4.2. Hurricane Assessment

This example is a small-scale regional hurricane risk assessment which performs damage/loss estimation for a group of 20 wood buildings in Atlantic City under the Sandy Storm. The buildings are subject to two types of hazards: wind, measured by peak wind speed (PWS), and flooding, measured by flood water depth (FWD). This example does not use response simulation; rather, an IMasEDP application translates the IMs directly to the DL application and uses IM-based component fragility functions to estimate damage. The distribution of the buildings’ structural types and stories are illustrated below.

../../../../_images/SandyWindFlood.png

Fig. 4.2.1 Peak wind speed and floor water depth maps of the Sandy Storm

4.2.1. Inputs

The example input files can be downloaded here: example_hu.zip. For more information about required input files, refer to Inputs.

  1. Configuration file: The configuration file specifies all simulation settings, including the application types, input file names, units, and type of outputs.

 1{
 2  "Name": "rWHALE_",
 3  "Author": "Kuanshi Zhong",
 4  "WorkflowType": "Regional Simulation",
 5  "buildingFile":"buildings.json",
 6  "runDir": "...",
 7  "localAppDir": "C:/rWHALE/",
 8  "units": {
 9      "force": "kips",
10      "length": "ft",
11      "speed": "mph"
12   },
13  "outputs": {
14    "EDP": true,
15    "DM": true,
16    "DV": true,
17    "every_realization": false
18  },
19  "Applications": {
20    "Building": {
21      "Application": "CSV_to_BIM",
22      "ApplicationData": {
23        "Min": "1",
24        "Max": "20",
25        "buildingSourceFile":"input_params_hu.csv"
26      }
27    },
28    "RegionalMapping": {
29      "Application": "NearestNeighborEvents",
30      "ApplicationData": {
31        "filenameEVENTgrid": "IMs/EventGrid.csv",
32        "samples": 10,
33        "neighbors": 4
34      }
35    },
36    "Events": [{
37      "EventClassification": "Hurricane",
38      "Application": "SimCenterEvent",
39      "ApplicationData": {
40        "pathEventData": "IMs/"
41      }
42    }],
43    "Simulation": {
44       "Application": "IMasEDP",
45       "ApplicationData": {}
46    },
47    "DL": {
48       "Application": "pelicun",
49       "ApplicationData": {
50          "DL_Method": "HAZUS MH HU",
51          "Realizations": 5000,
52          "coupled_EDP": true,
53          "detailed_results": false,
54          "log_file": true,
55          "auto_script": "auto_HU_NJ.py"
56       }
57    }
58  }
59}
  1. Building Application: This example uses the CSV_to_BIM building application. In the configuration file, the Max and Min parameters are set to run the full set of 20 buildings, and the name of the building source file is provided as “input_params.csv”. In the building source file, input parameters for the DL assessment (e.g., NumberofStories, YearBuilt, OccupancyClass, StructureType, PlanArea, ReplacementCost) are specified.

Building source file:

