Predicting building damages in mega-disasters under uncertainty: An improved Bayesian network learning approach

This paper develops a hybrid approach that integrates the cloud model and Bayesian networks (BNs) to predict building damages induced by earthquakes under uncertainty. The cloud model is used to perform data discretization for modeling information losses and uncertainties. The learned BN from the historical data by using the Peter-Clark (PC) algorithm is validated by using the k-fold cross-validation. Three types of analysis, including predictive, diagnosis, and sensitivity analysis, are conducted for new knowledge discovery. A selected dataset consisting of 9920 buildings in the 2015 Nepal earthquake is used to demonstrate the adaptability and significance of the proposed approach. Results imply that (1) The constructed BN displays a high precision in predicting earthquake-induced building damage, where the accuracy of the 10-fold cross-validation ranges from 0.89 to 0.92. (2) Building structures and foundation materials are identified as critical factors leading to severe building damages. (3) The proposed approach possesses better prediction capabilities than the conventional BN approach, where an average improvement of 3.5 % in terms of the average accuracy is achieved. This research provides a data-driven perspective to perceive the magnitude of building damages subjected to uncertainties and complexities.