基于改进PSO-BP算法的矩形顶管隧道地表沉降预测
Journal: Building Technology Research DOI: 10.12238/btr.v7i5.4533
Abstract
在矩形顶管隧道工程中,现有有限元、数值模拟等方法难以准确的对隧道顶进施工引起的地表沉降进行预测。因此,本文基于BP神经网络的多参数输入下对任意函数的高逼近性,考虑自适应变异方法,采用自适应惯性权重和变异粒子后期寻优改进的粒子群算法(PSO),确定预测模型的最优超参数,建立超浅埋大断面矩形顶管隧道地表沉降PSO-BP预测模型。通过对超浅埋大断面矩形顶管隧道案例进行研究,将该算法与传统算法结合现场监测数据进行对比分析和预测。预测结果表明：改进的PSO-BP神经网络预测模型相比于传统BP神经网络预测模型在地表沉降变化平缓和凹凸性较大时均呈现更加稳定的预测效果,预测沉降值与真实值较为接近,预测的准确性和鲁棒性显著提高。
Keywords
BP神经网络；地表沉降；隧道施工；矩形顶管；粒子群算法
Funding
湖南省自然科学基金(No.2023JJ30110)；湖南省教育厅科学研究重点项目(No.23A0568)。
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[1] JAIN A K,MAO J,MOHIUDDIN K M.Artificial neural networks: A tutorial[J].Computer,1996,29(3):31-44.
[2] JAVAD G, NARGES T. Application of artificial neural networks to the prediction of tunnel boring machine penetra tion rate[J].Mining Science and Technology(China), 2010, 20(5):727-33.
[3] 陈仁朋,戴田,张品.基于机器学习算法的盾构掘进地表沉降预测方法[J].湖南大学学报(自然科学版),2021,48(7):111-8.
[4] RAMEZANSHIRAZIM,SEBASTIANID,MILIZIANOS.ArtificialIntelligence to Predict Maximum Surface Settlements Induced by Mechanized Tunnelling; proceedings of the National Confe rence of the Researchers of Geotechnical Engineering, F, 2019 [C].Springer.
[5] TANG J C, PENG L, CHEN Z. A Computational Approach of Displacement Prediction in an Engineering Project; proceedi ngs of the Journal of Physics: Conference Series, F, 2022 [C]. IOP Publishing.
[6] CHEN R,ZHANG P,WU H,etal.Prediction of shield tunneling -induced ground settlement using machine learning techniques [J]. Frontiers of Structural and Civil Engineering, 2019, 13(6):1363-78.
[7] ELBAZ K, SHEN S-L, SUN W-J, et al. Prediction model of shield performance during tunneling via incorporating improved particle swarm optimization into ANFIS[J].IEEE Access,2020,8:39659-71.
[8] CAO Y, ZHOU X, YAN K. Deep learning neural network model for tunnel ground surface settlement prediction based on sensor data[J].Mathematical Problems in Engineering,2021.
[9] LI C,LI J, SHI Z, et al. Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine -Learning Algorithms[J].Geofluids,2022.
[10] GHIASI V, KOUSHKI M. Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil[J].SN Applied Sciences,2020,2(5):1-14.
[11] ZHANG P, CHEN R-P, WU H-N. Real-time analysis and regulation of EPB shield steering using Random Forest[J]. Aut omation in Construction,2019,106:102860.
[12] 马文辉,杨成永,彭华.大直径泥水盾构下穿地铁挡墙路基沉降控制[J].湖南大学学报(自然科学版),2020,47(03):44-53.
[2] JAVAD G, NARGES T. Application of artificial neural networks to the prediction of tunnel boring machine penetra tion rate[J].Mining Science and Technology(China), 2010, 20(5):727-33.
[3] 陈仁朋,戴田,张品.基于机器学习算法的盾构掘进地表沉降预测方法[J].湖南大学学报(自然科学版),2021,48(7):111-8.
[4] RAMEZANSHIRAZIM,SEBASTIANID,MILIZIANOS.ArtificialIntelligence to Predict Maximum Surface Settlements Induced by Mechanized Tunnelling; proceedings of the National Confe rence of the Researchers of Geotechnical Engineering, F, 2019 [C].Springer.
[5] TANG J C, PENG L, CHEN Z. A Computational Approach of Displacement Prediction in an Engineering Project; proceedi ngs of the Journal of Physics: Conference Series, F, 2022 [C]. IOP Publishing.
[6] CHEN R,ZHANG P,WU H,etal.Prediction of shield tunneling -induced ground settlement using machine learning techniques [J]. Frontiers of Structural and Civil Engineering, 2019, 13(6):1363-78.
[7] ELBAZ K, SHEN S-L, SUN W-J, et al. Prediction model of shield performance during tunneling via incorporating improved particle swarm optimization into ANFIS[J].IEEE Access,2020,8:39659-71.
[8] CAO Y, ZHOU X, YAN K. Deep learning neural network model for tunnel ground surface settlement prediction based on sensor data[J].Mathematical Problems in Engineering,2021.
[9] LI C,LI J, SHI Z, et al. Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine -Learning Algorithms[J].Geofluids,2022.
[10] GHIASI V, KOUSHKI M. Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil[J].SN Applied Sciences,2020,2(5):1-14.
[11] ZHANG P, CHEN R-P, WU H-N. Real-time analysis and regulation of EPB shield steering using Random Forest[J]. Aut omation in Construction,2019,106:102860.
[12] 马文辉,杨成永,彭华.大直径泥水盾构下穿地铁挡墙路基沉降控制[J].湖南大学学报(自然科学版),2020,47(03):44-53.
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