |
Implemented |
R2D7.3 |
Ability to use a tool created by linking heterogeneous array of simulation tools to provide a toolset for regional simulation |
GC |
M |
Implemented |
R2D7.4 |
Provide open-source software for developers to test new data and algorithms |
GC |
M |
Implemented |
R2D7.5 |
Ability of stakeholders to perform simulations of different scenarios that aid in planning and response after damaging events |
GC |
M |
|
RDT7.7 |
Ability to explore different strategies in community development, pre-event, early response, and post event, through long term recovery |
GC |
P |
|
RDT7.8 |
Ability to use system that creates and monitors real-time data, updates models, incorporates crowdsourcing technologies, and informs decision makers |
GC |
P |
|
RDT7.9 |
Ability to use sensor data to update models for simulation and incorporate sensor data into simulation |
GC |
P |
|
R2D7.10 |
Ability to include latest information and algorithms (i.e. new attenuation models, building fragility curves, demographics, lifeline performance models, network interdependencies, indirect economic loss) |
GC |
D |
|
R2D7.11 |
Incorporate programs that can address lifeline network disruptions and network interdependencies |
GC |
M |
|
R2D7.12 |
Application to Provide Common SimCenter Research Application Requirements listed in CR (not already listed above) |
GC |
M |
InProgresss |
PBE¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
PBE |
Integrate fully coupled multi-model computations from hazard source through structure response, to compute reliable estimates of financial loss, business interruption, and casualties |
GC |
M |
|
PBE1 |
Ability to determine damage and loss for multiple different hazards |
GC |
M |
InProgress |
PBE1.1 |
Damage and Loss due to ground shaking from Earthquake |
GC |
M |
Implemented |
PBE1.2 |
Damage and Loss due to Wind Loading |
GC |
M |
InProgress |
PBE1.3 |
Damage and Loss due to water damage from Tsunami or Coastal Inundation |
GC |
M |
|
PBE2 |
Ability to Select from Different Hazard Options |
|||
PBE2.1 |
Ability to select from all EE-UQ Event Options listed in EE-UQ |
SP |
M |
Implemented |
PBE2.2 |
Ability to select from all WE-UQ Event Options listed in WE-UQ |
SP |
M |
|
PBE2.3 |
Ability to select from all HydroUQ Event Options listed in Hydro-UQ |
SP |
M |
|
PBE3 |
Ability to use different Model Generation Tools |
|||
PBE3.1 |
Ability to Select All Building Model Generators in EE-UQ |
SP |
M |
Implemented |
PBE3.2 |
Ability to Select All Building Model Generators in WE-UQ |
SP |
M |
|
PBE3.3 |
Ability to Select All Building Model Generators in HydroUQ |
SP |
M |
|
PBE4 |
Ability to use Various UQ Methods and Variable Options |
|||
PBE4.1 |
Ability to use all forward propagation methods available in EE-UQ, WE-UQ and HydroUQ |
SP |
M |
Implemented |
PBE4.2 |
Ability to use all random variable distributions in EE-UQ, WE-UQ and HydroUQ |
SP |
M |
Implemented |
PBE4.3 |
Ability to use train surrogate models using the methods from quoFEM |
SP |
D |
|
PBE5 |
Ability to determine damage and loss utilizing different methods |
SP |
M |
Implemented |
PBE5.1 |
Interface with pelicun to make available its suite of methods for damage and loss assessment for buildings |
SP |
M |
Implemented |
PBE6 |
Misc User Requests |
|||
PBE6.1 |
Ability to Process own Output Parameters |
UF |
D |
|
PBE6.2 |
Add to Standard Earthquake a variable indicating analysis failure |
UF |
D |
|
PBE6.3 |
Allow users to provide their own set of EDPs for the analysis. |
UF |
D |
Implemented |
PBE6.4 |
Simplify run local and run remote by removing workdir locations. Move to preferences |
UF |
D |
Implemented |
PBE6.5 |
Add to EDP a variable indicating analysis failure |
UF |
D |
|
PBE6.6 |
Enable saving and loading Performance Models in CSV files |
UF |
D |
Implemented |
PBE7 |
General Software Requirements |
|||
PBE7.1 |
Application to Provide Common SimCenter Research Application Requirements listed in CR |
GC |
M |
InProgresss |
PBE7.2 |
Ability to use new vizualization tools for viewing large datasets generated by PBE |
GC |
M |
Implemented |
WE-UQ Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
WE |
Ability to determine response of Building Subject to Wind Loading including formal treatment of randomness and uncertainty uncertainty |
GC |
M |
Implemented |
WE1 |
Adaptation of non-linear analysis methods used in seismic design |
GC |
M |
Implemented |
WE1.1 |
Include ability to create models incorprating options listed in MOD under BM |
SP |
M |
Implemented |
WE1.2 |
Include ability to perform nonlinear analysis on the building models listed in ANA |
SP |
M |
Implemented |
WE2 |
Ability to select from Wind Loading Options listed in WL2 |
SP |
M |
Implemented |
WE3 |
Ability to use Various UQ Methods and Variable Options |
|||
WE3.1 |
Ability to use Forward Propagtion methods listed in UQ under UF |
SP |
M |
Implemented |
WE3.2 |
Ability to use Reliability Methods listed in UQ under UR |
SP |
M |
Implemented |
WE3.3 |
Ability to use Global Sensitivity Methods listed in UQ under UG |
SP |
M |
Implemented |
WE3.4 |
Ability to both use and create surrogates listed in UQ under US |
SP |
M |
|
WE3.5 |
Ability to use High Dimensional UQ listed in UQ under UH |
SP |
M |
|
WE4 |
Ability to see pressure distribution on buildings |
GC |
M |
|
WE5 |
Ability to obtain basic building responses |
SP |
M |
|
WE6 |
Ability to Visualize the Results |
SP |
M |
Implemented |
WE6.1 |
Ability to view individual sample results |
SP |
M |
Implemented |
WE6.2 |
Ability to graphically view the results to show distribution in respone |
SP |
M |
Implemented |
WE7 |
Misc User Requests |
|||
WE7.1 |
Ability to Process own Output Parameters |
UF |
M |
Implemented |
WE7.2 |
Ability to Remove from Results certain Samples, e.g. ones that failed in analysis |
UF |
M |
Implemented |
WE7.3 |
Create a digital twin of the Wall of Wind facility to allow researchers to simulate experiments |
UF |
M |
|
WE8 |
Tool should incorporate data from www |
GC |
M |
Implemented |
WE8.1 |
Tool could obtain loading from Vortex Winds over www |
SP |
M |
Implemented |
WE8.2 |
Tool should obtain loading info from TPU wind tunnel tests |
SP |
D |
Implemented |
WE8.3 |
Tool should obtain building modelling info from database through www |
SP |
D |
|
Hydro-UQ Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
H |
Application to determine response of Building Subject Water Action due to Storm Surge or Tsunami including formal treatment of randomness and uncertainty |
GC |
M |
InProgress |
H1 |
Quantification of flood-borne debris hazards |
GC |
M |
|
H2 |
Effects of over-land flow, including waves, debris, flood velocity, wind-driven influences, erosion effects at buildings and channeling effects of the built environment |
GC |
D |
|
H3 |
Ability to select from all Loading Options listed in HL2 |
SP |
M |
InProgress |
H4 |
Ability to select from Building Modeling Options listed in MOD under BM |
SP |
M |
Implemented |
H5 |
Include ability to perform nonlinear analysis on the building models listed in ANA |
SP |
M |
Implemented |
H6 |
Ability to use Various UQ Methods and Variable Options |
|||
H7.1 |
Ability to use Forward Propagtion methods listed in UQ under UF |
SP |
M |
Implemented |
H7.2 |
Ability to use Random Variable Distributions defeined in RV |
SP |
M |
|
H7.3 |
Ability to use Reliability Methods listed in UQ under UR |
SP |
M |
Implemented |
H7.4 |
Ability to use Global Sensitivity Methods listed in UQ under UG |
SP |
M |
Implemented |
H7.5 |
Ability to both use and create surrogates listed in UQ under US |
SP |
M |
|
H7.6 |
Ability to use High Dimensional UQ listed in UQ under UH |
SP |
M |
|
H8 |
Ability to Visualize the Results |
SP |
M |
Implemented |
H8.1 |
Ability to view individual sample results |
SP |
M |
Implemented |
H8.2 |
Ability to graphically view the results to show distribution in respone |
SP |
M |
Implemented |
H9 |
Misc User Requests |
|||
EE9.1 |
Ability to quickly model experimental tests perform in OSU wave tank |
UF |
M |
|
H10 |
General Software Requirements |
|||
H10.1 |
Application to Provide Common SimCenter Research Application Requirements listed in CR |
GC |
M |
InProgresss |
H11 |
Tool should incorporate data from www |
GC |
M |
|
H11.1 |
Tool should use satelite imagery in aid of determine channeling effect |
SP |
D |
|
H11.2 |
Tool should use satelite imagery in aid of determining amount of debris |
SP |
D |
|
H11.3 |
Tool should obtain building modelling info from database through www |
SP |
D |
|
EE-UQ Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
EE |
Application to determine response of Building Subject to Earthquake hazard including formal treatment of randomness and uncertainty |
GC |
M |
Implemented |
EE1 |
Ability to select from Earthquake Loading Options listed in EL2 |
SP |
M |
Implemented |
EE2 |
Ability to select from Building Modeling Options listed in MOD under BM |
SP |
M |
Implemented |
EE3 |
Ability to select from nonlinear analysis options listed in ANA |
SP |
M |
Implemented |
EE4 |
Ability to use Various UQ Methods and Variable Options |
|||
EE4.1 |
Ability to use Forward Propagtion methods listed in UQ under UF |
SP |
M |
Implemented |
EE4.2 |
Ability to use Random Variable Distributions defeined in RV |
SP |
M |
|
EE4.3 |
Ability to use Reliability Methods listed in UQ under UR |
SP |
M |
Implemented |
EE4.4 |
Ability to use Global Sensitivity Methods listed in UQ under UG |
SP |
M |
Implemented |
EE4.5 |
Ability to both use and create surrogates listed in UQ under US |
SP |
M |
|
EE4.6 |
Ability to use High Dimensional UQ listed in UQ under UH |
SP |
M |
|
EE5 |
Ability to Visualize the Results |
SP |
M |
Implemented |
EE5.1 |
Ability to view individual sample results |
SP |
M |
Implemented |
EE5.2 |
Ability to graphically view the results to show distribution in respone |
SP |
M |
Implemented |
EE6 |
Misc User Requests |
|||
EE6.1 |
Add to Standard Earthquake a variable indicating analysis failure |
UF |
D |
|
EE6.3 |
Run application from command line, include option to run remotely |
UF |
D |
|
EE7 |
General Software Requirements |
|||
EE7.1 |
Application to Provide Common SimCenter Research Application Requirements listed in CR |
GC |
M |
InProgresss |
EE8 |
Tool should incorporate data from www |
GC |
M |
Implemented |
EE8.1 |
Tool should obtain motion input data from www |
SP |
M |
Implemented |
EE8.2 |
Tool should obtain building modelling info from database through www |
SP |
D |
|
quoFEM Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
QF |
Application to Promote and aid use of UQ Methods in NHE Research for response estimation, surrogate modeling, and calibration |
GC |
M |
Implemented |
QF1 |
Ability to use Various UQ Methods and Variable Options |
|||
QF1.1 |
Ability to use Forward Propagtion methods listed in UQ under UF |
SP |
M |
Implemented |
QF1.2 |
Ability to use Random Variable Distributions defeined in RV |
SP |
M |
|
QF1.3 |
Ability to use Reliability Methods listed in UQ under UR |
SP |
M |
Implemented |
QF1.4 |
Ability to use Global Sensitivity Methods listed in UQ under UG |
SP |
M |
Implemented |
QF1.5 |
Ability to both use and create surrogates listed in UQ under US |
SP |
M |
InProgress |
QF1.6 |
Ability to use High Dimensional UQ listed in UQ under UH |
SP |
M |
InProgress |
QF1.7 |
Ability to use Bayesian Calibration methods listed in UQ under UB |
SP |
M |
|
QF1.8 |
Ability to use Nonlinear Least Squares methods listed in UQ under UN |
SP |
M |
Implemented |
QF2 |
Ability to use Different Simulation Applications |
|||
QF2.1 |
Ability to use OpenSees |
SP |
M |
Implemented |
QF2.2 |
Ability to use OpenSeesPy |
SP |
M |
Implemented |
QF2.3 |
Ability to use OpenSeesPy |
UF |
M |
Implemented |
QF2.3 |
Ability to Incorporate User Own Applkications |
