Abstract
The construction of the engineering is often accompanied by the excavation of the foundation pit, which will lead to the ground settlement around the foundation pit. It poses a serious threat to the surrounding buildings and even causes casualties. This puts forward higher requirements for the prediction accuracy of ground settlement around the foundation pit. The existing prediction model of ground settlement around foundation pit only considers the single factor of excavation days. However, the ground settlement around the foundation pit is affected by many factors. Obviously, the prediction model considering single factor is not accurate enough. To accurately predict the settlement of the ground around the foundation pit, the sine algorithm (SA) is used to optimize regularized extreme learning machine (RELM), and a prediction model of ground settlement around foundation pit related to excavation elevation, groundwater level, number of supporting layers and geotechnical parameters is proposed. The model is applied to the prediction of ground settlement around a pipe jacking receiving foundation pit in Foshan City, Guangdong Province, China. SA-RELM models based on time series and multiple factors are established, respectively. The prediction results are compared to BP neural network, ELM and SA-ELM model. The results show that the relative error, MAE, MAPE and RMSE of SA-RELM model based on multiple factors are the smallest, which are close to 0. The comprehensive analysis shows that the SA-RELM model has high accuracy and generalization ability, and the SA-RELM model based on multiple factors has higher accuracy in the prediction of ground settlement around the foundation pit.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and materials
The data support the findings of this study are not publicly available due to privacy. If needed, the data are available from Han Yalu.
References
Abd El Aal AK, Rouaiguia A (2020) Determination of the geotechnical parameters of soils behavior for safe future urban development, Najran Area, Saudi Arabia: implications for settlements mitigation. Geotech Geol Eng 38(1):695–712. https://doi.org/10.1007/s10706-019-01058-x
Ayeneh AL, Amanipoor H, Battaleb-Looie S, Nia KB (2022) Evaluation of geological hazard of the subway (case study: Ahvaz subway, southwest of Iran). Int J Environ Sci Technol 19(4):3061–3074. https://doi.org/10.1007/s13762-021-03427-7
Bhadoria A, Marwaha S, Kamboj VK (2021) A solution to statistical and multidisciplinary design optimization problems using hGWO-SA algorithm. Neural Comput Appl 33:3799–3824. https://doi.org/10.1007/s00521-020-05229-3
Borthakur N, Dey AK (2020) Evaluation of group capacity of micropile in soft clayey soil from experimental analysis using SVM-based prediction model. Int J Geomech. https://doi.org/10.1061/(asce)gm.1943-5622.0001606
Calin N, Radu C, Bica I (2017) Dewatering system of a deep of excavation in urban area—Bucharest case study. In: Urban subsurface planning and management week (SUB-URBAN). Tech Univ Civil Engn Bucharest, Groundwater Engn Res Ctr, Bucharest, Romania, vol 3(209), pp 210–215. https://doi.org/10.1016/j.proeng.2017.11.149
Chegini SN, Bagheri A, Najafi F (2018) PSOSCALF: a new hybrid PSO based on sine cosine algorithm and levy flight for solving optimization problems. Appl Soft Comput 73:697–726. https://doi.org/10.1016/j.asoc.2018.09.019
Chen Z, Qin L, Zhao S, Chan THT, Nguyen A (2019) Toward efficacy of piecewise polynomial truncated singular value decomposition algorithm in moving force identification. Adv Struct Eng 22:2687–2698. https://doi.org/10.1177/1369433219849817
Cheng Z, Lu ZX, Dai F (2019) Research on HMCVT efficiency model based on the improved SA algorithm. Math Probl Eng. https://doi.org/10.1155/2019/2856908
