摘要
This paper aims to explore the ability of genetic programming(GP)to achieve the intelligent prediction of tunnelling-induced building deformation considering the multifactor impact.A total of 1099 groups of data obtained from 22 geotechnical centrifuge tests are used for model development and analysis using GP.Tunnel volume loss,building eccentricity,soil density,building transverse width,building shear stiffness and building load are selected as the inputs,and shear distortion is selected as the output.Results suggest that the proposed intelligent prediction model is capable of providing a reasonable and accurate prediction of framed building shear distortion due to tunnel construction with realistic conditions,highlighting the important roles of shear stiffness of framed buildings and the pressure beneath the foundation on structural deformation.It has been proven that the proposed model is efficient and feasible to analyze relevant engineering problems by parametric analysis and comparative analysis.The findings demonstrate the great potential of GP approaches in predicting building distortion caused by tunnelling.The proposed equation can be used for the quick and intelligent prediction of tunnelling induced building deformation,providing valuable guidance for the practical design and risk assessment of urban tunnel construction projects.
准确预测隧道开挖引起的建筑物变形对优化城市隧道的设计与施工至关重要。目前,有关利用遗传编程预测隧道开挖引起建筑物变形的研究鲜有报道。本文旨在探索遗传编程在预测隧道施工引起建筑变形方面的能力,并考虑多种参数对预测结果的影响。为训练并验证遗传编程的模型,采用了从22组离心模型试验中获取的1099组数据。通过对试验工况的分析,选择了隧道体积损失率、建筑物偏心率、砂土密度、建筑物横向宽度、建筑物剪切刚度和建筑物荷载6种参数作为模型的输入参数。为预测建筑的最大变形,选择建筑的最大剪切变形为模型的输出值。研究结果表明,本文得到的智能预测模型能够对实际工况隧道施工引起的框架结构剪切变形进行合理、准确的预测,明确了框架结构剪切刚度和建筑荷载对结构变形的重要作用。通过参数分析和对比分析,证明了该模型对相关工程问题的预测分析是有效可行的。因此,研究结果证明了遗传编程方法在预测隧道开挖引起建筑物变形方面具有巨大的潜力。通过对建筑物变形的快速智能预测,该预测模型可为城市隧道建设项目的设计和风险评估提供有价值的指导。
作者
XU Jing-min
WANG Chen-cheng
CHENG Zhi-liang
XU Tao
ZHANG Ding-wen
LI Zi-li
徐敬民;王陈成;程志良;徐涛;章定文;李孜理(School of Transportation,Southeast University,Nanjing 211189,China;Department of Engineering,University of Cambridge,Cambridge CB 21 TN,UK;College of Pipeline and Civil Engineering,China University of Petroleum(East China),Qingdao 266580,China;Civil,Structural and Environmental Engineering,University College Cork,Cork T 12 K 8 AF,Ireland;Department of Civil and Environmental Engineering,Massachusetts Institute of Technology,MA 02139,USA)
基金
Projects(52108364,52278398)supported by the National Natural Science Foundation of China
Project(211179)supported by the Royal Society,UK
Project(22CX06051A)supported by the Independent Innovation Research Plan Project of China University of Petroleum(East China)
Project(ZR2023QE004)supported by the Shandong Provincial Natural Science Foundation,China。
作者简介
Corresponding author:XU Tao,PhD,Associate Professor,E-mail:taoxu@seu.edu.cn,ORCID:https://orcid.org/0000-0001-6958-7944。