UF |
M |
Implemented |
QF2 |
Ability to Visualize the Results |
SP |
M |
Implemented |
QF2.1 |
Ability to view individual sample results |
SP |
M |
Implemented |
QF2.2 |
Ability to graphically view the results to show distribution in respone |
SP |
M |
Implemented |
QF2.2 |
Ability to view statistical measures of response |
SP |
M |
Implemented |
QF3 |
Misc User Requests |
|||
QF3.1 |
Run application from command line, include option to run remotely |
UF |
D |
|
QF7 |
General Software Requirements |
|||
QF7.1 |
Application to Provide Common SimCenter Research Application Requirements listed in CR |
GC |
M |
InProgresss |
Earthquake Loading Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
EL |
Loading from Earthquake Hazard on Local and Regional Assets |
GC |
M |
InProgress |
EL1 |
Regional Scale Earthquake Hazard Simulation Options |
|||
EL1.1 |
Coupling of multi-scale nonlinear models from the point of rupture through rock and soil into structure |
GC |
M |
|
EL1.1.1 |
Replacement of empirical linear models with multi-scale nonlinear models as will represent a paradigm shift in engineering science |
GC |
D |
|
EL<IP_ADDRESS> |
Interface to regional simulations using DRM method |
SP |
M |
|
EL1.1.2 |
Include both multi-scale and multi-phase (account for liquefaction) |
GC |
M |
|
EL1.2 |
Method to include both the intra-event residual and inter-event residual in simulating spatial correlated ground motion intensity measures with multiple correlation model options. Select site specific grouind motions from PEER to match target intensity |
SP |
M |
Implemented |
EL1.3 |
Use GIS-Specified Matrix of Recorded Motions |
SP |
M |
Implemented |
EL2 |
Select from Multiple Local Scale Earthquake Hazard Options |
|||
EL2.1 |
Coupling of multi-scale nonlinear models from the point of rupture through rock and soil into structure |
GC |
M |
|
EL2.1 |
Ability to select utilizing PEER NGA_West web service |
SP |
D |
Implemented |
EL2.1.1 |
Select using default selection options |
SP |
D |
Implemented |
EL2.1.1 |
Select using all options available at PEER site |
UF |
D |
Implemented |
EL2.1.2 |
Select using user supplied spectrum |
UF |
D |
Implemented |
EL2.2 |
Ability to select from list of user supplied PEER motions |
SP |
M |
Implemented |
EL2.3 |
Ability to select from list of SimCenter motions |
SP |
M |
Implemented |
EL2.4 |
Ability to use OpenSHA and selection methods to generate motions |
UF |
D |
|
EL2.5 |
Ability to Utilize Own Application in Workflow |
SP |
M |
Implemented |
EL2.6 |
Ability to use Broadband |
SP |
D |
|
EL2.7 |
Ability to include Soil Structure Interaction Effects |
GC |
M |
Implemented |
EL2.7.1 |
1D nonlinear site response with effective stress analysis |
SP |
M |
Implemented |
EL2.7.2 |
Nonlinear site response with bidirectional loading |
SP |
M |
Implemented |
EL2.7.3 |
Nonlinear site response with full stochastic characterization of soil layers |
SP |
M |
Implemented |
EL2.7.4 |
Nonlinear site response, bidirectional different input motions |
SP |
M |
|
EL2.8 |
Ability to select from synthetic ground motions |
SP |
M |
Implemented |
EL2.8.1 |
per Vlachos, Papakonstantinou, Deodatis (2017) |
SP |
D |
Implemented |
EL2.8.2 |
per Dabaghi, Der Kiureghian (2017) |
UF |
D |
Implemented |
Wind Loading Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
WL |
Loading from Wind Hazards (Hurricane, Downbursts, Tornados) on Local and Regional Assets |
GC |
M |
InProgress |
WL1 |
Regional Loading due to Wind Hazards |
GC |
M |
InProgress |
WL1.1 |
Regional Hurricane Wind Options |
GC |
M |
InProgress |
WL1.1.1 |
Utilize GIS and online to account for wind speed given local terrain, topography and nearby buildings |
GC |
D |
|
WL1.1.2 |
MultiScale Wind Models, |
SP |
D |
|
WL1.1.3 |
Multi-scale models for wind and water flows, i.e. lower fidelity regional models with more refined models to capture local flow |
SP |
D |
|
WL1.2 |
Modeling and simulation for determination of wind loads due to non-synoptic winds, including tornadoes |
GC |
D |
|
WL1.3 |
Interface with Open-Source Applications that can provide Hazard loading |
GC |
D |
|
WL1.3.1 |
Interface with NOAA |
SP |
D |
|
WL2 |
Local Scale Wind Hazard Options |
|||
WL2.1 |
Utilize Extensive wind tunnel datasets in industry and academia for wide range of building shapes |
GC |
M |
Implemented |
WL2.1.1 |
Accommodate Range of Low Rise building shapes |
SP |
M |
|
WL<IP_ADDRESS> |
Flat Shaped Roof - TPU dataset |
SP |
M |
Implemented |
WL<IP_ADDRESS> |
Gable Shaped Roof - TPU dataset |
SP |
M |
|
WL<IP_ADDRESS> |
Hipped Shaped Roof - TPU dataset |
SP |
M |
|
WL2.1.2 |
Accommodate Range of High Rise building |
SP |
M |
InProgress |
WL2.1.3 |
Non Isolated Low Rise Buildings - TPU dataset |
SP |
M |
InProgress |
WL2.2 |
Interface with data driven |
GC |
M |
InProgress |
WL2.2.1 |
Interface with Vortex Winds DEDM-HRP Web service |
SP |
M |
Implemented |
WL2.3 |
Accommodate Data from Wind Tunnel Experiment |
SP |
M |
Implemented |
WL2.4 |
Simple CFD model generation and turbulence modeling |
GC |
M |
Implemented |
WL2.5 |
Computational Fluid Dynamics tool for utilizing open source CFD software for practitioners |
GC |
M |
Implemented |
WL2.5.1 |
Uncoupled OpenFOAM CFD model with nonlinear FEM code for building response |
SP |
M |
Implemented |
WL2.5.1 |
Coupled OpenFOAM CFD model with linear FEM code for building response |
SP |
M |
InProgress |
WL2.5.2 |
Inflow Conditions for non-synoptic winds |
GC |
M |
|
WL2.6 |
Quantification of Effects of Wind Borne Debris |
GC |
D |
|
WL2.7 |
Ability to utilize synthetic wind loading algorithms |
SP |
M |
Implemented |
WL2.7.1 |
per Wittig and Sinha |
SP |
D |
Implemented |
WL2.8 |
Hazard modification by terrain, topography, and nearby buildings |
GC |
D |
|
WL2.9 |
Probabilistic methods are needed to enable site-specific and storm-type specific characterization of likely debris types, weights, and speeds |
GC |
D |
|
Wl2.10 |
Joint description for hurricane wind, storm surge, and wave hazards |
GC |
D |
|
WL2.11 |
Libraries of high resolution hurricane wind/surge/wave simulations |
GC |
M |
|
WL2.11 |
Libraries of high resolution hurricane wind/surge/wave simulations |
GC |
M |
|
WL2.12 |
Multi-scale models for wind and water flows, i.e. lower fidelity regional models with more refined models to capture local flow |
SP |
Surge/Tsunami Loading Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
HL |
Loading from Storm Surge/Tsunami on Local and Regional Assets |
GC |
M |
InProgress |
HL1 |
Regional Loading due to Storm Surge/Tsunami Hazards |
GC |
M |
InProgress |
HL1.1 |
Multi-scale models for wind and water flows, i.e. lower fidelity regional models with more refined models to capture local flow |
SP |
D |
|
HL2 |
Local Scale Storm Surge/Tsunami Hazard Options |
|||
HL2.1 |
Using computational fluid dynamics to model interface and impact between water loads and buildings |
GC |
M |
|
HL2.1.1 |
CFD to model fluid flow around a single rigid structure |
SP |
M |
|
HL2.1.2 |
Mesh refinement around structures |
SP |
M |
|
HL2.1.3 |
CFD to model fluid flow around a single deformable structure |
SP |
M |
|
HL2.1.4 |
CFD to model fluid flow considering inflow and accumulation of fluid inside a rigid structure |
SP |
M |
|
HL2.1.5 |
CFD to model fluid flow considering inflow, accumulation, and possible outflow of fluid across a deformable structure |
SP |
M |
|
HL2.2 |
Quantification of flood-borne debris hazards |
GC |
M |
|
H2.2.1 |
Ability to quantify the effect of unconstrained and non-colliding floating |
SP |
M |
|
H2.2.2 |
Ability to quantify the effect of colliding flood-borne debris |
SSP |
M |
|
H2.2.3 |
Explore multiple methods like Material Point Method (MPM), Immersed Boundary Method (IBM), DEM-CFD, particle tracking |
SP |
M |
|
H2.2.4 |
Integrate one of the methods for integrating particles with Hydro workflow |
GC |
M |
|
HL2.3 |
load combinations need to be developed to account for the simultaneous impacts of various flood forces, such as those generated by breaking waves, moving water and flood-borne debris |
GC |
||
HL2.5 |
Multi-scale models for wind and water flows, i.e. lower fidelity regional models with more refined models to capture local flow |
SP |
||
HL2.5.1 |
Interface GeoClaw and OpenFOAM |
SP |
M |
|
HL2.5.2 |
Interface AdCirc and OpenFOAM |
SP |
M |
|
HL2.6 |
Libraries of high resolution hurricane wind/surge/wave simulations |
GC |
M |
|
HL2.6.1 |
Develop a simulation library of GeoClaw simulations |
SP |
M |
|
HL2.6.2 |
Develop a simulation library of AdCirc simulations |
SP |
M |
|
HL2.6.3 |
Develop a simulation library of OpenFOAM simulations |
SP |
M |
|
HL2.7 |
Ability to simulate with surrogate models as alternative to full 3D CFD |
SP |
M |
|
HL2.8 |
Develop digital twin with OSU wave Tank Facility |
SP |
M |
|
UQ Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
UF |
Forward Propagation Methods |
GC |
M |
InProgress |
UF1 |
Ability to use basic Monte Carlo and LHS methods |
SP |
M |
Implemented |
UF2 |
Ability to use Multi-Scale Monte Carlo |
SP |
M |
|
UF3 |
Ability to use Multi-Fidelity Models |
SP |
M |
|
UR |
Reliability Methods |
SP |
M |
Implemented |
UR1 |
Ability to use First Order Reliability method |
SP |
M |
Implemented |
UR2 |
Ability to use Second Order Reliability method |
SP |
M |
Implemented |
UR3 |
Ability to use Surrogate Based Reliability |
SP |
M |
Implemented |
UR4 |
Ability to use Importance Sampling |
SP |
M |
Implemented |
UG |
Global Sensitivity Methods |
SP |
M |
Implemented |
UG1 |
Ability to obtain Global Sensitivity Sobol indices |
UF |
M |
Implemented |
UG2 |
Ability to use probability model-based global sensitivity analysis (PM-GSA) |
SP |
M |
Implemented |
US |
Surrogate Models |
UF |
M |
InProgress |
US1 |
Ability to Construct Gaussian Process (GP) Regression Model from a Simulation Model |
SP |
M |
InProgress |
US2 |
Ability to Construct GP Regression Model from Input-output Dataset |
SP |
M |
InProgress |
US3 |
Ability to use Surrogate Model for UQ Analysis |
SP |
M |
InProgress |
US4 |
Ability to Save the Surrogate Model |
SP |
M |
InProgress |
US5 |
Ability to Use Adaptive Design of Experiments |
SP |
M |
InProgress |
US6 |
Ability to Asses Reliability of Surrogate Model |
SP |
M |
InProgress |
US7 |
Ability to Build Surrogate Under Stochastic Excitation |
SP |
M |
|
US8 |
Ability to Use Physics-Informed Machine Learning |
SP |
M |
|
UN |
Non-linear Least Squares Optimization |
SP |
M |
InProgress |
UN1 |
Ability to use Gauss-Newton solvers for parameter estimation |
SP |
M |
Implemented |
UN2 |
Ability to read calibration data from file |
UF |
M |
InProgress |
UN3 |
Ability to handle non-scalar response quantities |
UF |
M |
InProgress |
UB |
Bayesian Calibration |
SP |
M |
InProgress |
UB1 |
Ability to use DREAM algorithm for Bayesian inference |
SP |
M |
InProgress |
UB2 |
Ability to use TMCMC algorithm for Bayesian inference |
SP |
M |
Implemented |
UB3 |
Ability to read calibration data from file |
UF |
M |
InProgress |
UB4 |
Ability to handle non-scalar response quantities |
UF |
M |
InProgress |
UB5 |
Ability to calibrate multipliers on error covariance |
UF |
M |
|
UB6 |
Ability to use a default log-likelihood function |
UF |
M |
|
UB7 |
Ability to use Kalman Filtering |
UF |
M |
|
UB8 |
Ability to use Particle Filtering |
UF |
M |
|
UH |
High Dimensional UQ |
SP |
M |
|
UH1 |
Ability to sample from manifold |
SP |
M |
|
UH2 |
Ability to build Reduced Order Model |
SP |
M |
|
UO |
Other/General Features |
SP |
M |
|
UO1 |
Ability to use Own External UQ Engine |
SP |
M |
Implemented |
UO2 |
Ability to use Own External FEM Application |
UF |
M |
Implemented |
RV Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
RV |
Random Variables |
|||
RV1 |
Various Random Variable Probability Distributions |
