Intelligent prediction model of tunnelling-induced building deformation based on genetic programming and its application

Abstract

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.

Keywords

building deformation / genetic programming / tunnel construction / modification factor

References

[1] MAIR R J, TAYLOR R N, BURLAND J B. Prediction of ground movements and assessment of risk of building damage due to bored tunnelling [C]// International Symposium on Geotechnical Aspects of Underground Construction in Soft Ground, 1996: 713–718

[2] PottsD M, AddenbrookeT I. A structure’s influence on tunnellinginduced ground movements [J]. Proceedings of the Institution of Civil Engineers: Geotechnical Engineering, 1997, 125(2): 109-125

[3] FranziusJ N, PottsD M, BurlandJ B. The response of surface structures to tunnel construction [J]. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, 2006, 159(1): 3-17

[4] XuJ-m, FranzaA, MarshallA M. Response of framed buildings on raft foundations to tunneling [J]. Journal of Geotechnical and Geoenvironmental Engineering, 2020, 146(11): 04020120

[5] XuJ-m, FranzaA, MarshallA M, et al. . Role of footing embedment on tunnel-foundation interaction [J]. Journal of Geotechnical and Geoenvironmental Engineering, 2021, 147906021009

[6] XuJ-m, FranzaA, MarshallA M, et al. . Tunnel-framed building interaction: Comparison between raft and separate footing foundations [J]. Géotechnique, 2021, 717631-644

[7] LiuW, LiangJ-x, XuTao. Tunnelling-induced ground deformation subjected to the behavior of tail grouting materials [J]. Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research, 2023, 140: 105253

[8] TangQ-l, PengL-m, DengE, et al. . Dynamic responses of train under deformation caused by underpass shield tunnel construction based on stochastic field theory [J]. Journal of Central South University, 2023, 30(3): 1006-1026

[9] BoldiniD, LosaccoN, FranzaA, et al. . Tunneling-induced deformation of bare frame structures on sand: numerical study of building deformations [J]. Journal of Geotechnical and Geoenvironmental Engineering, 2021, 147(11): 04021116

[10] ZhouZ, ZhengY-d, HuJ-f, et al. . Deformation analysis of shield undercrossing and vertical paralleling excavation with existing tunnel in composite stratum [J]. Journal of Central South University, 2023, 3093127-3144

[11] LeiM-f, ShiY-b, TangQ-l, et al. . Construction control technology of a four-hole shield tunnel passing through pile foundations of an existing bridge: A case study [J]. Journal of Central South University, 2023, 30(7): 2360-2373

[12] GongC-j, XieC-r, LinZ-q, et al. . Ground deformation prediction induced by shield tunnelling considering existing multi-story buildings [J]. Journal of Central South University, 2023, 30(4): 1373-1387

[13] LiuX, ZhangR, FangQ, et al. . Subgrade settlements of existing railway lines and operational parameters of shield machine induced by twin shield tunnel excavations: A case study [J]. Journal of Central South University, 2024, 31(1): 272-287

[14] XU Jing-min, ZHENG Luo-rui, SONG Ge-yang, et al. Effects of embedded walls on tunnelling-induced sandy ground displacements: A numerical investigation [J]. Géotechnique, 2024: 1–14. DOI: https://doi.org/10.1680/jgeot.23.00307.

[15] XuJ-m, GuiJ-s, SheilB. A numerical investigation of the role of basements on tunnel-frame interaction in sandy soil [J]. Computers and Geotechnics, 2024, 169(5): 106197

[16] PanQ-j, WuH-t, ZhangZ-l, et al. . Prediction of tunneling-induced ground surface settlement within composite strata using multi-physics-informed neural network [J]. Rock and Soil Mechanics, 2024, 452539-551

[17] SheilB. Prediction of microtunnelling jacking forces using a probabilistic observational approach [J]. Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research, 2021, 109103749

[18] Sheil BrianB, Suryasentana StephenK, Mooney MichaelA, et al. . Discussion: Machine learning to inform tunnelling operations: Recent advances and future trends [J]. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 2020, 173(1): 180-181

[19] ZhangP, WuH-n, ChenR-p, et al. . Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study [J]. Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research, 2020, 99103383

[20] PanQ-j, LiX-z, HuangS, et al. . Application of machine learning to intelligent shield tunnelling: review and prospects [J]. Hazard Control in Tunnelling and Underground Engineering, 2022, 4(3): 10-30(in Chinese)

[21] PhoonK K, ZhangW-gang. Future of machine learning in geotechnics [J]. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2023, 17(1): 7-22