Table 4.2.1.1 input_params.csv

Latitude

Longitude

NumberofStories

YearBuilt

OccupancyClass

StructureType

PlanArea

ReplacementCost

BldgID

BuildingType

DesignLevel

NoUnits

FoundationType

SplitLevel

FirstFloorHt

FloodZone

DSWII

WindZone

AvgJanTemp

RoofShape

RoofSlope

RoofCover

RoofSystem

MeanRoofHt

WindowArea

Garage

z0

Address

City

State

1

39.3946476

-74.38335171

2

1958

RES1

nav

2374.401933

1

NJBF000076747

3001

NE

1

3507

NO

-0.48

6106

124.720242

I

Above

Flat

0

5701

Wood

41.01907403

0

1.1

0.03

304 29TH STREET SO

Brigantine

NJ

2

39.40478555

-74.37292084

2

1950

RES1

nav

1818.980493

1

NJBF000078331

3001

NE

1

3505

NO

1.33

6106

124.815139

I

Above

Hip

0

5701

Wood

37.65467865

0

0

0.03

117 12TH STREET SO

Brigantine

NJ

3

39.40217618

-74.37051873

3

2008

RES1

nav

1619.336001

1

NJBF000077993

3001

NE

1

3502

NO

5.55

6106

124.886524

I

Above

Hip

0

5701

Wood

46.37095544

0

1.1

0.03

323 12TH STREET SO

Brigantine

NJ

4

39.3948475

-74.3972315

3

1997

RES1

nav

3055.902695

1

NJBF000076799

3001

NE

2

3507

YES

0.45

6106

124.458543

I

Above

Gable

0

5701

Wood

45.02992381

0

1.2

0.03

3943 ATL-BRIGANTINE BLVD

Brigantine

NJ

5

39.38753797

-74.39629697

2

1968

RES1

nav

1582.19943

1

NJBF000074714

3001

NE

1

3507

NO

1.25

6112

124.550411

I

Above

Hip

0

5701

Wood

43.76175332

0

0

0.35

11 HUDSON COVE

Brigantine

NJ

6

39.37750766

-74.40624347

3

2008

RES1

nav

2517.40494

1

NJBF000073094

3001

NE

1

3507

NO

0.3

6106

124.468658

I

Above

Hip

0

5701

Wood

44.3589516

0

1.1

0.03

5216 OCEAN DRIVE SOUTH

Brigantine

NJ

7

39.38842155

-74.39774174

2

1968

RES1

nav

1848.058233

1

NJBF000075007

3001

NE

1

3507

NO

2.6

6106

124.51451

I

Above

Gable

0

5701

Wood

30.78723018

0

0

0.03

4503 HARBOR BEACH BLVD

Brigantine

NJ

8

39.39420925

-74.39982597

1

1957

RES1

nav

1741.939084

1

NJBF000076649

3001

NE

1

3507

NO

0.77

6106

124.416655

I

Above

Hip

0

5701

Wood

24.63089952

0

0

0.03

4116 ATL-BRIGANTINE BLVD

Brigantine

NJ

9

39.39605527

-74.37686411

2

1955

RES1

nav

2607.634807

1

NJBF000077041

3001

NE

1

3507

NO

1.91

6106

124.827938

I

Above

Hip

0

5701

Wood

36.70707309

0

0

0.03

404 22ND STREET SO

Brigantine

NJ

10

39.39195514

-74.39192268

2

1975

RES1

nav

1822.850966

1

NJBF000076014

3001

NE

2

3507

NO

0.03

6106

124.587066

I

Above

Gable

0

5701

Wood

42.49945715

0

0

0.03

227 39TH STREET SO

Brigantine

NJ

11

39.39230634

-74.39190046

1

1950

RES1

nav

1452.656628

1

NJBF000076122

3001

NE

1

3507

NO

2.99

6106

124.583936

I

Above

Hip

0

5701

Wood

20.3015897

0

0

0.03

214 38TH STREET SO

Brigantine

NJ

12

39.38827458

-74.40175717

2

1970

RES1

nav

1484.453177

1

NJBF000074951

3001

NE

1

3507

YES

1.24

6106

124.441341

I

Above

Hip

0

5701

Wood

31.03331003

0

1.1

0.03

36 SHIPMASTER DR

Brigantine

NJ

13

39.39283616

-74.38484882

1

1955

RES1

nav

1193.381064

1

NJBF000076280

3001

NE

1

3507

NO

0.15

6106

124.710439

I

Above

Hip

0

5701

Wood

19.31594674

0

0

0.03

344 32ND STREET SO

Brigantine

NJ

14

39.4010149

-74.37840223

2

1956

RES1

nav

2221.005922

1

NJBF000077814

3001

NE

1

3507

YES

2.23

6106

124.749357

I

Above

Hip

0

5701

Wood

31.33017882

0

1.1

0.03

1905 REVERE BLVD

Brigantine

NJ

15

39.39508401

-74.37938833

3

2009

RES1

nav

4174.045602

1

NJBF000076854

3001

NE

1

3507

NO

-0.62

6106

124.790239

I

Above

Flat

0

5701

Wood

37.32194809

0

1.2

0.03

2506 OCEAN AVE

Brigantine

NJ

16

39.39891822

-74.38374712

1

1951

RES1

nav

1854.784061

1

NJBF000077477

3001

NE

1

3507

NO

0.97

6106

124.669854

I

Above

Gable

0

5701

Wood

23.20650706

0

0

0.03

103 26TH STREET SO

Brigantine

NJ

17

39.39846003

-74.37610456

1

1953

RES1

nav

2564.4215

1

NJBF000077411

3001

NE

1

3507

NO

0.91

6106

124.818181

I

Above

Hip

0

5701

Wood

22.62546519

0

0

0.03

315 20TH STREET SO

Brigantine

NJ

18

39.3846399

-74.40300921

2

1980

RES1

nav

7108.155643

1

NJBF000074052

3001

NE

1

3507

YES

-0.9

6110

124.455304

I

Above

Gable

0

5701

Wood

35.41950816

0

0

0.35

36 DELMAR DRIVE

Brigantine

NJ

19

39.39273002

-74.38416265

2

1960

RES1

nav

1044.401088

1

NJBF000076250

3001

NE

1

3507

NO

0.3

6106

124.724352

I

Above

Gable

0

5701

Wood

39.90304837

0

1.2

0.03

3119 OCEAN AVE

Brigantine

NJ

20

39.39505885

-74.39231003

2

1925

RES1

nav

3546.990298

1

NJBF000076852

3001

NE

1

3507

NO

-0.91

6106

124.54835

I

Above

Flat

0

5701

Wood

31.73204006

0

0

0.03

Brigantine

NJ

  1. Regional Mapping Application: This example uses the NearestNeighborEvents regional mapping application. From the parameters set in the configuration file, the algorithm is set to randomly select 10 samples of wind/flooding IMs from the 4 nearest neighbors for each building asset.

  2. Event Application: This example uses the SimCenterEvents event application. It takes as input the EventGrid.csv and corresponding site files, which each have one instance of the intensity measures PWS and FWD assigned to the event site.

Event grid file:

Table 4.2.1.2 EventGrid.csv

sta lon lat

1001.csv -74.55 39.2972

1002.csv -74.55 39.3004

1003.csv -74.55 39.3036

1004.csv -74.55 39.3068

1005.csv -74.55 39.3099

1006.csv -74.55 39.3131

1007.csv -74.55 39.3163

1008.csv -74.55 39.3195

1009.csv -74.55 39.3227

1010.csv -74.55 39.3259

1011.csv -74.55 39.329

1012.csv -74.55 39.3322

1013.csv -74.55 39.3354

1014.csv -74.55 39.3386

1015.csv -74.55 39.3418

1016.csv -74.55 39.345

1017.csv -74.55 39.3481

1018.csv -74.55 39.3513

1019.csv -74.55 39.3545

1020.csv -74.55 39.3577

1021.csv -74.55 39.3609

1022.csv -74.55 39.3641

1023.csv -74.55 39.3672

1024.csv -74.55 39.3704

1025.csv -74.55 39.3736

1026.csv -74.55 39.3768

1027.csv -74.55 39.38

1028.csv -74.55 39.3832

1029.csv -74.55 39.3863

1030.csv -74.55 39.3895

1031.csv -74.55 39.3927

1032.csv -74.55 39.3959

1033.csv -74.55 39.3991

1034.csv -74.55 39.4023

1035.csv -74.55 39.4054

1036.csv -74.55 39.4086

1037.csv -74.55 39.4118

1038.csv -74.55 39.415

1039.csv -74.55 39.4182

1040.csv -74.55 39.4214

1041.csv -74.55 39.4245

1042.csv -74.55 39.4277

1043.csv -74.55 39.4309

1044.csv -74.55 39.4341

1045.csv -74.55 39.4373

1046.csv -74.55 39.4405

1047.csv -74.55 39.4436

1048.csv -74.55 39.4468

1049.csv -74.55 39.45

1050.csv -74.55 39.4532

1051.csv -74.5454 39.2972

1052.csv -74.5454 39.3004

1053.csv -74.5454 39.3036

1054.csv -74.5454 39.3068

1055.csv -74.5454 39.3099

1056.csv -74.5454 39.3131

1057.csv -74.5454 39.3163

1058.csv -74.5454 39.3195

1059.csv -74.5454 39.3227

1060.csv -74.5454 39.3259

1061.csv -74.5454 39.329

1062.csv -74.5454 39.3322

1063.csv -74.5454 39.3354

1064.csv -74.5454 39.3386

1065.csv -74.5454 39.3418

1066.csv -74.5454 39.345

1067.csv -74.5454 39.3481

1068.csv -74.5454 39.3513

1069.csv -74.5454 39.3545

1070.csv -74.5454 39.3577

1071.csv -74.5454 39.3609

1072.csv -74.5454 39.3641

1073.csv -74.5454 39.3672

1074.csv -74.5454 39.3704

1075.csv -74.5454 39.3736

1076.csv -74.5454 39.3768

1077.csv -74.5454 39.38

1078.csv -74.5454 39.3832

1079.csv -74.5454 39.3863

1080.csv -74.5454 39.3895

1081.csv -74.5454 39.3927

1082.csv -74.5454 39.3959

1083.csv -74.5454 39.3991

1084.csv -74.5454 39.4023

1085.csv -74.5454 39.4054

1086.csv -74.5454 39.4086

1087.csv -74.5454 39.4118

1088.csv -74.5454 39.415

1089.csv -74.5454 39.4182

1090.csv -74.5454 39.4214

1091.csv -74.5454 39.4245

1092.csv -74.5454 39.4277

1093.csv -74.5454 39.4309

1094.csv -74.5454 39.4341

1095.csv -74.5454 39.4373

1096.csv -74.5454 39.4405

1097.csv -74.5454 39.4436

1098.csv -74.5454 39.4468

1099.csv -74.5454 39.45

1100.csv -74.5454 39.4532

1101.csv -74.5408 39.2972

1102.csv -74.5408 39.3004

1103.csv -74.5408 39.3036

1104.csv -74.5408 39.3068

1105.csv -74.5408 39.3099

1106.csv -74.5408 39.3131

1107.csv -74.5408 39.3163

1108.csv -74.5408 39.3195

1109.csv -74.5408 39.3227

1110.csv -74.5408 39.3259

1111.csv -74.5408 39.329

1112.csv -74.5408 39.3322

1113.csv -74.5408 39.3354

1114.csv -74.5408 39.3386

1115.csv -74.5408 39.3418

1116.csv -74.5408 39.345

1117.csv -74.5408 39.3481

1118.csv -74.5408 39.3513

1119.csv -74.5408 39.3545

1120.csv -74.5408 39.3577

1121.csv -74.5408 39.3609

1122.csv -74.5408 39.3641

1123.csv -74.5408 39.3672

1124.csv -74.5408 39.3704