SP |
M |
Implemented |
RV1.1 |
Normal |
SP |
M |
Implemented |
RV1.2 |
Lognormal |
SP |
M |
Implemented |
RV1.3 |
Uniform |
SP |
M |
Implemented |
RV1.4 |
Beta |
SP |
M |
Implemented |
RV1.5 |
Weibull |
SP |
M |
Implemented |
RV1.6 |
Gumbel |
SP |
M |
Implemented |
RV2 |
User defined Distribution |
SP |
M |
|
RV3 |
Define Correlation Matrix |
SP |
M |
|
RV4 |
Random Fields |
SP |
M |
|
RV5 |
Ability to View Graphically the density function when defining the RV |
UF |
D |
Implemented |
Modeling Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
MOD |
Asset Model Generators for Analysis |
|||
BM |
Asset Model Generators for Buildings |
|||
BM1 |
Ability to quickly create a simple nonlinear building model for simple methods of seismic evaluation |
GC |
D |
Implemented |
BM2 |
Ability to use existing OpenSees model scripts |
SP |
M |
Implemented |
BM3 |
Ability to define building and use Expert System to generate FE mesh |
SP |
D |
|
BM4 |
Ability to define building and use Machine Learning applications to generate FE |
GC |
D |
|
BM5 |
Ability to specify connection details for member ends |
UF |
D |
|
BM6 |
Ability to define a user-defined moment-rotation response representing the connection details |
UF |
D |
|
BM7 |
Ability to incoporate AutoSDA Steel Design Application in Local Applications |
UF |
M |
Implemented |
BM8 |
Ability to use user supplied python script to generate mesh |
UF |
M |
Implemented |
Analysis Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
ANA1 |
Ability to select from different Nonlinear Analysis options |
GC |
M |
Implemented |
ANA1.1 |
Ability to specify OpenSees as FEM engine and to specify different analysis options |
SP |
M |
Implemented |
ANA1.2 |
Ability to provide own OpenSees Analysis script to OpenSees engine. |
SP |
D |
Implemented |
ANA1.3 |
Ability to provide own Python script and use OpenSeesPy engine. |
SP |
D |
|
ANA1.4 |
Ability to use alternative FEM engines. |
SP |
M |
|
ANA2 |
Ability to know if an analysis run fails. |
UF |
M |
|
ANA2 |
Ability to specify Modal Damping. |
UF |
M |
Implemented |
ANA2.1 |
Ability to specify damping ratio as a random variable |
UF |
M |
Implemented |
ANA2.2 |
When using Rayleigh Damping, ability to specify the two modes used to calculate damping parameters |
UF |
M |
Implemented |
ANA3 |
Ability to run for more than 60hours at DesignSafe |
UF |
D |
|
ANA4 |
Ability to specify number of iterations in convergence test |
UF |
M |
Implemented |
Damage & Loss Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
DL |
Damage & Loss (DL) Predictions |
|||
DL1 |
Ability to use open-source version of Hazus |
GC |
M |
Implemented |
DL2 |
Ability to incorporate improved damage and fragility models for buildings and lifelines |
GC |
M |
InProgress |
DL3 |
Ability to perform time-based assessment |
GC |
M |
|
DL4 |
Methods for DL Prediction for Buildings for Various Hazards |
SP |
M |
|
DL4.1 |
Incorporate PACT application for earthquake hazard |
SP |
D |
|
DL4.2 |
Ability to perform downtime estimation using the REDi methodology for earthquakes. |
UF |
D |
|
DL4.3 |
Incorporate Various Methods from PELICUN |
SP |
M |
InProgress |
DL4.3.1 |
Incorporate the scenario-based assessment from FEMA-P58, item P6.1.1 |
SP |
M |
Implemented |
DL4.3.2 |
Incorporate the time-based assessment from FEMA-P58, item P1.1.2 |
SP |
D |
|
DL4.3.3 |
Incorporate high-resolution assessment of buildings under wind hazards, item P1.1.3 |
SP |
M |
|
DL4.3.4 |
Incorporate high-resolution assessment of buildings under water hazards, item P1.1.4 |
SP |
M |
|
DL4.3.5 |
Incorporate high-resolution assessment of transportation networks, item P1.1.5 |
SP |
M |
|
DL4.3.6 |
Incorporate high-resolution assessment of buried pipelines, item P1.1.6 |
SP |
M |
|
DL4.3.7 |
Incorporate the assessment of buildings under earthquake hazard from HAZUS, P1.2.1 |
SP |
M |
Implemented |
DL4.3.8 |
Incorporate the assessment of buildings under hurricane wind hazard from HAZUS, item P1.2.2 |
SP |
M |
Implemented |
DL4.3.9 |
Incorporate the assessment of buildings under storm surge hazard from HAZUS, item P1.2.3 |
SP |
M |
Implemented |
DL4.3.10 |
Incorporate the assessment of buried pipelines under earthquake hazard from HAZUS, item P1.2.4 |
SP |
M |
|
DL4.3.11 |
Incorporate the assessment of transportation networks under earthquake hazard from HAZUS, item P1.2.5 |
SP |
M |
|
DL4.3.12 |
Incorporate the assessment of power networks under earthquake hazard from HAZUS, item P1.2.6 |
SP |
M |
Recovery Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
REC |
Recovery (REC) Modeling |
|||
REC1 |
Building Recovery |
|||
REC1.1 |
Incorporate advanced methods for building recovery time estimation* |
SP |
M |
|
REC2 |
Housing and Community Recovery |
|||
REC2.1 |
Incorporate modeling of the recovery of households and communities |
SP |
M |
|
REC3 |
Infrastructure Recovery |
|||
REC3.1 |
Incorporate modeling of the recovery of transportation networks |
SP |
M |
|
REC3.2 |
Incorporate modeling of the recovery of buried pipeline networks |
SP |
M |
|
REC3.3 |
Incorporate modeling of the recovery of power networks |
SP |
M |
|
REC4 |
Business Recovery |
|||
REC4.1 |
Incorporate modeling of the recovery of businesses |
SP |
M |
|
REC4.2 |
Ability to incorporate improved indirect economic loss estimation models |
GC |
M |
|
REC4.3 |
Ability to include demand surge in determination of losses |
GC |
M |
|
REC5 |
Interdependencies |
|||
REC5.1 |
Implement a framework to model interdependencies between the recovery of various systems |
SP |
M |
|
REC5.2 |
Ability to include lifeline disruptions |
GC |
M |
|
REC6 |
Metrics of recovery |
|||
REC6.1 |
Implement metrics to inform recovery and community resilience based on the outputs of the available recovery models |
SP |
M |
|
Common Research Application Requirements¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
CR |
Common Requirements of all SimCenter Research Applications |
|||
CR1 |
Open-source software where developers can test new data and develop algorithms |
GC |
M |
Implemented |
CR1.1 |
Provide open-source applications utilizing code hosting platforms, e.g. GitHub |
SP |
M |
Implemented |
CR1.2 |
Assign an open-source licensce that allows free use. |
SP |
M |
Implemented |
CR2 |
Ability of Practicing Engineers to use multiple coupled resources (applications, databases, viz tools) in engineering practice |
GC |
M |
Implemented |
CR2.1 |
Allow users to launch scientifiv workflows |
SP |
M |
Implemented |
CR3 |
Ability to utilize resources beyond the desktop including HPC |
GC |
M |
Implemented |
CR3.1 |
Allow users to utilize HPC resources at TACC through DesignSafe |
SP |
M |
Implemented |
CR5 |
Tool available for download from web |
GC |
M |
Implemented |
CR5.1 |
Tool downloadable from DesignSafe website |
GC |
M |
Implemented |
CR6 |
Ability to benefit from programs that move research results into practice and obtain training |
GC |
M |
|
CR6.1 |
Ability to use educational provisions to gain interdisclipinary education so as to gain expertise in earth sciences and physics, engineering mechanics, geotechnical engineering, and structural engineering in order to be qualified to perform these simulations |
GC |
D |
|
CR6.1 |
Documentation exists demonstrainting application usage |
SP |
M |
Implemented |
CR6.2 |
Video Exists demonstrating application usage |
SP |
M |
Implemented |
CR6.3 |
Tool Training through online and in person training events |
SP |
M |
Implemented |
CR7 |
Verification Examples Exist |
SP |
M |
Implemented |
CR8 |
validation of proposed analytical models against existing empirical datasets |
GC |
M |
|
CR8.1 |
Validation Examples Exist, validated against tests or other software |
GC |
M |
|
CR9 |
Tool to allow user to load and save user inputs |
SP |
M |
Implemented |
CR10 |
Installer which installs application and all needed software |
UF |
D |
BRAILS¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
BR1.0 |
Need for scalable tools that autonomously create an accurate database of all infrastructure components, including points of inter- dependency with other infrastructure components |
GC |
M |
InProgress |
BR2.0 |
Promote living community risk models utilizing local inventory data from various sources |
GC |
M |
InProgress |
BR3.0 |
Developing and sharing standardized definitions, measurement protocols and strategies for data collection |
GC |
M |
|
BR4.0 |
Developing tools that utilize GIS information and online images, e.g. google maps, for data collection for gathering Building Information |
GC |
M |
|
BR4.1 |
|
SP |
M |
Implemented |
BR4.2 |
Create workflow application for building inventory from framework modules |
SP |
M |
Implemented |
BR4.2 |
Create workflow application for transportation network from framework modules |
SP |
M |
|
BR5 |
Developing Modules for Asset Inventory Workflows identified in BR4 |
|||
BR5.1 |
Predicting if building is a soft-story building for earthquake simulations |
UF |
M |
Implemented |
BR5.2 |
Predicting First Floor Height |
SP |
M |
InProgress |
BR5.3 |
Predicting Roof Height |
SP |
M |
InProgress |
BR5.4 |
Predicting Eave Height |
SP |
M |
InProgress |
BR5.5 |
Predicting Eave Length |
SP |
M |
|
BR5.6 |
Predicting Roof Shape |
SP |
M |
Implemented |
BR5.7 |
Predicting Roof Pitch |
SP |
M |
InProgress |
BR5.8 |
Predicting Roof Material |
SP |
M |
InProgress |
BR5.9 |
Predicting Window Area |
SP |
M |
InProgress |
BR5.10 |
Predicting Building Material |
SP |
M |
|
BR5.11 |
Classifying Elevated Building |
SP |
M |
Implemented |
BR5.12 |
Predicting Structural Type |
SP |
M |
|
BR5.13 |
Predicting Occupancy Type |
SP |
M |
Implemented |
BR5.14 |
Predicting Year Built |
SP |
M |
Implemented |
BR5.15 |
Predicting Attached Garage |
SP |
M |
|
BR5.16 |
Predicting Presence of Masonry Chimney |
UF |
D |
|
BR6 |
DesignSafe integration to provide access to GPU |
|||
BR6.1 |
Create JupyterHub notebook at DesignSafe for building asset inventory workflow usage |
SP |
M |
|
BR6.2 |
For classification done at DesignSafe, store images and meta data for BE Database |
SP |
M |
|
BR6.3 |
Create JupyterHub notebook at DesignSafe for individual modules to demonstrate immediate results |
SP |
M |
|
BR7 |
Work to improve existing performance models through Continous Learning |
|||
BR7.1 |
Continous Learning for Year Built |
SP |
M |
|
BR7.2 |
Continous Learning for Roof Material |
SP |
M |
|
BR8 |
Work to gather data for Module Validation/Verification/Training |
SP |
M |
InProgress |
PELICUN¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
P1 |
Existing Assessment Methods |
|||
P1.1 |
Implement the high-resolution loss assessment methodologies |
GC |
||
P1.1.1 |
Implement the scenario-based assessment from FEMA-P58 |
SP |
M |
Implemented |
P1.1.2 |
Implement the time-based assessment from FEMA-P58 |
SP |
D |
|
P1.1.3 |
Implement high-resolution assessment of buildings under wind hazards |
SP |
M |
|
P1.1.4 |
Implement high-resolution assessment of buildings under water hazards |
SP |
M |
|
P1.1.5 |
Implement high-resolution assessment of transportation networks |
SP |
M |
|
P1.1.6 |
Implement high-resolution assessment of buried pipelines |
SP |
M |
|
P1.2 |
Implement the efficient loss assessment methodologies from HAZUS |
GC |
||
P1.2.1 |
Implement the assessment of buildings under earthquake hazard from HAZUS |
SP |
M |
Implemented |
P1.2.2 |
Implement the assessment of buildings under hurricane wind hazard from HAZUS |
SP |
M |
Implemented |
P1.2.3 |
Implement the assessment of buildings under storm surge hazard from HAZUS |
SP |
M |
|
P1.2.4 |
Implement the assessment of buried pipelines under earthquake hazard from HAZUS |
SP |
M |
|
P1.2.5 |
Implement the assessment of transportation networks under earthquake hazard from HAZUS |
SP |
M |
|
P1.2.6 |
