An Analysis of Housing Structures’ Earthquake Vulnerability in Two Parts of Dhaka City

Abstract

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.

Properties of Old Concrete Built in the Former Leipziger Palace

“…It should also be noted that in active seismic regions, earthquake damage is an important causality, not only in old historical buildings [9,10], thus assessments of old concrete and reinforced concrete buildings have to be extended with earthquake knowledge [11][12][13][14]. Nevertheless, earthquake disaster damage is correlated to the types of building structures [15,16].…”

Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest

“…Tree-based machine learning methodologies have mainly been applied in seismic vulnerability studies for parameter evaluation [45][46][47]. For other natural disasters, these methodologies have also been used to determine the relative influence of seismic parameters on the model results.…”

Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea

“…Riedel et al (2015) [44] and Liu et al (2019) [45] proposed building seismic vulnerability prediction methods based on building characteristics using the SVM and ARL approaches. Alizadeh et al (2018) [2] studied the social vulnerability of Tabriz, Iran, using an ANN-based seismic-threat model, and Ahmed and Morita (2018) [46] analyzed the seismic vulnerability of residential buildings in Dhaka, Bangladesh, based on RF and DT approaches.…”