8. Damage assessment

Flood or cyclone damage to a unique road bridge

gmtra.damage.road_bridge_flood_cyclone(x, design_table, depth_threshs, param_values, events, all_rps, sensitivity=False)

Function to estimate the range of either flood or cyclone damages to an individual bridge asset.

Arguments:

x : row in geopandas GeoDataFrame that represents an individual asset.

design_table : A NumPy array that represents the design standards for different bridge types, dependent on road type.

depth_thresh : A list with failure thresholds. Either contains flood depths or gust speeds.

param_values : A NumPy Array with sets of parameter values we would like to test.

events : A list with the unique hazard events in row x.

all_rps : A list with all return periods for the hazard that is being considered.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified bridge, based on the parameter set.

Flood or cyclone damage to a unique railway bridge

gmtra.damage.rail_bridge_flood_cyclone(x, design_table, depth_threshs, param_values, events, all_rps, sensitivity=False)

Function to estimate the range of either flood or cyclone damages to an individual bridge asset.

Arguments:

x : row in geopandas GeoDataFrame that represents an individual asset.

design_table : A NumPy array that represents the design standards for different bridge types, dependent on road type.

depth_thresh : A list with failure thresholds. Either contains flood depths or gust speeds.

param_values : A NumPy Array with sets of parameter values we would like to test.

events : A list with the unique hazard events in row x.

all_rps : A list with all return periods for the hazard that is being considered.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified bridge, based on the parameter set.

Earthquake damage to a unique road bridge

gmtra.damage.road_bridge_earthquake(x, eq_curve, param_values, events, all_rps, sensitivity=False)

Function to estimate the range of earthquake damages to an individual bridge asset.

Arguments:

x : A row in a geopandas GeoDataFrame that represents an individual asset.

eq_curve : A pandas DataFrame with unique damage curves for earthquake damages.

param_values : A NumPy Array with sets of parameter values we would like to test.

events : A list with the unique hazard events in row x.

all_rps : A list with all return periods for the hazard that is being considered.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified bridge, based on the parameter set.

Earthquake damage to a unique railway bridge

gmtra.damage.rail_bridge_earthquake(x, eq_curve, param_values, vals_EQ, events, all_rps, sensitivity=False)

Function to estimate the range of earthquake damages to an individual bridge asset.

Arguments:

x : A row in a geopandas GeoDataFrame that represents an individual asset.

eq_curve : A pandas DataFrame with unique damage curves for earthquake damages.

param_values : A NumPy Array with sets of parameter values we would like to test.

vals_EQ : A list with the unique hazard events in row x.

all_rps : A list with all return periods for the hazard that is being considered.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified bridge, based on the parameter set.

Cyclone damage to a unique road asset

gmtra.damage.road_cyclone(x, events, param_values, sensitivity=False)

Function to estimate the range of cyclone damages to an individual road asset.

Arguments:

x : row in geopandas GeoDataFrame that represents an individual asset.

events : A list with the unique hazard events in row x.

param_values : A NumPy Array with sets of parameter values we would like to test.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified asset, based on the parameter set.

Cyclone damage to a unique railway asset

gmtra.damage.rail_cyclone(x, events, param_values, sensitivity=False)

Function to estimate the range of cyclone damages to an individual railway asset.

Arguments:

x : row in geopandas GeoDataFrame that represents an individual asset.

events : A list with the unique hazard events in row x.

param_values : A NumPy Array with sets of parameter values we would like to test.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified asset, based on the parameter set.

Earthquake damage to a unique road asset

gmtra.damage.road_earthquake(x, global_costs, paved_ratios, frag_tables, events, wbreg_lookup, param_values, sensitivity=False)

Function to estimate the range of earthquake damages to an individual road asset.

Arguments:

x : row in geopandas GeoDataFrame that represents an individual asset.

global_costs : A pandas DataFrame with the total cost for different roads in different World Bank regions. These values are based on the ROCKS database.

paved_ratios : A pandas DataFrame with road pavement percentages per country for each road type.

frag_tables : A NumPy Array with a set of unique fragility tables which relate PGA to liquefaction to estimate the damage to the asset. events : A list with the unique earthquake events.

wbreg_lookup : a dictioniary that relates countries (in ISO3 codes) with World Bank regions.

param_values : A NumPy Array with sets of parameter values we would like to test.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified asset, based on the parameter set.

Earthquake damage to a unique railway asset

gmtra.damage.rail_earthquake(x, frag_tables, events, param_values, sensitivity=False)

Function to estimate the range of earthquake damages to an individual railway asset.

Arguments:

x : row in geopandas GeoDataFrame that represents an individual asset.

frag_tables : A NumPy Array with a set of unique fragility tables which relate PGA to liquefaction to estimate the damage to the asset.

events : A list with the unique earthquake events.

param_values : A NumPy Array with sets of parameter values we would like to test.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified asset, based on the parameter set.

Flood damage to a unique road asset

gmtra.damage.road_flood(x, global_costs, paved_ratios, flood_curve_paved, flood_curve_unpaved, events, wbreg_lookup, param_values, val_cols, sensitivity=False)

Function to estimate the range of flood damages to an individual road asset.

