文献类型：期刊文献

中文题名：基于数据驱动的大直径泥水盾构掘进性能多目标优化决策

英文题名：Data-Driven Multi-Objective Optimization Decision-Making for Large-Diameter Slurry Shield Tunneling Performance

作者：刘四进[1];谭帅[2];王华伟[1];刘颂玉[1];朱学豪[2];连鑫[1]

机构：[1]中铁十四局集团有限公司,山东济南250000;[2]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

年份：2026

卷号：46

期号：4

起止页码：704

中文期刊名：隧道建设(中英文)

外文期刊名：Tunnel Construction

收录：;EI(收录号：20261820617893);北大核心:【北大核心2023】；

基金：国家自然科学基金(62273147);上海市自然科学基金(22ZR1417000);中国铁建股份有限公司科研计划项目(2024-W09);中铁十四局集团有限公司科技重大专项项目(913700001630559891202301)。

语种：中文

中文关键词：泥水盾构;掘进性能;多目标优化;数据驱动;NSGA-Ⅱ;长短时记忆网络

外文关键词：slurry shield;tunneling performance;multiobjective optimization;data-driven;nondominated sorting genetic algorithm Ⅱ;long short-term memory network

摘要：为解决盾构掘进过程中效率、安全与能耗难以协同优化的问题,提出一种面向盾构实时掘进的多目标优化决策方法。首先,提出一种双阶段注意力机制的长短时记忆网络,用于构建掘进参数与性能指标之间的预测模型,并基于该预测模型构建目标函数,分析盾构掘进过程中的机理约束和经验约束;同时,为提高多目标优化算法在复杂约束条件下求解全局最优解的效率与解集多样性,设计一种基于算子动态更新策略的第2代非支配排序遗传算法Ⅱ(NSGA-Ⅱ),并利用优劣解距离法(TOPSIS)对Pareto前沿解集进行综合决策,获得不同偏好策略下的最优掘进参数组合,用于指导盾构参数设定;最后,基于盾构掘进环切数据库获取的湛江湾海底隧道项目数据,在2种不同的掘进工况下进行试验验证,并进行区段优化试验,效率、安全、能耗指标相较于实际掘进性能提高了7.65%、10.26%和8.89%,且其余性能指标均有所提高。
Efficiency,safety,and energy consumption during shield tunneling are difficult to optimize synergistically.To address this challenge,a multiobjective optimization decision-making method oriented toward real-time shield tunneling is proposed.This method aims to balance the trade-offs among efficiency,safety,and energy consumption,thereby enhancing overall tunneling performance.First,a dual-stage attention-based long short-term memory network is proposed to construct a prediction model linking tunneling parameters with performance indicators.Based on this model,objective functions are formulated,and both the mechanism and empirical constraints within the tunneling process are analyzed.Furthermore,to improve the efficiency of identifying global optimal solutions and enhance the diversity of the solution set under complex constraints,a nondominated sorting genetic algorithm II based on a dynamic operator update strategy is developed.The technique for order preference by similarity to the ideal solution method(TOPSIS)is then employed to conduct comprehensive decision-making on the Pareto front solution set,obtaining optimal tunneling parameter combinations under different preference strategies to guide parameter settings.Finally,using data from the shield tunneling ring database of the Zhanjiang Bay subsea tunnel project,experimental verification is conducted under two different tunneling conditions,along with section optimization tests.Under strategies prioritizing efficiency,safety,and energy consumption,performance improves by 7.65%,10.26%,and 8.89%,respectively,compared to real-world tunneling performance,with other performance indicators also showing improvement.