Summary

Most of the Unreinforced Masonry (URM) buildings are quite old in Europe based on 'Building stock inventory to assess seismic vulnerability across Europe' [1] report. Following the earthquakes (Albania, Italy, etc.) that occurred in Europe, it was revealed that masonry buildings are extremely vulnerable. While probabilistic and deterministic approaches are important for examining a small number of buildings, they do not offer the opportunity to examine a large building stock in a short period of time. Rapid Visual Screening (RVS) methods are used to identify building preand post-earthquake vulnerability. Several RVS techniques have been presented in literature over last 30 years. Recent earthquakes have highlighted critical necessity of a rapid vulnerability assessment method for pre-earthquake warning, mitigation, preparedness, and post-earthquake damage state assessment of existing buildings. These findings demonstrate the importance of using an accurate RVS technique to inspect buildings. Due to the subjectivity of the screener, these RVS methods contain uncertainty and vagueness. Fuzzy Inference System (FIS) overcomes nonrandom uncertainty and vagueness by considering building characteristics in terms of their degree of truth. This paper introduces a FIS-based SRVS case implementation and compares FIS-based Soft-RVS (S-RVS) to traditional RVS methods for identifying building damage state taking into account rapid visual assessment reports about damage caused by the 2019 Albania earthquake. To determine the damage states of URM buildings, 40 buildings damaged in the 2019 Albania earthquake were analyzed and processed to use in the applied fuzzy logic mathematical model. Initial findings demonstrate that the site-specific FIS-based S-RVS method is capable of accurately determining the damage states of at least half of the buildings.

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Document information

Published on 24/11/22
Accepted on 24/11/22
Submitted on 24/11/22

Volume Computational Applied Mathematics, 2022
DOI: 10.23967/eccomas.2022.132
Licence: CC BY-NC-SA license

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