MULTI-RISK MANAGEMENT FOR UNDERSEA TUNNEL CONSTRUCTION BASED ON NSGA-III ALGORITHM

19th WORLD CONFERENCE OF THE ASSOCIATED RESEARCH CENTRES FOR THE URBAN UNDERGROUND SPACE, Belgrade, Serbia, November 4-7, 2025. (Paper No: 7.2.123,  pp. 933-941)

 

АУТОР(И) / AUTHOR(S): Sihan Liu, Hongwei Huang

 

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DOI:  10.46793/ACUUS2025.7.2.123

САЖЕТАК / ABSTRACT:

Due to the complexity of construction techniques, long construction periods, high investment costs, numerous safety-related factors, and stringent quality requirements, managing multi-source information in subsea tunnel projects often lacks coordination, making it difficult to achieve unified multi-objective goals. This poses a common challenge for construction decision-makers during on-site management. To address these gaps, this study focuses on safety, schedule, and cost risks in undersea tunnel construction, designing a multi-object model to address these challenges. By integrating the relationships among safety levels, schedule and cost across various construction processes, a multi-objective optimization model tailored to the management of complex undersea tunnel construction projects is proposed. The model is solved by the NSGA-III algorithm to obtain the Pareto solution set. This approach is applied to a subsea tunnel project, and the results show that when the number of generations is set to 600, the population size to 140, and the number of reference points per dimension to 14, a total of 170 solutions are obtained. Among them, the construction duration can be optimized by up to 132 days, and the cost by up to 110.58 million CNY, while both quality and safety levels remain within acceptable project limits. These results can assist decision-makers in selecting the optimal construction plan from multiple perspectives.

КЉУЧНЕ РЕЧИ / KEYWORDS:

Tunnel construction; Construction management; Multi-object optimization; NSGA-III algorithm

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

The work described in this paper is supported by the Qingdao Conson Second Jiaozhou Bay Subsea Tunnel Co., Ltd, National Natural Science Foundation of China (Grant Nos. 52130805, 52408434) and the Shanghai Pujiang Talent Program (2022PJD077).

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