Chin YT, Shen SL, Zhou AN, Chen J (2019) Foundation pit collapse on 8 June 2019 in Nanning, China: a brief report. Safety. https://doi.org/10.3390/safety5040068
Cui D, Zhu C, Li Q, Huang Q, Luo Q (2021) Research on deformation prediction of foundation pit based on PSO-GM-BP model. Adv Civil Eng. https://doi.org/10.1155/2021/8822929
Devarapalli R, Venkateswara Rao B, Dey B, Vinod Kumar K, Malik H, Márquez FPG (2021) An approach to solve OPF problems using a novel hybrid whale and sine cosine optimization algorithm. J Intell Fuzzy Syst 42:957–967. https://doi.org/10.3233/jifs-189763
Fan J, Yue W, Wu L, Zhang F, Cai H, Wang X, Lu X, Xiang Y (2018) Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agric for Meteorol 263:225–241. https://doi.org/10.1016/j.agrformet.2018.08.019
Gabis AB, Meraihi Y, Mirjalili S, Ramdane-Cherif A (2021) A comprehensive survey of sine cosine algorithm: variants and applications. Artif Intell Rev. https://doi.org/10.1007/s10462-021-10026-y
Gotman AL, Gotman YA (2019) Numerical analysis of the shorings of deep foundation pits with regard for the soil solidification. Soil Mech Found Eng 56(4):225–231. https://doi.org/10.1007/s11204-019-09595-6
Gupta U, Gupta D (2021) Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification. Int J Mach Learn Cybern 12:1311–1342. https://doi.org/10.1007/s13042-020-01235-y
Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48. https://doi.org/10.1016/j.neunet.2014.10.001
Jiang X, Lu Q, Chen X, Liu J, Li P (2021) Numerical analysis of deep foundation pit excavation process. IOP Conf Ser Earth Environ Sci 719:032051. https://doi.org/10.1088/1755-1315/719/3/032051
Khosravi M, Khosravi MH, Ghoreishi Najafabadi SH (2021) Determining the portion of dewatering-induced settlement in excavation pit projects. Int J Geotech Eng 15(5):563–573. https://doi.org/10.1080/19386362.2018.1467858
Kishi N, Sonoda K, Komuro M, Kawarai T (2021) Numerical simulation of the daikai station subway structure collapse due to sudden uplift during earthquake. J Eng Mech. https://doi.org/10.1061/(asce)em.1943-7889.0001895
Lancia M, Su H, Tian Y, Xu J, Andrews C, Lerner DN, Zheng C (2020) Hydrogeology of the Pearl River Delta, southern China. J Maps 16:388–395. https://doi.org/10.1080/17445647.2020.1761903
Lei G, Gong XN (2021) Analysis of lateral displacement law of deep foundation pit support in soft soil based on improved MSD method. Adv Civil Eng. https://doi.org/10.1155/2021/5550214
Li WD, Wu MH, Lin N (2016) Horizontal displacement prediction research of deep foundation pit based on the least square support vector machine. In: Wang J, Wang H, Cheng B, Chen T (eds) Proceedings of the 3rd international conference on wireless communication and sensor networks, pp 379–382
Li X, Liu X, Li CZ, Hu ZM, Shen GQ, Huang ZY (2019) Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement. Struct Health Monitor Int J 18:715–724. https://doi.org/10.1177/1475921718767935
Liu B, Xia SX, Meng FR, Zhou Y (2016) Manifold regularized extreme learning machine. Neural Comput Appl 27:255–269. https://doi.org/10.1007/s00521-014-1777-8
Liu CY, Wang Y, Hu XM, Han YL, Zhang XP, Du LZ (2021) Application of GA-BP neural network optimized by Grey Verhulst model around settlement prediction of foundation pit. Geofluids. https://doi.org/10.1155/2021/5595277
Lv Y, Liu TT, Ma J, Wei SD, Gao CL (2020) Study on settlement prediction model of deep foundation pit in sand and pebble strata based on grey theory and BP neural network. Arab J Geosci. https://doi.org/10.1007/s12517-020-06232-7
Mansouri H, Asghari-Kaljahi E (2019) Two dimensional finite element modeling of Tabriz metro underground station L2–S17 in the marly layers. Geomech Eng 19(4):315–327. https://doi.org/10.12989/gae.2019.19.4.315
