Uncertainty and Fuzzy Decisions in Earthquake Risk Evaluation of Buildings

Abstract

The Northern region of Thailand has been considered as one of the seismic risk zones. However, most existing buildings in the area had been designed and constructed based on old building design codes without seismic consideration. Therefore, those buildings are required to upgrade based on earthquake building damage risk evaluation. With resource limitations, it is not feasible to retrofit all buildings in a short period. In addition, the results of the risk evaluation contain uncertain inputs and outputs. The objective of this study is to prioritize building retrofit based on fuzzy earthquake risk assessment. The risk assessment of a building was made considering the risk factors including (1) building vulnerability, (2) seismic intensity and (3) building values. Then, the total risk was calculated by integrating all the risk factors with their uncertainties using a fuzzy rule based model. An example of the retrofit prioritization is shown here considering the three fuzzy factors. The ranking is hospital, temple, school, government building, factory and house, respectively. The result helps decision makers to screen and prioritize the building retrofitting in the seismically prone area.

Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model

“…Some practical comparisons carried out between different national RVS methods and their robustness have been discovered [12][13][14]. Although RVS is an acceptable estimation method that gives a general overview to do proper earthquake mitigation planning, the variables considered contain a level of uncertainty [15].…”

Designing a Data Warehouse for Earthquake Risk Assessment of Buildings: A Case Study for Healthcare Facilities

“…Because of that, earthquake is a critical threat for people living in this country. It is clear that, healthcare facilities have higher risks than other types of buildings due to their high importance [3]. Thus, assessing healthcare facilities is quite necessary for disaster preparedness process.…”

A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

“…A substantial literature has been published in the attempts of integrating several methods from various domains with RVS, for instance, statistical methods [11,12], Artificial Neural Network (ANN) [13][14][15][16][17], multi-criteria decision making [18,19], and type-1 [20][21][22][23] and type-2 [24,25] fuzzy logic systems are frequently assimilated within RVS for increasing the interface and efficacy of seismic vulnerability screening. However, there are methods developed to evaluate the damage and change detection of buildings by remote sensing and image analysis [26,27].…”