Developing a Probability of Collapse Model for Residential Buildings in Lombok Island, Indonesia

Abstract

Title: Developing a Probability of Collapse Model for Residential Buildings in Lombok Island, Indonesia

Abstract

A destructive earthquake is among the deadliest and most expensive natural disasters, capable of destroying whole communities, claiming tens of thousands of lives, and causing economic losses in the tens of billions of dollars within a short timeframe. Indonesia, situated in highly active seismic zone, faces significant risk related to earthquakes. With a population of nearly 270 million, the country grapples with serious challenges due by seismic events. The main societal concern during earthquakes revolves around non-engineered and poorly constructed residential buildings, which are susceptible to collapse due to ground shaking. The vulnerability of Indonesian society to earthquakes was seen in 2018 when a series of earthquakes struck Lombok Island. Four consecutive earthquakes on 28 July (Mw 6.3), 5 August (Mw 6.9), and 19 August (Mw 6.4 and Mw 6.9) resulted in the loss of over 500 lives and damaged nearly 200,000 residential buildings. Collapsed buildings were the primary cause of both the casualties and economic losses. This study presents the development of an empirical probability of collapse model for residential buildings in Lombok using the earthquake that occurred on 5 August 2018 as a scenario hazard, as this particular seismic event caused significant damage to the island. The development of the model involves the integration of ground motion model and an exposure/collapse model for dwellings. The ground motion model simulated in this study integrate source parameters, ground motion prediction equations (GMPEs), and local site conditions. OpenQuake, an open-source software for seismic risk and hazard assessment developed by the Global Earthquake Model (GEM) Foundation, was utilised to simulate the model in this study. The methodology used for the development of building exposure and collapse model was based on multiple datasets: OpenStreetMap (OSM), post-earthquake survey, government census data, and high-resolution satellite images captured prior to and subsequent to the 2018 earthquakes. The outcomes presented in this research provide valuable insights for policy makers, urban planners, and engineers to enhance resilience against seismic hazards and contribute to the broader understanding risk assessment in Lombok. Furthermore, the probability of collapse model presented in this study has the potential to be explored on national scale to test its reliability in predicting the potential distribution of residential buildings collapses due to seismic events.