Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach

This paper develops a novel hybrid information fusion approach that integrates cloud model (CM), Dempster-Shafer (D-S) evidence theory and Monte Carlo (MC) simulation technique to perceive safety risk of tunnel-induced building damage under uncertainty. The correlation measurement in the CM framework is used to construct basic probability assignments (BPAs) within different risk states of input factors. An improved combination rule that incorporates the Dempster' rule and the weighted mean rule is used to deal with multi-source evidence with conflicts. The MC technique is used to simulate the input observation by using probability distribution in order to describe and reduce underlying uncertainty during the characterization and measurement of input factors. A multi-layer information fusion framework is proposed for the safety risk perception, with both hard data and soft data taken into account. Four buildings adjacent to the excavation of one tunnel section in Wuhan metro system in China are utilized as a case study to demonstrate the effectiveness and applicability of the developed approach. Results indicate that the developed approach is capable of (i) synthesizing multi-source information to achieve a more accurate result for safety risk perception, and (ii) identifying global sensitivities of input factors under uncertainty. Reliability of safety risk perception results is further tested under different scenarios with different bias levels in the measurement of input factors, and the developed approach proves to have a strong robustness and fault-tolerant capacity. This approach can be used by practitioners in the industry as a decision tool to perceive and anticipate the potential safety risks in tunneling projects.