1125.csv -74.5408 39.3736

1126.csv -74.5408 39.3768

1127.csv -74.5408 39.38

1128.csv -74.5408 39.3832

1129.csv -74.5408 39.3863

1130.csv -74.5408 39.3895

1131.csv -74.5408 39.3927

1132.csv -74.5408 39.3959

1133.csv -74.5408 39.3991

1134.csv -74.5408 39.4023

1135.csv -74.5408 39.4054

1136.csv -74.5408 39.4086

1137.csv -74.5408 39.4118

1138.csv -74.5408 39.415

1139.csv -74.5408 39.4182

1140.csv -74.5408 39.4214

1141.csv -74.5408 39.4245

1142.csv -74.5408 39.4277

1143.csv -74.5408 39.4309

1144.csv -74.5408 39.4341

1145.csv -74.5408 39.4373

1146.csv -74.5408 39.4405

1147.csv -74.5408 39.4436

1148.csv -74.5408 39.4468

1149.csv -74.5408 39.45

1150.csv -74.5408 39.4532

1151.csv -74.5362 39.2972

1152.csv -74.5362 39.3004

1153.csv -74.5362 39.3036

1154.csv -74.5362 39.3068

1155.csv -74.5362 39.3099

1156.csv -74.5362 39.3131

1157.csv -74.5362 39.3163

1158.csv -74.5362 39.3195

1159.csv -74.5362 39.3227

1160.csv -74.5362 39.3259

1161.csv -74.5362 39.329

1162.csv -74.5362 39.3322

1163.csv -74.5362 39.3354

1164.csv -74.5362 39.3386

1165.csv -74.5362 39.3418

1166.csv -74.5362 39.345

1167.csv -74.5362 39.3481

1168.csv -74.5362 39.3513

1169.csv -74.5362 39.3545

1170.csv -74.5362 39.3577

1171.csv -74.5362 39.3609

1172.csv -74.5362 39.3641

1173.csv -74.5362 39.3672

1174.csv -74.5362 39.3704

1175.csv -74.5362 39.3736

1176.csv -74.5362 39.3768

1177.csv -74.5362 39.38

1178.csv -74.5362 39.3832

1179.csv -74.5362 39.3863

1180.csv -74.5362 39.3895

1181.csv -74.5362 39.3927

1182.csv -74.5362 39.3959

1183.csv -74.5362 39.3991

1184.csv -74.5362 39.4023

1185.csv -74.5362 39.4054

1186.csv -74.5362 39.4086

1187.csv -74.5362 39.4118

1188.csv -74.5362 39.415

1189.csv -74.5362 39.4182

1190.csv -74.5362 39.4214

1191.csv -74.5362 39.4245

1192.csv -74.5362 39.4277

1193.csv -74.5362 39.4309

1194.csv -74.5362 39.4341

1195.csv -74.5362 39.4373

1196.csv -74.5362 39.4405

1197.csv -74.5362 39.4436

1198.csv -74.5362 39.4468

1199.csv -74.5362 39.45

1200.csv -74.5362 39.4532

1201.csv -74.5316 39.2972

1202.csv -74.5316 39.3004

1203.csv -74.5316 39.3036

1204.csv -74.5316 39.3068

1205.csv -74.5316 39.3099

1206.csv -74.5316 39.3131

1207.csv -74.5316 39.3163

1208.csv -74.5316 39.3195

1209.csv -74.5316 39.3227

1210.csv -74.5316 39.3259

1211.csv -74.5316 39.329

1212.csv -74.5316 39.3322

1213.csv -74.5316 39.3354

1214.csv -74.5316 39.3386

1215.csv -74.5316 39.3418

1216.csv -74.5316 39.345

1217.csv -74.5316 39.3481

1218.csv -74.5316 39.3513

1219.csv -74.5316 39.3545

1220.csv -74.5316 39.3577

1221.csv -74.5316 39.3609

1222.csv -74.5316 39.3641

1223.csv -74.5316 39.3672

1224.csv -74.5316 39.3704

1225.csv -74.5316 39.3736

1226.csv -74.5316 39.3768

1227.csv -74.5316 39.38

1228.csv -74.5316 39.3832

1229.csv -74.5316 39.3863

1230.csv -74.5316 39.3895

1231.csv -74.5316 39.3927

1232.csv -74.5316 39.3959

1233.csv -74.5316 39.3991

1234.csv -74.5316 39.4023

1235.csv -74.5316 39.4054

1236.csv -74.5316 39.4086

1237.csv -74.5316 39.4118

1238.csv -74.5316 39.415

1239.csv -74.5316 39.4182

1240.csv -74.5316 39.4214

1241.csv -74.5316 39.4245

1242.csv -74.5316 39.4277

1243.csv -74.5316 39.4309

1244.csv -74.5316 39.4341

1245.csv -74.5316 39.4373