Implement the assessment of power networks under earthquake hazard from HAZUS |
SP |
M |
|
P2 |
Control |
|||
P2.1 |
Analysis & Data |
|||
P2.1.1 |
Allow users to set the number of realizations |
SP |
M |
Implemented |
P2.1.2 |
Allow users to customize fragility and consequence function parameters |
SP |
D |
Implemented |
P2.1.3 |
Allow users to specify dependencies between logically similar parts of the stochastic models |
SP |
D |
Implemented |
P2.2 |
Response Model |
|||
P2.2.1 |
Allow users to specify the added uncertainty to EDPs (increase in log-standard dev.) |
SP |
M |
Implemented |
P2.2.2 |
Allow users to specify the EDP ranges that correspond to reliable simulation results |
SP |
D |
Implemented |
P2.2.3 |
Allow users to specify the type of distribution they want to fit to the empirical EDP data |
UF |
D |
Implemented |
P2.2.4 |
Allow users to choose if they want to fit a distribution only to the non-collapsed EDPs |
UF |
M |
Implemented |
P2.3 |
Performance Model |
|||
P2.3.1 |
Allow users to prescribe a different number of inhabitants on each floor |
SP |
D |
Implemented |
P2.3.2 |
Allow users to customize the temporal distribution of inhabitants |
SP |
D |
Implemented |
P2.3.3 |
Allow users to prescribe different component quantities for each floor in each direction |
SP |
D |
Implemented |
P2.3.4 |
Allow users to specify the number of component groups and their quantities in each performance group |
UF |
D |
Implemented |
P2.4 |
Damage Model |
|||
P2.4.1 |
Allow users to specify the residual drift limits that determine irrepairability |
SP |
D |
Implemented |
P2.4.2 |
Allow users to specify the yield drift value that is used to estimate residual drifts from peak drifts |
SP |
D |
Implemented |
P2.4.3 |
Allow users to specify the EDP limits that are used to determine collapse probability |
SP |
D |
Implemented |
P2.4.4 |
Allow users to specify arbitrary collapse modes and their likelihood |
SP |
D |
Implemented |
P2.4.5 |
Allow users to prescribe the collapse probability of the structure |
UF |
M |
Implemented |
P2.5 |
Loss Model |
|||
P2.5.1 |
Allow users to decide which DVs to calculate |
SP |
D |
Implemented |
P2.5.2 |
Allow users to specify the likelihood of various injuries in each collapse mode |
SP |
D |
Implemented |
P3 |
Hazard Model |
|||
P3.1 |
Hazard Occurrence Rate |
|||
P3.1.1 |
Enable estimation of the likelihood of earthquake events |
SP |
M |
|
P3.1.2 |
Enable estimation of the likelihood of wind events |
SP |
M |
|
P3.1.3 |
Enable estimation of the likelihood of storm surge events |
SP |
M |
|
P3.1.4 |
Enable estimation of the likelihood of tsunami events |
SP |
M |
|
P4 |
Response Model |
|||
P4.1 |
EDP (re-)sampling |
|||
P4.1.1 |
Enable coupled assessment by using raw EDP values as-is |
UF |
M |
1.2 |
P4.1.2 |
Enable non-Gaussian EDP distributions |
UF |
D |
|
P4.2 |
EDP Identification |
|||
P4.2.1 |
Implement automatic identification of required EDP types based on the performance model |
SP |
M |
|
P5 |
Performance Model |
|||
P5.1 |
Auto-population of performance models |
|||
P5.1.1 |
Implement framework to enable user-defined auto-population scripts |
UF |
D |
Implemented |
P5.1.2 |
Prepare script to perform auto-population based on normative quantities in FEMA P58 |
UF |
D |
|
P6 |
Damage Model |
|||
P6.1 |
Collapse estimation |
|||
P6.1.1 |
Estimate collapse probability of the structure using EDP limits and the joint distribution of EDPs |
SP |
D |
Implemented |
P6.1.2 |
Estimate the collapse probability of the structure using empirical (raw) EDP data |
UF |
M |
1.2 |
P6.1.3 |
Enable user-defined collapse probability |
UF |
M |
Implemented |
P6.2 |
Building Damage |
|||
P6.2.1 |
Implement earthquake fragility functions for building components from FEMA P58 |
SP |
M |
Implemented |
P6.2.2 |
Implement earthquake fragility functions for buildings from HAZUS |
SP |
M |
1.2 |
P6.2.3 |
Implement wind fragility functions for buildings from HAZUS |
SP |
M |
Implemented |
P6.2.4 |
Implement inundation fragility functions for buildings from HAZUS |
SP |
M |
3.0 |
P6.2.5 |
Implement high-resolution wind fragility functions for building components |
SP |
M |
|
P6.2.6 |
Implement high-resolution inundation fragility functions for building components |
SP |
M |
|
P6.3 |
Lifeline Damage |
|||
P6.3.1 |
Implement earthquake fragility functions for buried pipelines from HAZUS |
SP |
M |
|
P6.3.2 |
Implement earthquake fragility functions for bridges from HAZUS |
SP |
M |
|
P6.3.3 |
Implement earthquake fragility functions for power networks from HAZUS |
SP |
M |
|
P6.3.4 |
Implement high-resolution fragility functions for buried pipelines |
SP |
M |
|
P6.3.5 |
Implement high-resolution fragility functions for transportation networks |
SP |
M |
|
P6.4 |
Cascading Damages |
|||
P6.4.1 |
Implement fault tree-based cascading damage model |
SP |
M |
|
P7 |
Loss Model |
|||
P7.1 |
Consequence functions for buildings |
|||
P7.1.1 |
Implement functions for repair cost and time as per FEMA P58 |
SP |
M |
Implemented |
P7.1.2 |
Implement functions for red tag triggering as per FEMA P58 |
SP |
M |
Implemented |
P7.1.3 |
Implement functions for injuries and fatalities as per FEMA P58 |
SP |
M |
Implemented |
P7.1.4 |
Implement functions for repair cost and time as per HAZUS earthquake |
SP |
M |
Implemented |
P7.1.5 |
Implement functions for debris as per HAZUS earthquake |
SP |
D |
|
P7.1.6 |
Implement functions for business interruption as per HAZUS earthquake |
SP |
D |
|
P7.1.7 |
Implement functions for repair cost and time as per HAZUS wind |
SP |
M |
Implemented |
P7.1.8 |
Implement functions for repair cost and time as per HAZUS inundation |
SP |
M |
|
P7.1.9 |
Implement functions for environmental impact estimation as per FEMA P58 2nd edition |
SP |
M |
|
P7.1.10 |
Implement functions for high-resolution repair cost and time assessment for wind hazards |
SP |
M |
|
P7.1.11 |
Implement functions for high-resolution repair cost and time assessment for water hazards |
SP |
M |
|
P7.2 |
Consequence functions for other assets |
|||
P7.2.1 |
Implement functions for repair cost and time for buried pipelines as per HAZUS earthquake |
SP |
M |
Implemented |
P7.2.2 |
Implement functions for repair cost and time for bridges as per HAZUS earthquake |
SP |
M |
|
P7.2.3 |
Implement functions for repair cost and time for power networks as per HAZUS earthquake |
SP |
M |
|
P7.2.4 |
Implement high-resolution functions for repair cost and time for transportation networks |
SP |
M |
|
P7.2.5 |
Implement high-resolution functions for repair cost and time for buried pipelines |
SP |
M |
BE Database¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
BE |
Establish a National Infrastructure Data Base for characterizing the physical and natural infrastructure |
GC |
M |
|
BE1 |
Ability to use cumulative knowledge bases rather than the piecemeal individual approaches |
GC |
M |
|
BE1.1 |
Utilize Federated Databases to maintain individual databases & data sources yet provide central database resource |
SP |
M |
|
BE2 |
Include National building model inventories |
GC |
M |
InProgress |
BE2.1 |
Incorporate Building data from existing datasets published by states, counties and cities |
SP |
M |
InProgress |
BE2.2 |
Ingest building data from web-scraping techniques, e.g. from zillow, county websites |
SP |
M |
InProgress |
BE2.3 |
Ingest building data using AI/ML techniques and satellite and streetview images |
SP |
M |
InProgress |
BE3 |
Incorporate transportation newtwork data from existing datasets made avail through www |
SP |
M |
|
BE3.1 |
Ingest additionally needed transportation newtwork data utilizing AI/ML and satellite and streetview images |
SP |
M |
|
BE4 |
Include National Models of Utility Networks |
GC |
M |
|
BE4.1 |
Incorporate utility network data from existing datasets made avail through www |
SP |
M |
|
DL Database¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
DLD |
Database for Damage and Loss Fragilities and Consequence Functions: Loss computations use fragility and consequence functions for modern and archaic structural and nonstructural components and assem- blies in structures. The database of such functions for components and assemblies is small and must be expanded through coordinated numerical and experimental simulations |
GC |
M |
|
DLD1 |
Data Sources |
|||
DLD1.1 |
Make the component fragility and consequence functions from FEMA P58 available |
SP |
M |
|
DLD1.1.1 |
FEMA P58 First Edition |
SP |
M |
Implemented |
DLD1.1.2 |
FEMA P58 Second Edition |
UF |
M |
Implemented |
DLD1.1.3 |
Extend FEMA P58 Second Edition consequence functions with environmental impact parameters |
SP |
M |
|
DLD1.2 |
Make the building fragility and consequence functions from HAZUS available |
SP |
M |
|
DLD1.2.1 |
HAZUS earthquake damage and reconstruction cost and time |
SP |
M |
Implemented |
DLD1.2.2 |
HAZUS hurricane wind damage and reconstruction cost and time |
SP |
M |
Implemented |
DLD1.2.3 |
HAZUS storm surge damage and reconstruction cost and time |
SP |
M |
|
DLD1.3 |
Make the lifeline fragility and consequence functions from HAZUS available |
SP |
M |
|
DLD1.3.1 |
HAZUS bridge damage and reconstruction cost and time |
SP |
M |
|
DLD1.3.2 |
HAZUS buried pipeline damage and reconstruction cost and time |
SP |
M |
|
DLD1.3.3 |
HAZUS power network damage and reconstruction cost and time |
SP |
M |
|
DLD1.4 |
Extend available high-resolution building damage and loss model parameters |
SP |
M |
|
DLD1.4.1 |
Building damage and loss model parameters under wind hazards |
SP |
M |
|
DLD1.4.2 |
Building damage and loss model parameters under water hazards |
SP |
M |
|
DLD1.5 |
Make high-resolution damage and loss model parameters available for lifelines |
SP |
M |
|
DLD1.5.1 |
Transportation network damage and loss model parameters |
SP |
M |
|
DLD1.5.2 |
Buried pipeline network damage and loss model parameters |
SP |
M |
|
DLD2 |
Data Storage |
|||
DLD2.1 |
Generic JSON format |
SP |
M |
|
DLD2.1.1 |
Develop a generic JSON data format for component fragility and consequence functions |
SP |
D |
Implemented |
DLD2.1.2 |
Store FEMA P58 and HAZUS component data in the new JSON format and make them available |
SP |
D |
Implemented |
DLD2.2 |
HDF5 Data Storage |
SP |
M |
|
DLD2.2.1 |
Store the JSON files in an HDF5 data structure for each data source |
SP |
M |
Implemented |
DLD2.3 |
Online Database |
SP |
M |
|
DLD2.3.1 |
Create an online database for storing parameters of damage and loss models for buildings |
SP |
M |
|
DLD2.3.2 |
Extend online database to store parameters of damage and loss models for transportation networks |
SP |
M |
|
DLD2.3.3 |
Extend online database to store parameters of damage and loss models for buried pipeline networks |
SP |
M |
|
DLD2.3.4 |
Populate building database with high-resolution model parameters from researchers |
SP |
M |
|
DLD2.3.5 |
Populate lifeline database with high-resolution model parameters from researchers |
SP |
M |
Regional Testbeds¶
# |
Description |
Source |
Priority |
Status |
|---|---|---|---|---|
TB |
Identify knowledge gaps and promote NSF generated knowledge through regional demonstration projects that help generate linkages to operational entities and decision makers |
GC |
M |
|
TB1 |
Provide Earthquake Testbeds |
SP |
M |
TB1.0 |
TB1.1 |
Provide Bay Area Earthquake Testbed |
SP |
M |
TB1.0 |
TB1.2 |
Provide Anchorage Earthquake Testbed |
SP |
M |
TB1.1 |
TB2 |
Provide Tsunami Testbed |
SP |
M |
|
TB3 |
Provide Hurricane Testbed |
SP |
M |
|
TB3.1 |
Provide Atlantic City Hurricane Wind Testbed |
SP |
M |
TB2.0 |
TB3.2 |
Provide Atlantic City Hurricane Wind and Storm Surge Testbed |
SP |
M |
|
TB3.3 |
Provide Hurricane Laura Testbed looking at Lake Charles |
SP |
M |
|
TB4 |
Provide Earthquake and Lifelines Testbed |
SP |
M |