[22] ZhouC, GaoY-y, ChenE J, et al. . Deep learning technologies for shield tunneling: Challenges and opportunities [J]. Automation in Construction, 2023, 154104982

[23] AhangariK, MoeinossadatS R, BehniaD. Estimation of tunnelling-induced settlement by modern intelligent methods [J]. Soils and Foundations, 2015, 55(4): 737-748

[24] HasanipanahM, Noorian-BidgoliM, Jahed ArmaghaniD, et al. . Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling [J]. Engineering with Computers, 2016, 32(4): 705-715

[25] ChenR-p, ZhangP, WuH-n, et al. . Prediction of shield tunneling-induced ground settlement using machine learning techniques [J]. Frontiers of Structural and Civil Engineering, 2019, 13(6): 1363-1378

[26] KohestaniV R, BazarganlariM R, AsgariM J. Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest [J]. Journal of AI and Data Mining, 2017, 5(1): 127-135

[27] ZhangL-m, WuX-g, JiW-y, et al. . Intelligent approach to estimation of tunnel-induced ground settlement using wavelet packet and support vector machines [J]. Journal of Computing in Civil Engineering, 2017, 31(2): 04016053

[28] MahmoodzadehA, MohammadiM, DaraeiA, et al. . Forecasting maximum surface settlement caused by urban tunneling [J]. Automation in Construction, 2020, 120103375

[29] MoeinossadatS R, AhangariK, ShahriarK. Modeling maximum surface settlement due to EPBM tunneling by various soft computing techniques [J]. Innovative Infrastructure Solutions, 2017, 3110

[30] LiuL-n, ZhouW, GutierrezM. Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets [J]. Journal of Rock Mechanics and Geotechnical Engineering, 2022, 1441028-1041

[31] GandomiA H, AlaviA H. A new multi-gene genetic programming approach to non-linear system modeling. Part II: Geotechnical and earthquake engineering problems [J]. Neural Computing and Applications, 2012, 21(1): 189-201

[32] ChengZ-l, KannangaraK K P M, SuL-j, et al. . Mathematical model for approximating shield tunneling-induced surface settlement via multi-gene genetic programming [J]. Acta Geotechnica, 2023, 18(9): 4923-4940

[33] QinS, XuT, ChengZ-l, et al. . Analysis of spatiotemporal variations of excess pore water pressure during mechanized tunneling using genetic programming [J]. Acta Geotechnica, 2023, 18(4): 1721-1738

[34] Genetic programming: on the programming of computers by means of natural selection [M]. 55 Hayward St.: MIT Press, DOI: https://doi.org/10.5555/138936.

[35] PoliR, LangdonW B, McpheeN FA Field Guide to Genetic Programming [M], 2008, UK Ltd, Lulu Enterprises

[36] FarisH, ShetaA, ÖznergizE. MGP-CC: A hybrid multigene GP-Cuckoo search method for hot rolling manufacture process modelling [J]. Systems Science Control Engineering, 2016, 4(1): 39-49

[37] ChengZ-l, YangS-d, ZhaoL-s, et al. . Multivariate modeling of soil suction response to various rainfall by multi-gene genetic programing [J]. Acta Geotechnica, 2021, 16(11): 3601-3616

[38] ChengZ-l, ZhouW-h, GargA. Genetic programming model for estimating soil suction in shallow soil layers in the vicinity of a tree [J]. Engineering Geology, 2020, 268105506

[39] SonM, CordingE J. Estimation of building damage due to excavation-induced ground movements [J]. Journal of Geotechnical and Geoenvironmental Engineering, 2005, 131(2): 162-177

[40] MairR J. Tunnelling and geotechnics: New horizons [J]. Géotechnique, 2008, 58(9): 695-736

[41] MairR J. Tunnelling and deep excavations: Ground movements and their effects [C]. Proceedings of the 15th European Conference on Soil Mechanics and Geotechnical Engineering-Geotechnics of Hard Soils-Weak Rocks (Part 4), 2013, Amsterdam, the Netherlands, IOS Press: 39-70

[42] DimmockP S, MairR J. Effect of building stiffness on tunnelling-induced ground movement [J]. Tunnelling and Underground Space Technology, 2008, 23(4): 438-450

[43] LoganathanN, PoulosH G. Analytical prediction for tunneling-induced ground movements in clays [J]. Journal of Geotechnical and Geoenvironmental Engineering, 1998, 124(9): 846-856

[44] MarshallA M, FarrellR, KlarA, et al. . Tunnels in sands: The effect of size, depth and volume loss on greenfield displacements [J]. Géotechnique, 2012, 62(5): 385-399