1246.csv -74.5316 39.4405

1247.csv -74.5316 39.4436

1248.csv -74.5316 39.4468

1249.csv -74.5316 39.45

1250.csv -74.5316 39.4532

1251.csv -74.527 39.2972

1252.csv -74.527 39.3004

1253.csv -74.527 39.3036

1254.csv -74.527 39.3068

1255.csv -74.527 39.3099

1256.csv -74.527 39.3131

1257.csv -74.527 39.3163

1258.csv -74.527 39.3195

1259.csv -74.527 39.3227

1260.csv -74.527 39.3259

1261.csv -74.527 39.329

1262.csv -74.527 39.3322

1263.csv -74.527 39.3354

1264.csv -74.527 39.3386

1265.csv -74.527 39.3418

1266.csv -74.527 39.345

1267.csv -74.527 39.3481

1268.csv -74.527 39.3513

1269.csv -74.527 39.3545

1270.csv -74.527 39.3577

1271.csv -74.527 39.3609

1272.csv -74.527 39.3641

1273.csv -74.527 39.3672

1274.csv -74.527 39.3704

1275.csv -74.527 39.3736

1276.csv -74.527 39.3768

1277.csv -74.527 39.38

1278.csv -74.527 39.3832

1279.csv -74.527 39.3863

1280.csv -74.527 39.3895

1281.csv -74.527 39.3927

1282.csv -74.527 39.3959

1283.csv -74.527 39.3991

1284.csv -74.527 39.4023

1285.csv -74.527 39.4054

1286.csv -74.527 39.4086

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3496.csv -74.325 39.4405

3497.csv -74.325 39.4436

3498.csv -74.325 39.4468

3499.csv -74.325 39.45

3500.csv -74.325 39.4532

Site file:

Table 4.2.1.3 1596.csv

PWS

FWD

102.27

-4

  1. This example does not use a Modeling, EDP, Simulation, or UQ application because EDPs are not simulated with structural analysis.

  2. DL Application: This example uses the pelicun DL application. From the building source file, since the DL method selected is “HAZUS MH HU”, damage/loss estimation is performed using the HAZUS loss assessment method based on hurricane IMs. Custom IM-based vulnerability functions are specified in the auto_HU_NJ.py.

4.2.2. Run Workflow

The workflow can be executed by uploading the appropriate files to DesignSafe, or by running the example on your local desktop, using the following initialization command in the terminal:

python "C:/rWHALE/applications/Workflow/R2D_workflow.py" "C:/rWHALE/hurricane_example/rWHALE_config_hu.json" --registry "C:/rWHALE/applications/Workflow/WorkflowApplications.json" --referenceDir "C:/rWHALE/hurricane_example/input_data/" -w "C:/rWHALE/hurricane_example/results"

This command locates the backend applications in the folder “applications”, and the input files in a directory “hurricane_example”. Please ensure that the paths in the command appropriately identify the locations of the files in your directory.

applications
hurricane_example
├── rWHALE_config_hu.json          # configuration file
└── input_data
   ├── IMs
       ├── EventGrid.csv           # event grid file
       ├── 1596.csv                # event IM files
       .
       .
       .
       └── 3500.csv
   ├── auto_HU_NJ.py               # DL autoscript
   └── input_params.csv            # building source file

4.2.3. Outputs

The example output files can be downloaded here: output_data_hu.zip. For more information about the output files produced, refer to Outputs.

  1. EDP_1-20.csv: reports statistics on EDP results, where the hazard IM is used as the EDP. As in HAZUS, the statistics reported are the median and lognormal standard deviation of peak wind speed (PWS) and flood water depth (FWD).

