Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An Analysis of Housing Structures’ Earthquake Vulnerability in Two Parts of Dhaka City
The damage done in earthquake disasters is correlated to the types of housing structures that are present. In the last two decades of urbanization in Dhaka, rapid growth without proper planning has been a major concern. This study evaluates the performance of the decision tree and random forest techniques to predict structures’ vulnerability factors for buildings as a step towards improving earthquake disaster preparedness. Applying the decision tree algorithm to locations (wards) in Dhaka North City Corporation (DNCC) and Dhaka South City Corporation (DSCC), we observed some important predictors of earthquake damage. Decision tree analysis reveals that the most important predictor for structures that fare well in earthquakes is the use of reinforced concrete, and a common factor among the most vulnerable structures is the soft story building style in the DNCC and DSCC areas. The random forest technique also showed reinforced concrete as being the most important factor for lowering the risk for housing structures, with the model having a 24.19% out-of-bag (OOB) error. As for vulnerability, soft story construction was a significant factor in estimating earthquake susceptibility (40.32% OOB error). The findings reveal that building materials in the DNCC are stronger than those in the DSCC but soft story buildings are more common in the DNCC, which make it one of the weakest parts of the area and point to the need to make plans to seismically retrofit soft story buildings.
An Analysis of Housing Structures’ Earthquake Vulnerability in Two Parts of Dhaka City
The damage done in earthquake disasters is correlated to the types of housing structures that are present. In the last two decades of urbanization in Dhaka, rapid growth without proper planning has been a major concern. This study evaluates the performance of the decision tree and random forest techniques to predict structures’ vulnerability factors for buildings as a step towards improving earthquake disaster preparedness. Applying the decision tree algorithm to locations (wards) in Dhaka North City Corporation (DNCC) and Dhaka South City Corporation (DSCC), we observed some important predictors of earthquake damage. Decision tree analysis reveals that the most important predictor for structures that fare well in earthquakes is the use of reinforced concrete, and a common factor among the most vulnerable structures is the soft story building style in the DNCC and DSCC areas. The random forest technique also showed reinforced concrete as being the most important factor for lowering the risk for housing structures, with the model having a 24.19% out-of-bag (OOB) error. As for vulnerability, soft story construction was a significant factor in estimating earthquake susceptibility (40.32% OOB error). The findings reveal that building materials in the DNCC are stronger than those in the DSCC but soft story buildings are more common in the DNCC, which make it one of the weakest parts of the area and point to the need to make plans to seismically retrofit soft story buildings.
An Analysis of Housing Structures’ Earthquake Vulnerability in Two Parts of Dhaka City
Md Sohel Ahmed (Autor:in) / Hiroshi Morita (Autor:in)
2018
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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