Mirjalili S (2016) SCA: A Sine Cosine Algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022
Mirsayapov IT, Aysin NN (2019) Influence of a deep construction pit on a technical condition of surrounding buildings. In: International scientific-technical conference on geotechnical fundamentals and applications in construction—new materials, structures, technologies and calculations (GFAC), vol 2. St Petersburg State Univ Architecture & Civil Engn, Saint Petersburg, Russia, pp 6–8
Mitew-Czajewska M (2019) A study of displacements of structures in the vicinity of deep excavation. Arch Civil Mech Eng 19(2):547–556. https://doi.org/10.1016/j.acme.2018.11.010
Mwangi AD, Jianhua Z, Gang H, Kasomo RM, Innocent MM (2020) Ultimate pit limit optimization methods in open pit mines: a review. J Min Sci 56(4):588–602. https://doi.org/10.1134/s1062739120046885
Nayak DR, Dash R, Lu Z, Lu S, Majhi B (2018) SCA-RELM: a new regularized extreme learning machine based on sine cosine algorithm for automated detection of pathological brain. In: 27th IEEE international symposium on robot and human interactive communication (IEEE RO-MAN), Nanjing, People’s Republic of China, pp 764–769
Pujades E, De Simone S, Carrera J, Vazquez-Sune E, Jurado A (2017) Settlements around pumping wells: analysis of influential factors and a simple calculation procedure. J Hydrol 548:225–236. https://doi.org/10.1016/j.jhydrol.2017.02.040
Qiao SF, Tan JK, Zhang YG, Wan LJ, Zhang MF, Tang J, He Q (2021) Settlement prediction of foundation pit excavation based on the GWO-ELM model considering different states of influence. Adv Civil Eng. https://doi.org/10.1155/2021/8896210
Russo G, Nicotera MV, Autuori S (2016) San Pasquale station of line 6 in Naples: measurements and numerical analyses. In: 3rd international conference on transportation geotechnics (ICTG), Guimaraes, Portugal
Salhi S (2014) Handbook of metaheuristics, 2nd edition. J Oper Res Soc 65:320–320. https://doi.org/10.1057/jors.2013.151
Shariati M, Mafipour MS, Mehrabi P, Zandi Y, Dehghani D, Bahadori A, Shariati A, Nguyen Thoi T, Salih MNA, Poi-Ngian S (2019) Application of extreme learning machine (ELM) and genetic programming (GP) to design steel-concrete composite floor systems at elevated temperatures. Steel Compos Struct 33:319–332. https://doi.org/10.12989/scs.2019.33.3.319
Silitonga AS, Shamsuddin AH, Mahlia TMI, Milano J, Kusumo F, Siswantoro J, Dharma S, Sebayang AH, Masjuki HH, Ong HC (2020) Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization. Renewable Energy 146:1278–1291. https://doi.org/10.1016/j.renene.2019.07.065
Su Y, Wang XQ, Fu YY, Zheng XJ, You GS (2018) Research on surface settlement prediction based on the combination prediction model of S-shaped growth curves. Geosyst Eng 21:236–241. https://doi.org/10.1080/12269328.2017.1422994
Sun A, Zhao TY, Chen JF, Chang JF (2018) Comparative study: common ANN and LS-SVM exchange rate performance prediction. Chin J Electron 27:561–564. https://doi.org/10.1049/cje.2018.01.003
Tabaroei A, Sarfarazi V, Pouraminian M, Mohammadzadeh DS (2022) Evaluation behavior of a deep excavation by three-dimensional numerical modeling. Period Polytechn Civil Eng. https://doi.org/10.3311/PPci.20353
Tan Y, Jiang WZ, Rui HS, Lu Y, Wang DL (2020) Forensic geotechnical analyses on the 2009 building-overturning accident in Shanghai, China: beyond common recognitions. J Geotechn Geoenviron Eng. https://doi.org/10.1061/(asce)gt.1943-5606.0002264
Tang JX, Deng CW, Huang GB (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27:809–821. https://doi.org/10.1109/tnnls.2015.2424995
Wang M, Lu G (2021) A modified sine cosine algorithm for solving optimization problems. IEEE Access 9:27434–27450. https://doi.org/10.1109/ACCESS.2021.3058128
