Xianguo Wu

TL;DR: The proposed systemic decision support approach for safety risk analysis under uncertainty in tunnel construction can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment.

TL;DR: This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time, to overcome deficiencies of traditional fault analysis methods.

TL;DR: A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed with detailed step‐by‐step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN‐based inference, defuzzification, and decision making.

TL;DR: The proposed reliable risk analysis method can efficiently fuse multi-sensory information with ubiquitous uncertainties, conflicts, and bias, and provides in-depth analysis for structural health status together with the most critical risk factors, and then proper remedial actions can be taken at an early stage.

TL;DR: In this article, a hybrid information fusion approach that integrates cloud model (CM), Dempster-Shafer (D-S) evidence theory and Monte Carlo (MC) simulation technique was developed to perceive safety risk of tunnel-induced building damage under uncertainty.