CLC number: TU473.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-05-27
Cited: 0
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Sheng-liang Lu, Ning Zhang, Shui-long Shen, Annan Zhou, Hu-zhong Li. A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data[J]. Journal of Zhejiang University Science A, 2020, 21(6): 496-508.
@article{title="A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data",
author="Sheng-liang Lu, Ning Zhang, Shui-long Shen, Annan Zhou, Hu-zhong Li",
journal="Journal of Zhejiang University Science A",
volume="21",
number="6",
pages="496-508",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1900544"
}
%0 Journal Article
%T A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data
%A Sheng-liang Lu
%A Ning Zhang
%A Shui-long Shen
%A Annan Zhou
%A Hu-zhong Li
%J Journal of Zhejiang University SCIENCE A
%V 21
%N 6
%P 496-508
%@ 1673-565X
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1900544
TY - JOUR
T1 - A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data
A1 - Sheng-liang Lu
A1 - Ning Zhang
A1 - Shui-long Shen
A1 - Annan Zhou
A1 - Hu-zhong Li
J0 - Journal of Zhejiang University Science A
VL - 21
IS - 6
SP - 496
EP - 508
%@ 1673-565X
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1900544
Abstract: This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground, independent of theoretical hypotheses and engineering experience. A series of field tests was first performed to investigate the characteristics of the shaft resistance of cast-in-site piles on reclaimed ground. Then, an intelligent approach based on the long short term memory deep-learning technique was proposed to calculate the shaft resistance of the cast-in-site pile. The proposed method allows accurate estimation of the shaft resistance of cast-in-site piles, not only under the ultimate load but also under the working load. Comparisons with empirical methods confirmed the effectiveness of the proposed method for the shaft resistance estimation of cast-in-site piles on reclaimed ground in offshore areas.
[1]Abu Kiefa MA, 1998. General regression neural networks for driven piles in cohesionless soils. Journal of Geotechnical and Geoenvironmental Engineering, 124(12):1177-1185.
[2]Alkroosh I, Nikraz H, 2012. Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Engineering Applications of Artificial Intelligence, 25(3):618-627.
[3]Ardalan H, Eslami A, Nariman-Zadeh N, 2009. Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Computers and Geotechnics, 36(4):616-625.
[4]Atangana Njock PG, Shen SL, Zhou AN, et al., 2020. Evaluation of soil liquefaction using AI technology incorporating a coupled ENN/t-SNE model. Soil Dynamics and Earthquake Engineering, 130:105988.
[5]Baziar MH, Kashkooli A, Saeedi-Azizkandi A, 2012. Prediction of pile shaft resistance using Cone Penetration Tests (CPTs). Computers and Geotechnics, 45:74-82.
[6]Behrmann JH, Meissl S, 2012. Submarine landslides, Gulf of Mexico continental slope: insights into transport processes from fabrics and geotechnical data. In: Yamada Y, Kawamura K, Ikehara K, et al. (Eds.), Submarine Mass Movements and Their Consequences. Springer, Dordrecht, the Netherland, p.463-472.
[7]Bustamante M, Gianeselli L, 1982. Pile bearing capacity prediction by means of static penetrometer CPT. Proceedings of the 2nd European Symposium Penetration Testing, p.493-500.
[8]Cai YQ, Xie ZW, Wang J, et al., 2018. New approach of vacuum preloading with booster prefabricated vertical drains (PVDs) to improve deep marine clay strata. Canadian Geotechnical Journal, 55(10):1359-1371.
[9]Chai JC, Shen JSL, Liu MD, et al., 2018. Predicting the performance of embankments on PVD-improved subsoils. Computers and Geotechnics, 93:222-231.
[10]Chan WT, Chow YK, Liu LF, 1995. Neural network: an alternative to pile driving formulas. Computers and Geotechnics, 17(2):135-156.
[11]Dauphin YN, de Vries H, Bengio Y, 2015. Equilibrated adaptive learning rates for non-convex optimization. Advances in Neural Information Processing Systems, 35(3):1504-1512.
[12]de Kuiter J, Beringen FL, 1979. Pile foundations for large North Sea structures. Marine Geotechnology, 3(3):267-314.
[13]Duchi J, Hazan E, Singer Y, 2011. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:257-269.
[14]Elbaz K, Shen SL, Sun WJ, et al., 2020. Prediction model of shield performance during tunneling via incorporating improved particle swarm optimization into ANFIS. IEEE Access, 8(1):39659-39671.
[15]Fang K, Pan M, Shen CP, 2019. The value of SMAP for long-term soil moisture estimation with the help of deep learning. IEEE Transactions on Geoscience and Remote Sensing, 57(4):2221-2233.
[16]Formela K, Wąsowicz D, Formela M, et al., 2015. Curing characteristics, mechanical and thermal properties of reclaimed ground tire rubber cured with various vulcanizing systems. Iranian Polymer Journal, 24(4):289-297.
