Intelligent decision on shield construction parameters based on safety evaluation model and sparrow search algorithm

Wei Gao, Shuangshuang Ge, Shuang Cui and Xin Chen

Reliability Engineering and System Safety, 2025, vol. 260, issue C

Abstract: Determination the suitable shield construction parameters is an important work to ensure the engineering construction safety. Traditional method for determination construction parameters relies on people's experience and lacks precision and adaptability. To overcome this limitation, based on the safety evaluation model using deep learning method of whale optimizing deep belief network (WO-DBN) and optimization method of sparrow search algorithm (SSA), one new method has been proposed. In this method, the WO-DBN, in which the whale optimization algorithm (WOA) has been used to select the suitable parameters of deep belief network (DBN), has been applied to safety evaluation of shield tunnel construction, and SSA has been used to determine the suitable construction parameters. By this method, the suitable construction parameters can be automatically determine by their adjustment sequence according to the sensitivity for safety, and the construction control standard is satisfied simultaneously. Then, the new method has been applied to the shield tunneling of Guangzhou subway line 18 in China to determine the suitable construction parameters for the safety evaluation objectives (ground settlement and segment floating). The results show that by the obtained construction parameters, the safety control standards for ground settlement and segment floating can be improved. This shows that the proposed method has a great contribution to improve the safety of construction by about 68 % and 89 %, respectively. In the future, the physical information and geological uncertainty can be considered in this method, which is the in-depth research to improve its adaptability.

Keywords: Intelligent decision; Shield tunnel; Construction parameters; Deep learning method; Optimization method (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025001760

DOI: 10.1016/j.ress.2025.110973

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