Arguments:

x : row in geopandas GeoDataFrame that represents an individual asset.

global_costs : A pandas DataFrame with the total cost for different roads in different World Bank regions. These values are based on the ROCKS database.

paved_ratios : A pandas DataFrame with road pavement percentages per country for each road type.

flood_curve_paved : A pandas DataFrame with a set of damage curves for paved roads.

flood_curve_unpaved : A pandas DataFrame with a set of damage curves for unpaved roads.

events : A list with the unique flood events.

wbreg_lookup : a dictioniary that relates a country ID (ISO3 code) with its World Bank region.

param_values : A NumPy Array with sets of parameter values we would like to test.

val_cols : A list with the unique flood events in row x.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified asset, based on the parameter set.

Flood damage to a unique railway asset

gmtra.damage.rail_flood(x, curve, events, param_values, val_cols, wbreg_lookup, sensitivity=False)

Function to estimate the range of flood damages to an individual road asset.

Arguments:

x : row in geopandas GeoDataFrame that represents an individual asset.

curve : A pandas DataFrame with a set of damage curves for railways.

events : A list with the unique flood events.

param_values : A NumPy Array with sets of parameter values we would like to test.

val_cols : A list with the unique flood events in row x.

wbreg_lookup : a dictioniary that relates a country ID (ISO3 code) with its World Bank region.

Optional Arguments:

sensitivity : Default is False. Set to True if you would like to return all damage values to be able to perform a sensitivity analysis.

Returns:

list : A list with the range of possible damages to the specified asset, based on the parameter set.

Regional damage to all bridge assets

gmtra.damage.regional_bridge(file, data_path, param_values, income_lookup, eq_curve, design_tables, depth_threshs, wind_threshs, rail=False)

Function to estimate the summary statistics of all bridge damages in a region

Arguments:

file : path to the .feather file with all bridges of a region.

data_path : file path to location of all data.

param_values : A NumPy Array with sets of parameter values we would like to test.

income_lookup : A dictionary that relates a country ID (ISO3 code) with its World Bank income goup.

eq_curve : A pandas DataFrame with unique damage curves for earthquake damages.

design_table : A NumPy array that represents the design standards for different bridge types, dependent on road type.

depth_thresh : A list with failure depth thresholds.

wind_threshs : A list with failure wind gustspeed thresholds.

Optional Arguments:

rail : Default is False. Set to True if you would like to intersect the railway assets in a region.

Returns:

DataFrame : a pandas DataFrame with summary damage statistics for the loaded region.

Cyclone damage to all assets in a region

gmtra.damage.regional_cyclone(file, data_path, events, param_values, rail=False)

Function to estimate the summary statistics of all cyclone damages in a region to road assets. Cyclone damage for roads is currently based on clean-up cost and repairs.

Arguments:

file : path to the .feather file with all bridges of a region.

data_path : file path to location of all data.

events : A list with the unique cyclone events.

param_values : A NumPy Array with sets of parameter values we would like to test.

Optional Arguments:

rail : Default is False. Set to True if you would like to intersect the railway assets in a region.

Returns:

DataFrame : a pandas DataFrame with summary damage statistics for the loaded region.

Earthquake damage to all assets in a region

gmtra.damage.regional_earthquake(file, data_path, global_costs, paved_ratios, events, wbreg_lookup, rail=False)

Function to estimate the summary statistics of all earthquake damages in a region to road assets

Arguments:

file : path to the .feather file with all bridges of a region.

data_path : file path to location of all data.

global_costs : A pandas DataFrame with the total cost for different roads in different World Bank regions. These values are based on the ROCKS database.

paved_ratios : A pandas DataFrame with road pavement percentages per country for each road type.

events : A list with the unique earthquake events.

wbreg_lookup : a dictioniary that relates countries (in ISO3 codes) with World Bank regions.

Optional Arguments:

rail : Default is False. Set to True if you would like to intersect the railway assets in a region.

Returns:

DataFrame : a pandas DataFrame with summary damage statistics for the loaded region.

Flood damage to all assets in a region

gmtra.damage.regional_flood(file, hzd, data_path, global_costs, paved_ratios, flood_curve_paved, flood_curve_unpaved, events, wbreg_lookup, rail=False)

Function to estimate the summary statistics of all flood damages in a region to road assets

Arguments:

file : path to the .feather file with all bridges of a region.

hzd : abbrevation of the hazard we want to intersect. FU for river flooding, PU for surface flooding and CF for coastal flooding.

data_path : file path to location of all data.

global_costs : A pandas DataFrame with the total cost for different roads in different World Bank regions. These values are based on the ROCKS database.

paved_ratios : A pandas DataFrame with road pavement percentages per country for each road type.

flood_curve_paved : A pandas DataFrame with a set of damage curves for paved roads.

flood_curve_unpaved : A pandas DataFrame with a set of damage curves for unpaved roads.

events : A list with the unique flood events.

wbreg_lookup : a dictioniary that relates a country ID (ISO3 code) with its World Bank region.

Optional Arguments:

rail : Default is False. Set to True if you would like to intersect the railway assets in a region.

Returns:

DataFrame : a pandas DataFrame with summary damage statistics for the loaded region.