Netherlands to reduce speed limit to combat pollution
by Rachael KennedyThe Netherlands has pledged to reduce its national speed limit in a bid to combat the country's ongoing nitrogen pollution crisis.
Prime Minister Mark Rutte confirmed the changes on Wednesday, saying the new maximum speed limit would be 100km/h (62mph) and would be enforced during daytime hours.
This is down from the current maximum speed limit of 130km/h (80mph).
It comes as part of the latest move to tackle the country's issue with nitrogen pollution, of which some gases have a more adverse effects on the environment than CO2.
In May, a top court in the Netherlands found the country was in breach of EU law in regards to its nitrogen emissions and it led to a halt on highly-emitting activities, including a number of construction projects.
Rutte said on Wednesday that it was important these construction projects were able to resume.
READ MORE: Dutch farmers demanding more respect cause 'busiest rush hour ever'
Farming is another industry that produces high levels of nitrogen emissions, but local farmers have complained that the crisis has been unfairly pinned on them.
Last month thousands of farmers caused the "busiest morning rush hour ever" by using their tractors to block roads around The Hague to oppose the focus on pollution in their sector.


#include <path_monitor_impl.hpp>
macOS implementation of the PathMonitor interface. Uses the Apple File System Events API to detect path changes. See:
https://developer.apple.com/documentation/coreservices/file_system_events?language=objc
- Author
- Timothy Flynn (trfly.nosp@m.nn89.nosp@m<EMAIL_ADDRESS>- Version
- August 15, 2020
Linux implementation of the PathMonitor interface. Uses the inotify API to detect path changes.
- Author
- Timothy Flynn (trfly.nosp@m.nn89.nosp@m<EMAIL_ADDRESS>- Version
- January 19, 2017
Windows implementation of the PathMonitor interface. Uses the ReadDirectoryChangesW API to detect path changes.
- Author
- Timothy Flynn (trfly.nosp@m.nn89.nosp@m<EMAIL_ADDRESS>- Version
- January 19, 2017
◆ PathMonitorImpl() [1/3]
Constructor. Creates the dispatch queue used to handle path events.
◆ ~PathMonitorImpl() [1/2]
| fly::path::PathMonitorImpl::~PathMonitorImpl |
( |
| ) |
|
|
override |
Destructor. Destroys the dispatch queue and any open FSEvents stream.
◆ PathMonitorImpl() [2/3]
Constructor. Create the path monitor's inotify handle.
◆ ~PathMonitorImpl() [2/2]
| fly::path::PathMonitorImpl::~PathMonitorImpl |
( |
| ) |
|
|
override |
Destructor. Close the path monitor's inotify handle.
◆ PathMonitorImpl() [3/3]
◆ create_path_info() [1/3]
| std::unique_ptr< PathMonitor::PathInfo > fly::path::PathMonitorImpl::create_path_info |
( |
const std::filesystem::path & |
path | ) |
const |
|
overrideprotectedvirtual |
Create an instance of the OS dependent PathInfo struct.
- Parameters
-
| path | The path to be monitored. |
- Returns
- Up-casted pointer to the PathInfo struct.
Implements fly::path::PathMonitor.
◆ create_path_info() [2/3]
| std::unique_ptr<PathMonitor::PathInfo> fly::path::PathMonitorImpl::create_path_info |
( |
const std::filesystem::path & |
path | ) |
const |
|
overrideprotectedvirtual |
Create an instance of the OS dependent PathInfo struct.
- Parameters
-
| path | The path to be monitored. |
- Returns
- Up-casted pointer to the PathInfo struct.
Implements fly::path::PathMonitor.
◆ create_path_info() [3/3]
| std::unique_ptr<PathMonitor::PathInfo> fly::path::PathMonitorImpl::create_path_info |
( |
const std::filesystem::path & |
path | ) |
const |
|
overrideprotectedvirtual |
Create an instance of the OS dependent PathInfo struct.
- Parameters
-
| path | The path to be monitored. |
- Returns
- Up-casted pointer to the PathInfo struct.
Implements fly::path::PathMonitor.
◆ is_valid() [1/3]
| bool fly::path::PathMonitorImpl::is_valid |
( |
| ) |
const |
|
overrideprotectedvirtual |
Check if the path monitor's dispatch queue was successfully created.
- Returns
- True if the dispatch queue is valid.
Implements fly::path::PathMonitor.
◆ is_valid() [2/3]
| bool fly::path::PathMonitorImpl::is_valid |
( |
| ) |
const |
|
overrideprotectedvirtual |
Check if the path monitor's inotify handle was successfully created.
- Returns
- True if the inotify handle is valid.
Implements fly::path::PathMonitor.
◆ is_valid() [3/3]
| bool fly::path::PathMonitorImpl::is_valid |
( |
| ) |
const |
|
overrideprotectedvirtual |
◆ poll() [1/3]
| void fly::path::PathMonitorImpl::poll |
( |
std::chrono::milliseconds |
timeout | ) |
|
|
overrideprotectedvirtual |
Check if the FSEvents API has detected any path events, and handle any that occurred.
- Parameters
-
| timeout | Max time allowed to wait for an event to occur. |
Implements fly::path::PathMonitor.
◆ poll() [2/3]
| void fly::path::PathMonitorImpl::poll |
( |
std::chrono::milliseconds |
timeout | ) |
|
|
overrideprotectedvirtual |
Check if the path monitor's inotify handle has any events to be read, and handle any that are readable.
- Parameters
-
| timeout | Max time allowed to wait for an event to be readable. |
Implements fly::path::PathMonitor.
◆ poll() [3/3]
| void fly::path::PathMonitorImpl::poll |
( |
std::chrono::milliseconds |
timeout | ) |
|
|
overrideprotectedvirtual |
Check the overlapped result of all monitored paths and handle any that have been posted.
- Parameters
-
| timeout | Max time allowed to wait for a completion to be posted. |
Implements fly::path::PathMonitor.
The documentation for this class was generated from the following files:


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The increased availability of data has forever changed trading patterns in the sports betting industry. More than ever before sports bettors have access to the same information as traders do, with plenty of “tipster” and data crunching websites dedicated to making the sports better smarter. What this means for the bookies we have yet to determine, but its certainly appears to be good news for sports bettors and enthusiasts.
FansUnite is a site that has been created to take the financial risk out of betting and to make sports bettors sharper. FansUnite CEO and Co-Founder Darius Eghdami has provided a platform where users can place wagers with a free virtual currency, track and compare their results to thousands of other sports bettors who they can also collaborate with. It’s a “crowd sourcing of handicappers’ picks” with a social, community-type feel.
“When we initially created FansUnite, we specifically set out to not create another tipster website. We wanted to build a platform that gave sports bettors the tools and data to become more profitable. We believe that players have a higher chance of profitably when they are given the information they need to make the right decision”, Eghdami shared.
He continued, “Tipster sites are traditionally one way and one dimensional. Expert tipsters make their picks and explain it to the audience, while clients can decide whether they want to trust the tip or not. Tipster sites are service driven as you pay to access a piece of information”.
“FansUnite is a platform which allows members to not only make picks, but share their picks and collaborate with the community; we aren’t trying to push our picks on users. We are not creating a single source of picks, but rather, creating multiple sources of picks with a fully verified and transparent history”, he said.
What’s truly unique about FanUnite as a site to educate bettors is the nature of the data it provides to its users.
“We’ve also given our community tools to increase their own profitability. Our PickTracker is a powerful resource which will quickly identify areas that a given member is doing well and areas in which the same member is leaking profit. We have many members that specialize in a given league, and together, we can increase all of our profits”.
Such increased availability of data for sports bettors is bound to impact trading behaviors and Eghdami believes the ripple effect is in its infancy stage.
“I think traders are only beginning to become aware of the utility of the data that FansUnite offers. The importance of specialization is starting to be realized by the public betting conscience. The days of traders thinking they can be masters in every league they touch are fading and a collaborative approach is taking over. By providing our members with an increased availability of data we’re seeing them bet more often and be more profitable”, he said.
FansUnite is an American company based in Boulder, Colorado, an interesting location considering the legality (or lack-thereof) of sports betting in the country. When asked what he thinks the future will bring for the legality of sports betting in America, Eghdami remains optimistic.
“While sports betting has always been a legal and legitimate form of entertainment in much of the world, it is only recently that North America’s legal betting market has gained traction. I’d like to echo Adam Silver’s comments that whether you have a gentlemen’s bet, or a small $5 wager on a game, you are suddenly a lot more engaged, and isn’t that what all major sports are striving for?”
He continued, “Sports betting and daily fantasy drive interest and viewership, which is why we are getting a push from some of the major leagues to legalize and regulate sports betting. FanDuel and Draftkings have grown so popular in such a short time, through a loophole in gaming laws. With the momentum they’ve created, it doesn’t make sense not to legalize sports betting. The population has shown they want it, and North Americans are finding a way to bet whether it’s legal or not. So I believe it’s just a matter of time, and we are perfectly situated to take advantage of it when it does”.
Another area of enormous growth in the sports betting industry is eSports and FansUnite has already taken advantage of this blossoming sector.
“The rise of eSports has been pretty spectacular to watch over the last few years and will continue to exponentially grow. Talking to many of the sportsbooks, they re-iterate to us that it is their fastest growing sport. We continue to try and increase our presence in eSports due to the recent growth in not just bettors, but viewers and players as well. It’s a fun industry to be involved in, and we have some pretty amazing eSports bettors on FansUnite with esportsbettor and J_Yanez who at times I think know more than the oddsmakers!”
As a start-up company in the sports betting arena, FansUnite has done an outstanding job differentiating themselves from other companies competing in this space. They’ve been accepted into the Boulder-based BoomTown Accelerator Program and according to Eghdami, the team at Boomtown has helped FansUnite refine their vision and reimagine what the company can be.
His best piece of advice to other start-ups providing data in the sports betting space?
“Talk to as many of your potential customers as you can and take guessing out of the equation so you can make sure you are solving their pain points. For the majority of people in our space, sports betting is not only about profitability so make sure you find what keeps them engaged and excited. Whether it’s allowing them to broadcast their wins, positive feedback or some sense of accomplishment when they are profitable”.
Based in Boulder, Colorado sports prediction platform FansUnite.com has announced the global release of its new user interface.
The new platform was created based on feedback received from FansUnite beta users. The latest platform has added some progressive features and bold ideas with a view to giving sports bettors the tools and data needed to become more profitable.
Darius Eghdami, FansUnite CEO, said this of the announcement: “We are thrilled about the new platform launch. Our team did a tremendous job listening to our community and building a product that they wanted.”
The new UI is focused on four key factors. There are now channels which can be subscribed to which will ensure only the information users want to see is displayed. Usability was another important factor in the development; the FansUnite team redesigned the platform’s core pages to enhance the experience and make it easier to navigate. It ensures that all the information a user may need is on hand. The site also offers an increase in betting options, and is working on introducing live betting too. Speed completes the four areas of focus, and a statement from the company says that speed testing has highlighted a more responsive platform.
Eghdami added: “The new FansUnite is a great leap towards redefining the individual betting experience. The promise of collaboration and transparency are much needed in the gaming industry.”
FansUnite is a social sports betting community started in 2014 by three sports fans. Users are able to take part without any monetary risk should they so choose, by utilising the site’s free virtual currency.
FansUnite was started by three sports fans in Canada, and intends to be a site for fellow fans offering transparency, fun, and top wagering advice via its social betting platform and community.
FansUnite utilises virtual currency meaning, should they opt to, users are able to bet on sports without the risk of losing any actual money. A place for gamblers of all types to hone their skills, and interact.Team SBC caught up with CEO, Darius Eghdami to talk about all things FansUnite, as well as social betting and Bitcoin.
SBC: Darius, could you please describe to our readers what it is FansUnite offers, and how it differs to other betting operators?
Darius: FansUnite gives users the opportunity to practice and bet on sports without the monetary risk. Our community is comprised of users that exchange ideas, discuss upcoming games and share betting strategies so that they can be confident with their picks.
We differentiate ourselves by giving users access to thousands of handicappers, with full transparency behind their betting history, for a fraction of the cost of what most individual handicappers charge. We also separate ourselves by providing our community with PickTracker, an analytics engine we developed that identifies strengths and weaknesses that help users critically examine their betting patterns, as well as their peers’.
SBC: The monthly Revenue Share scheme seems an attractive and innovative inclusion. Could you tell us a little more about this, and how it’s been going since its launch?
Darius: The entire premise of our site is to beat the books together. The idea behind the Revenue Share is to give back to our community. Since launch, the top users each month have been unbelievable in sharing and contributing their thoughts and bets, so giving them a percentage of our subscription revenue is a great way to thank them.
Although the revenue share scheme is a new initiative, we’ve set up the structure to give each of our top 5 handicappers, 5% of our monthly subscription revenue. As our community continues to grow, we hope this will be a sizeable pot for each handicapper where they could potentially make a living off FansUnite.
SBC: Which sports are proving the most popular to date, and are there any plans to establish a presence in any new markets?
Darius: FansUnite is a Canadian based company, so a lot of our users place bets on the major North American sports. NFL betting is massive as well as college basketball, come March. While football (soccer) is the most widely bet sport on our platform, we have a great community focused on Tennis, Rugby, Aussie Rules and more.
The fastest growing sport is eSports, as we’ve seen a massive emergence in this market over the last 12 months. This is a space we definitely want to increase our presence in, and it’s been remarkable to see the recent growth in not just bettors, but viewers and players as well.
SBC: You previously founded CappersDelight, and worked at KPMG. How did the idea for Fansunite come about, and what convinced you to run with it?
Darius: CappersDelight was an amazing experience as it really highlighted the areas of the betting industry that we wanted to change. We ran CappersDelight for a couple years where we were just another handicapping service, hiding our full record and charging high subscription fees while our members had to ride the ups and downs of an individual handicapper. Coupled with the ongoing issues of transparency and verifiability, it confirmed to us that the traditional handicapping market was in need of massive changes.
We ran with the idea of FansUnite as we wanted to try and bring the suspect betting services out of the shadows by ensuring that the records of all members on our platform are transparent, available, and verifiable. As avid sports fans and bettors, we couldn’t think of anything cooler than building a platform that increased the profitability for sports bettors.
SBC: Are you anticipating an increase in the number of betting operators that accept Bitcoin? What are the advantages, and difficulties, of this form of currency?
Darius: The Bitcoin industry is a very interesting one, as the older community doesn’t really understand what Bitcoin is, and the younger guys are starting to become wary of it. That being said, the thought of a reliable crypto-currency that allows an individual to remain anonymous can be very advantageous.
The team are big supporters of Bitcoin and would love to see it become a mainstay in the betting industry. We’ve been fortunate enough to team up with Nitrogen Sports in the running of our streak contests, which has been great. Unfortunately, I do not see Bitcoin becoming an accepted currency for the major sportsbooks. I think the smaller players will pick up on Bitcoin as a way to access a new user base, but I think the major ‘books will continue to avoid it as there are just too many uncertainties that accompany it. Although there are always fluctuations in currency value, Bitcoin is subject to extreme variances, which I think will continue to scare many sportsbooks.
SBC: Any final thoughts to share?
Darius: FansUnite is building a community of profitable sports bettors by accessing the large betting community with access to modern technology and analytics. It makes sense to provide transparent and verified data. We want to make sure our users feel comfortable with our offering and give them the best chance to succeed in sports betting.
Vancouver, February 5, 2015: Darius Eghdami, CEO of FansUnite, has been bringing more transparency to the field of sports handicapping for the past 4 years. FansUnite continues to push out new features that enable users to improve their returns by following top betting pro’s and analyzing their own betting patterns and results. FansUnite rolls out their much-anticipated PickTracker software today. This feature will enable users to analyze their betting history by allowing the user to view historical data and break down their past picks by sport, league, or bet-type and helping the user to look critically at their own betting patterns.
The idea behind FansUnite was originally conceptualized when Darius was a casual sports handicapper. He realized that in an industry where sports betting enthusiasts were often willing to invest in costly monthly subscriptions to follow a handicapper’s picks, it was relatively easy for these “professionals” to hide their comprehensive records because they would only selectively show their winning results to reflect their success. This slanted portrayal of “success” obviously put potential subscribers at a huge disadvantage when choosing whom to follow, as there is little transparency and resulted as the impetus behind FansUnite.
Unlike traditional handicapping sites that offer insight into the general consensus of picks, the FansUnite platform is based on the notion that bettors no longer need to blindly follow “pros” because they can further research the historical data of the handicappers they follow in order to gain greater access and insight based on real analytics.
FansUnite continues to evolve and produce more tools to improve the handicapping model to provide their user base more and more information. Every pick on the site goes through their proprietary algorithm to determine whether the pick is a “Pro Pick” based on factors such as the success and history on that sport and the teams involved. The FansUnite platform is attracting “pros” and their top handicappers are proving to be 10% more successful than the industry average. As an example, if you had bet $100 on every FansUnite NBA Pro Pick in January 2015, the profit would have been over $19,000.
Headquartered in Vancouver, Canada, FansUnite specializes in crowd sourcing of handicappers’ picks to earn you more money. With a social aspect behind making your picks, the tight-knit FansUnite community is in it to “beat the books” together.
About Darius Eghdami
Darius is a Chartered Accountant and paused his career to pursue a passion in the sports handicapping world. As an avid sports bettor, he wanted to build a platform to level the playing field and bring some transparency to the industry.
The Industry
Is everyone getting involved in betting and fantasy? It sure seems like it. Just this past weekend, according to an estimate done by the American Gaming Association, Americans were expected to wager $3.8 billion worth of illegal bets on the 2015 Super Bowl alone.
Comparing these figures to an estimated $1 billion being wagered on the English Premier League each week, and Interpol estimating that the unregulated Asian betting market is worth over $500 billion alone per year, the betting industry is continuing to sky rocket.
In the Premier League, four of the 20 teams are sponsored directly by sports books:
With the recent noise coming from NBA Commissioner Adam Silver, and Dallas Mavericks Owner Mark Cuban, along with our own research, we are confident that there is going to be a massive shift in the American sports betting market. Adam Silver stated that Congress should legalize sports betting and instituted a regulation scheme. Silver cited estimates that nearly $400 billion is illegally wagered on sports each year. We strongly believe FansUnite is perfectly situated to take advantage of this shift.
FansUnite, a social betting site that uses virtual currency, has rolled out its much-anticipated PickTrackersoftware. This feature will enable users to analyse their betting history by allowing the user to view historical data and break down their past picks by sport, league, or bet-type and helping the user to look critically at their own betting patterns.
The idea behind FansUnite was originally developed when CEO Darius Eghdami, a casual sports handicapper, realised that there was very little transparency in the performance of professional handicappers, despite the fact that sports betting enthusiasts were often willing to invest in costly monthly subscriptions.
Unlike traditional handicapping sites that offer insight into the general consensus of picks, the FansUnite platform is based on the notion that bettors no longer need to blindly follow “pros” because they can further research the historical data of the handicappers they follow in order to gain greater access and insight based on real analytics.
FansUnite continues to evolve and produce more tools to improve the handicapping model to provide their user base more and more information. Every pick on the site goes through their proprietary algorithm to determine whether the pick is a “Pro Pick” based on factors such as the success and history on that sport and the teams involved. The FansUnite platform is attracting “pros” and their top handicappers are proving to be 10% more successful than the industry average. As an example, if you had bet $100 on every FansUnite NBA Pro Pick in January 2015, the profit would have been over $19,000.
FansUnite, a social betting site has launched a PickTracker software that allows players to analyze their betting history and make a critical review of the used betting patterns.
The original idea of creating FansUnite first came to Darius Eghdami, a casual sports handicapper, upset with not enough transparency in the professional handicappers’ performance.
The key difference of the site from its traditional handicapping analogues is that it offers insight into the general consensus of choices made. The used platform lets players to no longer blindly follow “pros”. Instead it provides an opportunity to research the historical data of the handicappers they follow in order to gain advantage based on real analytics.
FansUnite keeps on evolving and producing extra tools to deliver more information to their users. Each pick done on a website is checked if it’s a “Pro Pick” based on real factors such as the success and history of that sport and teams involved.
The statistics proves that the the FansUnite handicappers are to be 10% more successful than the industry average.
BUSINESS LUNCH: At Homerand-Smithe's Homer Street Cafe Tuesday, Darius Eghdami relished the $20 daily roast and an $11 Zuccardi 'Q' Tempranillo 09 Mendoza. It's a safe bet he'll return to the Paris-like eatery. In fact, safe bets are his game, such as graduating as a chartered accountant to article at KPMG. Beyond that, he and like-aged Duncan McIntyre and Nicolas Shuster recently raised $250,000 to launch and develop a beta version of FansUnite.
"It's a virtual playground for sports bettors," said lifetime fan Eghdami, who, with the others, ran a handicapping system until 2011: "I charged $70 a week and sat at the back in class at UBC, making picks."
Today, he says paying one handicapper is a mug's game. That's because FansUnite's platform (fansunite.com) aims to include "10,000 handicappers who are making picks while we are tracking their records and verifying them ... so you can filter those who are best by week, month and all-time, by sports, by team and by betting type - outright win, spread, over-under." All for $15 a month, which FansUnite will charge after releasing fully this summer.
"Our motto is making betting social, Our goal is to make everybody win more money" said Eghdami, whose developer-father Kombiz's VLINX ecommerce trading platform "was a big help in setting up FansUnite."
Only those who view FansUnite's handicapper picks will pay. Others ers may exercise betting skills riskfree with free virtual currency that is renewed monthly. Meanwhile, "The sports books are all looking to link up with a social platform," Eghdami said, "and we are providing them with a more-educated bettor."


