Earthquakes used to cause significant harm, including loss of life and damage to buildings and infrastructure. For example, the 2006 Yogyakarta earthquake in Indonesia resulted in widespread devastation, injuries, and extensive damage. In the past, people relied on Seismic Risk Assessment (SRA) to estimate the chances of earthquake-related damage to buildings and infrastructure and the economic losses involved. SRA used vulnerability functions to understand how susceptible buildings were to earthquake damage. Many places and situations used the Hazard United States (HAZUS) system, which had categories like slight, moderate, extensive, and complete damage, to classify building damage. However, there used to be differences in expert opinions about earthquake vulnerability due to variations in their knowledge and experience. Experts often used words like “very high” or “low irregularity” to express their understanding, and they evaluated these factors using qualitative logic. Different approaches were explored in the past to tackle the complexity and uncertainty in the assessment process, including fuzzy logic. The methodology presented in this paper introduced a framework called Fuzzy Analytic Hierarchy Process (FAHP). This framework aimed to assist decision-makers, engineers, and policymakers in choosing the most appropriate category for assessing earthquake-induced housing damage. Four experts with over twenty years of experience in disaster management, earthquake-affected residential housing, and related fields were involved in the research. The goal was to present a method for estimating the Best Nonfuzzy Performance value (BNP) weight based on differences in Peak Ground Acceleration (PGA) zoning (green, yellow, and red zones) in the Bantul district. The results showed that slight damage had the highest score in the green zone, while complete damage had the lowest score. Similarly, in the yellow zone, slight damage maintained the highest score, while complete damage received the lowest score. Lastly, moderate damage was identified as the most critical in the red zone, and complete damage had the lowest score. These findings had implications for decision-makers, engineers, and policymakers: 1). Decision-makers could use this information to allocate budgets efficiently for safety measures. 2). Engineers were able to focus on strengthening structures in the green zone for slight damage and allocate more resources to address moderate damage in the red zone. 3). Policymakers had the opportunity to tailor disaster response plans based on the predominant damage state in each zone, allowing them to prioritize evacuations and resource deployment accordingly. This paper provides an overview that needs to be developed by researchers in order to improve the results and offer more effective education.
Read full abstract
Oct 14, 2024
Muhammad Azeem Akbar + 5
Open Access
