Seismic Building Damage Prediction From GIS-Based Building Data Using Artificial Intelligence System

Abstract

The estimation of seismic damage to buildings is complicated due to the many sources of uncertainties. This study aims to develop a new approach using an artificial intelligence system called adaptive neuro-fuzzy inference system (ANFIS) model to predict the damage of buildings at urban scale considering input uncertainties. First, the study performed seismic damage evaluation of buildings utilizing the capacity spectrum method (CSM) to obtain a set of 57,648 training data from a combination of three main parameters, i.e., 6 earthquake magnitudes, 8 structural types, and 1,201 distances. Next, the data was used to develop a practical ANFIS model for the seismic damage prediction. The variables of the fuzzy system are earthquake magnitudes, structural types, and distance between epicenter and building. To validate the applicability of the proposed model, analyses of spatial seismic building damage under five possible earthquakes in Chiang Mai Municipality were performed by using the proposed methodology. From the comparison of the damaged urban area, small discrepancies between the CSM and the ANFIS results could be observed. It should be noted that the proposed ANFIS model can predict the seismic building damage reasonably well compared with the CSM. Using the method proposed herein, it is possible to create damage scenarios for earthquake-prone areas where only a few seismic data are available, such as developing countries.

GIS Multisource Data for the Seismic Vulnerability Assessment of Buildings at the Urban Scale

“…In [64], the authors propose a new approach using an artificial intelligence system model implemented on the basis of a large set of data about different types of buildings surveyed and collected using the GIS for many years; the aim is to predict the damage to buildings on an urban scale, considering input uncertainties, by means of capacity spectrum method (CSM) to obtain a large set of training data from a combination of three parameters: earthquake magnitudes, structural types, and distances between the epicenter and buildings.…”

“…In this framework, the GIS represents a suitable tool to collect a large number of data and images [62,63] useful for the training phase. These developments make possible the comprehensive use of a variety of factors and the rapid extrapolation of necessary parameters for seismic assessment on a large scale, and they make it possible to check the reliability and uncertainty of the information gathered and results obtained using traditional technics [63,64].…”

Design of English Teaching Corpus Resource-Sharing System Based on XML Technology

“…(1) Specification of data standards: first, develop information standards and then formulate unified data standards to establish reliable data centers. is stage means establishing the basic information coding standards for the school [29,30]. (2) Design of the central database: after the data standard is determined, the database structure of the shared data center is designed, which is the key to building the data center.…”

Analysis of Non-Destructive Indicating Properties for Predicting Compressive Strengths of Dendrocalamus sericeus Munro Bamboo Culms

“…Bamboo offers environmental benefits over other conventional construction materials, e.g., steel and concrete, with lower Global Warming Potential (GWP) values [ 8 , 9 ]. With the merit of high flexibility, using bamboo for structural elements reduces building damage under an earthquake attack [ 10 , 11 , 12 , 13 ] and other extreme loads [ 14 , 15 , 16 , 17 ].…”