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Overview
Include files for Intel® Threading Building Blocks (Intel® TBB).
Directories
- tbb
- Include files for Intel TBB classes and functions.
- serial/tbb
- Include files for a sequential implementation of the parallel_for algorithm.
Up to parent directory
Copyright © 2005-2020 Intel Corporation. All Rights Reserved.
Intel is a registered trademark or trademark of Intel Corporation
or its subsidiaries in the United States and other countries.
* Other names and brands may be claimed as the property of others.


This tutorial shows you how to write an object detection example with OpenCV.js.
To try the example you should click the modelFile button(and configFile button if needed) to upload inference model.
You can find the model URLs and parameters in the model info section.
Then You should change the parameters in the first code snippet according to the uploaded model.
Finally click Try it button to see the result. You can choose any other images.
1.The parameters for model inference which you can modify to investigate more models.
2.Main loop in which will read the image from canvas and do inference once.
3.Load labels from txt file and process it into an array.
4.Get blob from image as input for net, and standardize it with mean and std.
5.Fetch model file and save to emscripten file system once click the input button.
6.The post-processing, including get boxes from output and draw boxes into the image.


 The order of your menus has been changed to reflect that.
Click "Rearrange" to make this change permanent. You won't be able to modify the order of your menus until you do so.
Please contact support or report an issue if you have any concerns.


+++ draft = false date = "2006-08-23T13:57:21-03:00" title = "Capturing print statements output" tags = ["Python", "cookbook"] created = "2006-08-23T13:52:04-03:00" modified = "2006-08-23T14:45:12-03:00" permaLink = "http://play.pixelblaster.ro/blog/archive/2006/08/23/capturing-print-statements-output" author = "Tiberiu Ichim" aliases = [ "/blog/archive/2006/08/23/capturing-print-statements-output" ] +++
Sometimes you have code that has a lot of print statements, which are helping you with debugging. But what happens if you try to port that code to an environment without a console? In the best case, you won't see the prints anymore, in the worse case, the code will fail because there's no "valid file descriptor" to which to write.
The solution to this problem is to replace python's stdout file, which will enable the code output to be recorded and maybe logged for debugging purposes.
The following snippet shows how to achieve this:
>>> import StringIO, sys
>>> out = StringIO.StringIO()
>>> sys.stdout = out
>>> print "Test"
>>> log = out.getvalue()
>>> assert (log == "Test\n")
>>>


This online convertor will convert your money in a blick of an eye!
Builded with love and passion!
Pick an amount and get to go!
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skm-consent is a web-component for creating cookie-consents. It's done with Svelte.
Please view-source to see how the story goes.


“Nadanti morā susikhā supekhuṇā,
Sunīlagīvā sumukhā sugajjino;
Susaddalā cāpi mahāmahī ayaṁ,
Subyāpitambu suvalāhakaṁ nabhaṁ.
The peacocks cry out with their fair crests and tails,
their lovely blue necks and fair faces, their beautiful song and their call.
This broad earth is lush with grass and dew,
and the sky is full of beautiful clouds.
Sukallarūpo sumanassa jhāyataṁ,
Sunikkamo sādhu subuddhasāsane;
Susukkasukkaṁ nipuṇaṁ sududdasaṁ,
Phusāhi taṁ uttamamaccutaṁ padan”ti.
… Cūḷako thero ….
One practicing absorption is happy in mind, and their appearance is uplifting;
going forth in the teaching of the Buddha is easy for a good person.
You should realize that supreme, unchanging state,
so very pure, subtle, and hard to see.