Table 4.2.3.1 EDP_1-20.csv

type

FWD

FWD

PWS

PWS

loc

1

1

1

1

dir

1

1

1

1

stat

median

beta

median

beta

1

8.225099999999998

0.10752438826522986

101.68

0.0015953462953481874

2

10.058

0.13049583215381486

100.54

8.88267250866992e-16

3

8.836

4.44133625433496e-16

100.54

8.88267250866992e-16

4

8.225099999999998

0.15691122836794946

102.27

0.0

5

3.9485

0.3179051915983189

102.27

0.0

6

10.669

0.2951379187180969

102.27

0.0

7

4.5595

0.42950255783800906

102.27

0.0

8

10.058

0.10762581604123418

102.27

0.0

9

9.4469

0.09854679237809824

100.54

0.00021139399188185612

10

6.3923

0.21909706937123827

102.27

0.0

11

7.0032

0.17821584780807276

102.27

0.0

12

7.6141

0.2506359538273619

102.27

0.0

13

7.0032

0.10851459635385508

101.87999999999998

0.0

14

11.890999999999998

0.07732474893386784

100.54

8.88267250866992e-16

15

8.836

0.0860439700924928

101.04

0.0009026049623865203

16

10.669

0.0772137889056351

101.11

0.001668504359737387

17

8.225099999999998

0.0749705372877345

100.54

8.88267250866992e-16

18

3.3376

0.14377526005530392

102.27

0.0

19

7.0032

0.0

101.87999999999998

0.0

20

9.4469

0.18249880706081664

102.27

0.0

  1. DM_1-20.csv: reports collapse probability and damage state probability for each building asset. In the table below the “DSG_DS” values 0, 1_1, 2_1, 3_1, and 4_1 are four wind-induced damage states (from minor to severe), while the rest are the flood-induced damage states.

