摘要
为有效优化盾构施工参数,实现在大直径泥水盾构掘进过程中安全、高效和节能的目标,提出分类助推(CatBoost)和基于分解的多目标进化算法(MOEAD)相结合的混合智能算法;综合考虑盾构施工参数与地质条件,以主要的盾构施工参数为研究对象,选择地表沉降、贯入度和掘进比能为预测和控制目标;优化调控选择的盾构施工参数,并以武汉市轨道交通某号线为例,验证该混合算法的有效性。结果表明:采用CatBoost算法建立的预测模型在大直径泥水盾构上表现出来的预测性能良好,对3个控制目标的拟合精度(R 2)均达到0.9以上;预测模型的重要性排序表明:大直径泥水盾构的总推进力和推进速度对地表沉降、贯入度和掘进比能有显著影响;所提出的CatBoost-MOEAD混合智能算法对3个控制目标的优化效果明显,地表沉降、贯入度和掘进比能分别达到12.35%、7.47%和10.70%的优化幅度,并给出相应盾构施工参数的控制范围。
To effectively optimize the shield construction parameters and achieve the goals of safety,efficiency,and energy-saving in the large-diameter slurry shield tunneling process,a hybrid intelligent algorithm combining categorical boosting(CatBoost)and decomposition was proposed based on a multi-objective evolutionary algorithm(MOEAD).The main shield construction parameters were set as the major research objects considering shield construction parameters and geological conditions,and the surface settlement,penetration rate,and tunneling-specific energy were determined as the prediction and control objectives.Moreover,the selected shield construction parameters were optimized,and a line of Wuhan rail transit was used to validate the hybrid algorithm performance.The results showed that the proposed CatBoost algorithm had great prediction performance for large-diameter slurry shields with the fitting accuracy(R 2)of the three control objectives more than 0.9.The model's importance rank indicated that the total propulsion force and propulsion speed of the large-diameter slurry shield had significant influences on surface settlement,penetration,and tunneling-specific energy.The proposed CatBoost-MOEAD hybrid intelligent algorithm had an obvious optimization effect on the three control objectives,and the optimization ranges of surface settlement,penetration rate,and tunneling-specific energy reached 12.35%,7.47%,and 10.70%,respectively.Moreover,the control ranges of corresponding shield construction parameters were presented.
作者
吴贤国
刘俊
苏飞鸣
陈虹宇
冯宗宝
WU Xianguo;LIU Jun;SU Feiming;CHEN Hongyu;FENG Zongbao(School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan Hubei 430074,China;Department of Building and Real Estate,The Hong Kong Polytechnic University,Hong Kong 999077,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2024年第6期57-64,共8页
China Safety Science Journal
基金
国家自然科学基金资助(51378235,71571078,51308240)
国家重点研发计划项目(2016YFC0800208)。