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| likert-package | Likert Analysis and Visualization |
| abs_formatter | Absolute value formatter for continuous_scale |
| align.plots | Adapted from ggExtra package which is no longer available. This is related to an experimental mlpsa plot that will combine the circular plot along with the two individual distributions. |
| gap | Fictitious dataset with importance and satisfaction results across five different offices. |
| label_wrap_mod | Wrap label text. |
| likert | Analyze Likert type items. |
| likert.bar.plot | Bar Plot for Likert Items. |
| likert.density.plot | Creates a density plot for likert items. |
| likert.heat.plot | Internal method. |
| likert.histogram.plot | Histogram of number of responses. |
| likert.matrix.plot | Matrix plot (experimental) |
| likert.options | Builds an object with options for plotting 'likert' results. |
| mass | Results from an administration of the Math Anxiety Scale Survey. |
| MathAnxiety | Pre-summarized results from an administration of the Math Anxiety Scale Survey. |
| MathAnxietyGender | Pre-summarized results from an administration of the Math Anxiety Scale Survey grouped by gender. |
| pisaitems | Programme of International Student Assessment |
| plot.likert | Plots a set of likert items. |
| plot.likert.gap | Plots a set of likert items. |
| print.likert | Prints results table. |
| print.likert.bar.plot | Print method for 'likert.bar.plot'. The primary purpose is to suppress the "Stacking not well defined when ymin != 0" warning printed by 'ggplot2' for bar plots that have negative bars (i.e. the centered plots). |
| print.likert.gap | Prints results table. |
| print.likert.heat.plot | Print method for 'likert.heat.plot'. |
| print.xlikert | Prints the results of 'xtable.likert'. |
| recode | Recode a vector. |
| reverse.levels | Reverse the levels of a factor. |
| sasr | Results from the Survey of Academic Self-Regulation (SASR). |
| shinyLikert | Shiny App for the likert package. |
| summary.likert | Prints summary table of a Likert analysis. |
| summary.likert.gap | Prints summary table of a Likert analysis. |
| xtable.likert | Prints a LaTeX table of the likert items. |


Embed TeX in your documents easily.
<img src="http://texembed.ian.pw/e^{i %5Cpi} + 1 = 0" />
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This approach attempts to provide technical enforcements of Identity Provider
blindness by having the IDP delegate
the generation of identity assertion to the browser.
Below is one variation that combines the IDP blindness mechanisms designed in
Mozilla’s Personas proposal
with the RP blindness, recovery processes
and the user-behavior backwards compatibility. This approach maximizes the
technical enforcement of both RP and IDP tracking, at the cost of IDP and RP
server-side backwards compatibility.
Notice that in this approach we are assuming that an email proxy and email
provider exist independent from the IDP. This is necessary to maintain IDP
blindness even after RP uses the directed email address to email the user.
Below is the sign up flow in this proposal where a user signs up to an RP and
share their directed basic profile. The
importance of recovery code and salt are discussed in the next section.
Description of the steps involved in this flow:
.well-known
SHA512(alice, salt, RP)
id_token
and signsid_token
to RP.id_token
and signs up the user.
Maximum IDP and RP tracking preventions using technical enforcement.
From a UX perspective, we expect this to have the lowest privacy-introduced
friction/consent, since both RP and IDP tracking problems are addressed
mechanically.
This approach requires changes in the RP backends:
With this proposal the sign-in user experience on RP would remain the same in
particular a) users can sign in to RP by selecting an IDP and b) users can sign
in to their existing account with RP even on platforms that do not support WebID
by falling back to OpenID and removing IDP blindness restriction.
However the user experience on IDP side changes. In particular users are no
longer able to see and manage all the RPs they have signed up with through their
IDP control panel. Browsers may be able to provide a similar UX. Most likely
scenario is that both browser and IDP would end up managing some account which
can be a source of confusion.
A directed email address requires an
email proxy service in addition to the email provider.
One option is for the email provider to be the email proxy as well. This seems
like a reasonable option.
Another seemingly natural option would be to have the IDP provide the email
proxy service. However this can weaken the IDP blindness if IDP is also either
the email proxy or email provider. This is beucase most RPs send a welcome email
to their users on account creation.
So to mitigate this risk, we envision an entity (or entities) different from the
IDP is responsible for proxying email and keeping the user inbox. At this stage
and for the purpose of the flows discussed here, we simply assume the key
property that separate IDP and Email Proxy/Provider exists and paper over the
details of how a directed email address is created and verified across email
proxy, email provider and IDP.
Directed emails notably increase account management complexity for RPs since
many users are not be able to readily recall their directed email address. This
proposal adds even more complexity to this by introducing email proxy to the
mix.
Here is the database for each of the participating entities.
Normally the RP verifies the JWT (JSON Web Token) on its backend by checking
its signature against the IDP public key. However the new proposed Certified JWT
contains, in its header, a browser generated public key which is certified by
the IDP. So the verification process first verifies the included public key with
the IDP public key and then verifies the token using this public key.
Here is a strawman proposal for a Certified JWT:
{
header: {
"alg": "ES512",
"typ": "JWT",
/* new entry in header */
"certified-public-key": {
"value": "MIIBIjANBgkqhkiG",
"signature": ECDSASHA512(
"<value>",
"<idp-private-key>"
),
}
},
payload: {
"sub": "1234567890",
"name": "John Doe",
"admin": true,
"iat":<PHONE_NUMBER>,
/* new entry in payload */
"recovery": ECDSASHA512("<user_id>", RP, "<salt>")
},
signature: ECDSASHA512(
base64(header) + "." + base64(payload),
"<browser-private-key>")
}
}
Recovery code is an opaque value that can be computed given user id, RP site,
and a salt value e.g., recovery code = SHA512(user_id, RP, salt)
.
The recovery code is kept at the RP. But it may be passed to the IDP to uniquely
identify the user. This means that an RP and IDP may be able to identify the
user if they directly or indirectly (e.g., browser mediation) collaborate. This
enables the recovery mechanism for a user to sign in to their existing account
on RP in a few important situations:
A random salt ensures that neither RP nor IDP is able to unblind themselves
simply by enumerating all possible values.


As expression profiles from different species cannot be directly compared the co-expression neighborhoods are compared instead. Consider two homologous genes A and B from different species. To calculate the ECC score first all genes co-expressed with A (in the first species) are extracted and the same is done for gene B. The ECC score is the number of gene families these two neighborhoods have in common divided by the total number of gene families in the union (i.e. the jaccard index).
Our implementation of ECC is similar to Movahedi et al. pubmed.


VFK - Czech Cadastral Exchange Data Format
Driver short name
VFK
Build dependencies
libsqlite3
This driver reads VFK files, i.e. data in the Czech cadastral exchange data format. The VFK file is recognized as an datasource with zero or more layers.
The driver is compiled only if GDAL is built with SQLite support.
Points are represented as wkbPoints, lines and boundaries as wkbLineStrings and areas as wkbPolygons. wkbMulti* features are not used. Feature types cannot be mixed in one layer.
Driver capabilities
Supports Georeferencing
This driver supports georeferencing
Supports VirtualIO
This driver supports virtual I/O operations (/vsimem/, etc.)
Open options
Starting with GDAL 2.3, the following open options can be specified
(typically with the -oo name=value
parameters of ogrinfo or ogr2ogr):
SUPPRESS_GEOMETRY=[YES/NO]: Defaults to
NO
. Setting it to YES will skip resolving geometry. All layers will be recognized with no geometry type. Mostly useful when user is interested at attributes only. Note that suppressing geometry can cause significant performance gain when reading input VFK data by the driver.FILE_FIELD=[YES/NO]: Defaults to
NO
. Setting it to YES will append new field VFK_FILENAME containing name of source VFK file to all layers.
Configuration options
Several configuration options are available:
OGR_VFK_DB_NAME=value: Determine the name of the SQLite backend database used when reading VFK data. By default, SQLite database is created in a directory of input VFK file (with file extension '.db').
OGR_VFK_DB_OVERWRITE=[YES/NO]: Determines whether the driver should overwrite an existing SQLite database and stores data read from input VFK file into newly created DB.
OGR_VFK_DB_DELETE=[YES/NO]: Determines whether the driver should delete the backend SQLite database when closing the datasource.
OGR_VFK_DB_SPATIAL=[YES/NO]: Determines whether the driver should store resolved geometries in the backend SQLite database. If
YES
, geometries are resolved only once when building SQLite database from VFK data. Geometries are stored in WKB format. Note that GDAL doesn't need to be built with SpatiaLite support. IfNO
, geometries are not stored in the DB and are resolved when reading data from DB on the fly.OGR_VFK_DB_READ_ALL_BLOCKS=[YES/NO]: Determines whether all data blocks should be read, or only the data blocks requested by the used.
OGR_VFK_DB_READ=[YES/NO]: If
YES
, opening a VFK backend SQLite database will cause the VFK driver to be used instead of the SQLite driver.
Internal working and performance tweaking
If backend SQLite database already exists then the driver reads features directly from the database and not from input VFK file given as an input datasource. This causes significant performance gain when reading features by the driver.
The driver reads by default all data blocks from VFK
file when building backend SQLite database. When configuration option
OGR_VFK_DB_READ_ALL_BLOCKS
=NO is given, the driver
reads only data blocks which are requested by the user. This can be
useful when the user want to process only part of VFK data.
Datasource name
Datasource name is a full path to the VFK file.
The driver supports reading files managed by VSI Virtual File System API, which include "regular" files, as well as files in the /vsizip/, /vsigzip/, and /vsicurl/ read-only domains.
Since GDAL 2.2 also a full path to the backend SQLite database can be
used as an datasource. By default, such datasource is read by SQLite
driver. If configuration option OGR_VFK_DB_READ
=YES
is given, such datasource is opened by VFK driver instead.
Layer names
VFK data blocks are used as layer names.
Filters
Attribute filter
An internal SQL engine is used to evaluate the expression. Evaluation is done once when the attribute filter is set.
Spatial filter
Bounding boxes of features stored in topology structure are used to evaluate if a features matches current spatial filter. Evaluation is done once when the spatial filter is set.


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"CAPHTOR "a chaplet, the original seat of the Philistines (Deuteronomy 2:23; Jeremiah 47:4; Amos 9:7). The name is found written in hieroglyphics in the temple of Kom Ombos in Upper Egypt. But the exact situation of Caphtor is unknown, though it is supposed to be Crete, since the Philistines seem to be meant by the “Cherethites” in 1 Samuel 30:14(see also 2 Samuel 8:18). It may, however, have been a part of Egypt, the Caphtur in the north Delta, since the Caphtorim were of the same race as the Mizraite people (Genesis 10:14; 1 Chronicles 1:12).
"CAPHTOR "a chaplet, the original seat of the Philistines (Deuteronomy 2:23; Jeremiah 47:4; Amos 9:7). The name is found written in hieroglyphics in the temple of Kom Ombos in Upper Egypt. But the exact situation of Caphtor is unknown, though it is supposed to be Crete, since the Philistines seem to be meant by the “Cherethites” in 1 Samuel 30:14(see also 2 Samuel 8:18). It may, however, have been a part of Egypt, the Caphtur in the north Delta, since the Caphtorim were of the same race as the Mizraite people (Genesis 10:14; 1 Chronicles 1:12).


{# What the dashboard of Ministry of Education will contain; template will be inherited for other role-based views #} {% extends "layout.html" %} {% block stylesheets %} {% endblock %} {% block javascripts %} {{ block.super }} {% include "education/partials/chart/javascripts.html" %} {% endblock javascripts %} {% block content %}
Welcome to the School Monitoring System, UNICEF's SMS-based Education monitoring and tracking system. Click a tab to explore.
{% if user.is_authenticated %} {% if user.is_superuser or user.get_profile.role == "Admins" %}
| Administrative Tasks |
|---|
| Manage Users |


JSON and AJAX
Fetch Info for 3 New Animals
dfsdfsdfdfdfdsdf
What greeting do you want to say?
Who do you want to say it to?
Send my greetings