Table 4.2.3.2 DM_1-20.csv

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

DS likelihood

Collapse

comp_type

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

other

probability

DSG_DS

0

1_1

2_1

3_1

4_1

0.1

10_1

11_1

12_1

13_1

14_1

15_1

16_1

17_1

18_1

19_1

1_1.1

2_1.1

3_1.1

4_1.1

5_1

6_1

7_1

8_1

9_1

1

0.13180000000000006

0.4476

0.11939999999999995

0.024400000000000036

0.2768

1.0

0.0

0.10399999999999998

0.2794

0.5168

0.007999999999999993

0.07880000000000001

0.013000000000000001

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

2

0.3622

0.5112000000000001

0.111

0.0156

0.0

1.0

0.0

0.0

0.08179999999999997

0.3598

0.13880000000000006

0.17439999999999994

0.2422

0.003

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

3

0.2016

0.6182

0.1142

0.008200000000000006

0.0578

1.0

0.0

0.0

0.0041999999999999815

0.6802

0.3156

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

4

0.12980000000000005

0.4264

0.11419999999999995

0.02839999999999998

0.3012

1.0

0.02200000000000002

0.16959999999999995

0.12580000000000002

0.3802

0.1358

0.1658

0.0008

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

5

0.598

0.35440000000000005

0.0444

0.001

0.0022

0.027000000000000024

0.027000000000000024

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.10519999999999996

0.1848

0.22420000000000007

0.4588

0.0

6

0.17379999999999995

0.609

0.1252

0.010800000000000004

0.0812

0.9018

0.0

0.0

0.000399999999999956

0.061200000000000025

0.04180000000000006

0.6799999999999999

0.1184

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0552

0.043

0.0

7

0.2338

0.5594

0.1848

0.02

0.002

0.244

0.04920000000000002

0.0

0.04620000000000002

0.14559999999999995

0.003000000000000003

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.000399999999999956

0.07140000000000002

0.12819999999999998

0.027800000000000047

0.5282

0.0

8

0.5539999999999999

0.3856

0.0548

0.0014000000000000002

0.0042

1.0

0.0

0.0

0.12739999999999996

0.3696

0.1744

0.3098

0.0188

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

9

0.354

0.5126000000000001

0.1172

0.0162

0.0

1.0

0.0

0.0

0.0202

0.14200000000000002

0.6954

0.0396

0.006400000000000003

0.0914

0.005

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

10

0.22340000000000004

0.4782

0.18

0.01680000000000001

0.1016

0.7964

0.057400000000000014

0.5294

0.1962

0.013399999999999995

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0005999999999999893

0.10960000000000003

0.0934

0.0

11

0.536

0.3924

0.0668

0.0013999999999999998

0.0034

0.9082

0.0444

0.4464

0.3854

0.03199999999999999

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0002000000000000057

0.0574

0.0342

0.0

12

0.17979999999999996

0.6074

0.1336

0.0092

0.07

0.6412

0.035599999999999965

0.016800000000000037

0.5147999999999999

0.07400000000000001

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.014800000000000037

0.344

0.0

13

0.5724

0.3662

0.057400000000000014

0.0038

0.0002

1.0

0.0023999999999999573

0.4420000000000001

0.455

0.0007999999999999953

0.0392

0.0606

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

14

0.20940000000000006

0.6228

0.1104

0.005199999999999996

0.0522

1.0

0.0

0.0

0.0

0.000199999999999978

0.13660000000000005

0.17359999999999998

0.4328

0.2564

0.0004

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

15

0.14680000000000004

0.4434

