The construction risk of deep foundation pit (DFP) engineering is high, and accidents occur frequently. It is necessary to evaluate the risk of deep foundation pits before construction. At present, although there are many risk assessment methods, there is not one with strong applicability and high accuracy. Based on expert scoring, this paper analyses the risk from two aspects (the severity of consequences and the probability of occurrence), divides the severity of the consequences into five indexes, calculates the risk by using the analytic hierarchy process (AHP), and sets the expert weight index so that the subjective expert scoring result can obtain the best possible objective calculation result. In addition, this paper uses the membership function from fuzzy mathematics to establish the level of risk and optimize the evaluation criteria of risk events. An engineering example is introduced, and the result of the risk assessment shows that the evaluation result R (risk value) obtained by the optimized risk assessment method in this paper is 7.9 and that the level of risk is grade III. The risk assessment method proposed in this paper has strong applicability and can obtain more accurate evaluation results. This method can provide a reference for the risk assessment of deep foundation pit engineering.
The tunneling collapse is the main engineering hazard in the construction of the drilling-and-blasting method. The accurate assessment of the tunneling collapse risk has become a key issue in tunnel construction. As for assessing the tunneling collapse risk and providing basic risk controlling strategies, this research proposes a novel multi-source information fusion approach that combines Bayesian network (BN), cloud model (CM), support vector machine (SVM), Dempster–Shafer (D–S) evidence theory, and Monte Carlo (MC) simulation technique. Those methods (CM, BN, SVM) are used to analyze multi-source information (i.e. statistical data, physical sensors, and expert judgment provided by humans) respectively and construct basic probability assignments (BPAs) of input factors under different risk states. Then, these BPAs will be merged at the decision level to achieve an overall risk evaluation, using an improved D–S evidence theory. The MC technology is proposed to simulate the uncertainty and randomness of data. The novel approach has been successfully applied in the case of the Jinzhupa tunnel of the Pu-Yan Highway (Fujian, China). The results indicate that the developed new multi-source information fusion method is feasible for (a) Fusing multi-source information effectively from different models with a high-risk assessment accuracy of 98.1%; (b) Performing strong robustness to bias, which can achieve acceptable risk assessment accuracy even under a 20% bias; and (c) Exhibiting a more outstanding risk assessment performance (97.9% accuracy) than the single-information model (78.8% accuracy) under a high bias (20%). Since the proposed reliable risk analysis method can efficiently integrate multi-source information with conflicts, uncertainties, and bias, it provides an in-depth analysis of the tunnel collapse and the most critical risk factors, and then appropriate remedial measures can be taken at an early stage.
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