Wei H (2021) Influence of foundation pit excavation and precipitation on settlement of surrounding buildings. Adv Civil Eng. https://doi.org/10.1155/2021/6638868
Wei X, Wu C (2011) Holocene delta evolution and sequence stratigraphy of the Pearl River Delta in South China. Sci China Earth Sci 54:1523–1541. https://doi.org/10.1007/s11430-011-4238-6
Wu H, Bian K, Qiu J, Ye X, Chen C, Fu B (2019) The prediction of foundation pit based on genetic back propagation neural network. J Comput Methods Sci Eng 19:707–717. https://doi.org/10.3233/jcm-190017
Xu YS, Shen JS, Zhou A-N, Arulrajah A (2018) Geological and hydrogeological environment with geohazards during underground construction in Hangzhou: a review. Arab J Geosci. https://doi.org/10.1007/s12517-018-3894-7
Yildirim H, Revan Ozkale M (2021) LL-ELM: a regularized extreme learning machine based on L-1-norm and Liu estimator. Neural Comput Appl 33:10469–10484. https://doi.org/10.1007/s00521-021-05806-0
Yuan CF, Hu ZH, Zhu Z, Yuan ZJ, Fan YX, Guan H, Li L (2021) Numerical simulation of seepage and deformation in excavation of a deep foundation pit under water-rich fractured intrusive rock. Geofluids. https://doi.org/10.1155/2021/6628882
Zhan Y, Zhang J, Liu Q, Zheng P (2021) A new prediction method for surface settlement of deep foundation pit in pelagic division based on Elman-Markov model. Arab J Geosci. https://doi.org/10.1007/s12517-021-07603-4
Zhang GC, Xie N, Tang HM, Zhang L, Wu JP (2015) Survey and cause analyses of ground surface deformation near a foundation pit slope: a case study in the Three Gorges area, China. Nat Hazards 75:13–31. https://doi.org/10.1007/s11069-014-1261-x
Zhang S, Liu Z, Huang X, Xiao W (2018) A modified residual extreme learning machine algorithm and its application. IEEE Access 6:62215–62223. https://doi.org/10.1109/access.2018.2876360
Zhang C, Li JZ, He Y (2019) Application of optimized grey discrete Verhulst-BP neural network model in settlement prediction of foundation pit. Environ Earth Sci. https://doi.org/10.1007/s12665-019-8458-y
Zhang YG, Tang J, Liao RP, Zhang MF, Zhang Y, Wang XM, Su Z-y (2021) Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction. Stoch Environ Res Risk Assess 35:1273–1291. https://doi.org/10.1007/s00477-020-01920-y
Zhao HY, Huang FL, Li L, Zhang CY (2018) Optimization of wastewater anaerobic digestion treatment based on GA-BP neural network. Desalin Water Treat 122:30–35. https://doi.org/10.5004/dwt.2018.22596
Zhao Q, Zhu H, Zhang X, Liu Q, Qiu X, Li M (2021) Geomorphologic reconstruction of an uplift in a continental basin with a source-to-sink balance: an example from the Huizhou-Lufeng uplift, Pearl River Mouth Basin, South China sea. Mar Petrol Geol. https://doi.org/10.1016/j.marpetgeo.2021.104984
Zhou Y, Li S, Zhou C, Luo H (2019) Intelligent approach based on random forest for safety risk prediction of deep foundation pit in subway stations. J Comput Civ Eng. https://doi.org/10.1061/(asce)cp.1943-5487.0000796
Funding
This study was financially supported by the National Natural Science Foundation of China (Grant No. 41402246).
Author information
Authors and Affiliations
Contributions
All the authors contributed to the conception and design of the study. Material acquisition, data collection and analysis were carried out by DL, HY, and WY. The first draft of the manuscript was written by DL and HY. All the authors put forward suggestions on the revision of the previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Han, Y., Wang, Y., Liu, C. et al. Application of regularized ELM optimized by sine algorithm in prediction of ground settlement around foundation pit. Environ Earth Sci 81, 413 (2022). https://doi.org/10.1007/s12665-022-10542-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-022-10542-2