0.11259999999999998

0.02280000000000004

0.2744

1.0

0.0

0.0

0.003000000000000003

0.4842

0.2212

0.2458

0.0458

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

16

0.4506

0.444

0.0846

0.0048

0.016

1.0

0.0

0.0

0.024000000000000018

0.07299999999999995

0.02039999999999997

0.7478

0.1348

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

17

0.614

0.3342

0.0488

0.003

0.0

1.0

0.0

0.0

0.18020000000000005

0.5364

0.2164

0.067

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

18

0.5056

0.4252

0.0664

0.0028

0.0

0.027599999999999958

0.027599999999999958

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.10340000000000003

0.5920000000000001

0.27699999999999997

0.0

19

0.134

0.4436

0.11759999999999997

0.025000000000000026

0.2798

1.0

0.0008000000000000228

0.4892

0.509

0.001

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

20

0.2318

0.5609999999999999

0.1862

0.019000000000000003

0.002

1.0

0.03280000000000005

0.2642

0.0958

0.04979999999999996

0.4782

0.07880000000000001

0.0004

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

  1. DV_1-20.csv: reports decision variable estimates (i.e., repair cost) for each building asset.

Table 4.2.3.3 DV_1-20.csv

DV

Repair Cost

Repair Cost

Repair Cost

Repair Cost

Repair Cost

Repair Impractical

comp_type

aggregate

aggregate

aggregate

aggregate

aggregate

probability

DSG_DS

stat

mean

std

10%

median

90%

1

51.674037830370615

31.125156859612943

23.44095160698559

31.36359086924329

100.0

0.2768

2

35.70378122147112

5.414766158386084

29.99999999999999

34.76050733856777

42.49042899758899

0.0

3

38.2725884473557

17.062183920350414

28.0

32.057354889690096

44.677766574083996

0.0578

4

53.91285103443637

31.554315784685308

25.99999999999999

39.13495749359598

100.0

0.3012

5

17.487533751801266

6.8732611345023225

7.765881991182407

15.680269258158072

23.59465652513371

0.0022

6

45.370678864917686

18.923298223341924

32.057354889690096

41.773645909121626

53.511580159157496

0.0898

7

23.241619882817808

7.302579011200807

14.000000000000016

25.44451139413511

33.25958502928005

0.002

8

47.38349873962555

4.7933840013911615

44.0

46.00000000000002

52.12013667822615

0.0042

9

34.421257986383516

4.576287890994681

30.000000000000018

34.76050733856777

42.000000000000014

0.0

10

33.833638285575105

22.986247976561

18.887984754415488

26.638933162371536

100.0

0.1016

11

41.57041621998247

8.89029608368382

25.490924968792385

41.99999999999999

48.31596357687845

0.0034

12

33.37779044068296

20.135085602945942

22.000000000000032

26.227580278031212

39.377478423039925

0.07

13

43.918269298044414

3.615294391844954

40.00000000000002

41.99999999999999

47.87427267794129

0.0002

14

45.56422210203436

14.476103596445546

34.000613093576426

41.77364590912164

53.511580159157496

0.057

15

53.87525442307725

29.50975159170383

29.382931053678867

39.28623013150104

100.0

0.2744

16

48.93064288146455

6.923106178860219

45.99999999999999

48.21567177241414

50.14928907108546

0.016

17

46.01297616246538

2.968573887668522

41.99999999999999

44.00000000000002

49.7832065574391

0.0

18

16.75530231075945

3.8678471617165635

13.232328076498716

15.216828181800453

23.154828603007225

0.0

19

48.96596518724591

32.835758448404896

23.44095160698559

27.40227123811441

100.0

0.2798

20

31.158790561826766

5.359807294623833

25.444511394135144

30.000000000000018

33.259585029280075

0.002