First level seismic risk assessment of old unreinforced masonry (URM) using fuzzy synthetic evaluation

The seismic risk of old unreinforced masonry (URM) buildings is inherently high due to their non-ductile behavior. Masonry buildings constructed without seismic resistance characteristics have performed poorly in previous earthquakes. There is a clear need for reliable vulnerability assessment techniques for identifying the most vulnerable buildings to support risk mitigation strategies in urban areas. Seismic risk evaluation and mitigation planning are often performed in a multi-level risk assessment approach, where the first level is meant to identify relatively vulnerable buildings that require further assessment. This paper proposes a fuzzy syntheticbased first level seismic risk evaluation technique for identifying relatively risky URM buildings in an urban area. Fourteen risk factors, categorized into five indexes, are used to estimate the overall seismic risk of individual URM buildings. The proposed approach is illustrated for 81 old URM buildings of an urban settlement at Shakhari Bazar, Dhaka city, Bangladesh, using data collected as part of a technical assessment project by Dhaka City Corporation. The results of the case study show that most of the buildings (more than 90%) have high to a very high level of risk and are expected to suffer severe damage in case of future large earthquakes in this region. The outcome of this study is compared with a previous study conducted for the same urban settlement. The proposed method provides a simple procedure for prioritizing riskiest URM buildings for seismic rehabilitation.

Automated summary

This paper proposes a fuzzy synthetic-based first level seismic risk evaluation technique for identifying relatively risky unreinforced masonry (URM) buildings in urban areas, using 14 risk factors categorized into five indexes. The majority of the buildings in the case study showed high to very high levels of risk, indicating the need for prioritizing seismic rehabilitation efforts. The proposed method provides a simple and effective approach for identifying and addressing high-risk URM buildings in urban settlements.