A collapse risk assessment method for subway foundation pit based on cloud model and improved Dempster-Shafer evidence theory

Abstract

Collapse is the main engineering hazard in the construction of open-cut foundation pit, and risk assessment is one of the important ways to reduce it. Aiming at the high conflicting evidence fusion failure, a fast convergence and highly reliable multi-data fusion method based on cloud model (CM) and improved Dempster-Shafer evidence theory is proposed, which can achieve accurate assessment of subway pit collapse risk. Firstly, CM is introduced to quantify the qualitative metrics. Then, A new correction parameter is defined for improving the conflicting among evidence bodies based on conflict degree, discrepancy degree and uncertainty, while a fine-tuning term is added to reduce the subjective effect of global focal element assignment. Finally, the risk level is obtained by the maximum affiliation principle. The method has been successfully applied to Luochongwei Station. The difference between the maximum value and the second largest value of the basic probability assignment is 0.624, and the global uncertainty degree is 0.087, both of which satisfy the decision evaluation condition, but other methods can only satisfy one or neither of them. And it requires only 5 cycles to reach the steady state by fusing data of the same index, which has faster convergence compared with other methods. The proposed method has good universality and effectiveness in subway pit collapse risk assessment.