[17]Gers FA, Schraudolph NN, Schmidhuber J, 2003. Learning precise timing with LSTM recurrent networks. The Journal of Machine Learning Research, 3:115-143.
[18]Ghorbani B, Sadrossadat E, Bazaz JB, et al., 2018. Numerical ANFIS-based formulation for prediction of the ultimate axial load bearing capacity of piles through CPT data. Geotechnical and Geological Engineering, 36(4):2057-2076.
[19]Goh ATC, 1996. Pile driving records reanalyzed using neural networks. Journal of Geotechnical Engineering, 122(6):492-495.
[20]Goh ATC, Kulhawy FH, Chua CG, 2005. Bayesian neural network analysis of undrained side resistance of drilled shafts. Journal of Geotechnical and Geoenvironmental Engineering, 131(1):84-93.
[21]Ji F, Ding JW, Hong ZS, et al., 2011. Experimental study on dewatering dredged clay with ventilating vacuum method. Advanced Materials Research, 261-263:1650-1654.
[22]Jin YF, Yin ZY, Wu ZX, et al., 2018a. Identifying parameters of easily crushable sand and application to offshore pile driving. Ocean Engineering, 154:416-429.
[23]Jin YF, Yin ZY, Wu ZX, et al., 2018b. Numerical modeling of pile penetration in silica sands considering the effect of grain breakage. Finite Elements in Analysis and Design, 144:15-29.
[24]Karlsrud K, Clausen CJF, Aas PM, 2005. Bearing capacity of driven piles in clay, the NGI approach. Proceedings of International Symposium on Frontiers in Offshore Geotechnics, p.775-782.
[25]Kingma DP, Ba J, 2014. Adam: a method for stochastic optimization. arXiv:1412.6980.
[26]Kong LG, Fan JY, Liu JW, et al., 2019. Group effect in piles under eccentric lateral loading in sand. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 20(4):243-257.
[27]Lee IM, Lee JH, 1996. Prediction of pile bearing capacity using artificial neural networks. Computers and Geotechnics, 18(3):189-200.
[28]Li AG, Tham LG, Wen JP, et al., 2014. Case study of ground improvement to Qianhai reclamation area, Qianhai Bay, Shenzhen. Proceedings of Geo-Shanghai, p.231-240.
[29]Li S, Yue ZQ, Tham LG, et al., 2005. Slope failure in underconsolidated soft soils during the development of a port in Tianjin, China. Part 2: analytical study. Canadian Geotechnical Journal, 42(1):166-183.
[30]Ling Z, Wang WD, Wu JB, et al., 2018. Shaft resistance of pre-bored precast piles in Shanghai clay. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, 172(3):228-242.
[31]Liu YX, Jiang SP, Zhao YM, 2004. Testing study on mechanical characteristics of natural underconsolidated soils. Chinese Journal of Rock Mechanics and Engineering, 23(S1):4409-4413 (in Chinese).
[32]Lyu HM, Shen SL, Zhou AN, et al., 2019a. Flood risk assessment of metro systems in a subsiding environment using the interval FAHP-FCA approach. Sustainable Cities and Society, 50:101682.
[33]Lyu HM, Shen SL, Yang J, et al., 2019b. Inundation analysis of metro systems with the storm water management model incorporated into a geographical information system: a case study in Shanghai. Hydrology and Earth System Sciences, 23(10):4293-4307.
[34]Lyu HM, Shen SL, Yang J, et al., 2020a. Risk assessment of earthquake-triggered geohazards surrounding Wenchuan, China. Natural Hazards Review, 21(3):05020003.
[35]Lyu HM, Shen SL, Zhou AN, et al., 2020b. Risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP. Science of the Total Environment, 717:135310.
[36]Lyu HM, Sun WJ, Shen SL, et al., 2020c. Risk assessment using a new consulting process in fuzzy AHP. Journal of Construction Engineering and Management, 146(3):04019112.
[37]Ma HP, Chen ZY, Yu S, 2014. Correlations of soil shear strength with specific penetration resistance of CPT in shanghai area. Rock and Soil Mechanics, 35(2):536-542 (in Chinese).
[38]Mayne PW, Kulhawy FH, 1982. K0-OCR relationships in soil. Journal of the Geotechnical Engineering Division, 108(6):851-872.
[39]MOHURD (Ministry of Housing and Urban-Rural Development of the People’s Republic of China), 2014. Technical Code for Testing of Building Foundation Piles, JGJ106-2014. National Standards of the People’s Republic of China (in Chinese).
[40]Nonaka T, Yamada S, Noda T, 2017. Soil-water coupled analysis of pore water pressure dissipation in performance design-examinations of effectiveness in reclaimed ground. Geotechnical Engineering, 48(4):19-31.
[41]O’Neill MW, Reese LC, 1999. Drilled Shafts: Construction Procedures and Design Methods. No. FHWA-IF-99-025, US Department of Transportation, Federal Highway Administration, Washington DC, USA.
[42]Park HI, Cho CW, 2010. Neural network model for predicting the resistance of driven piles. Marine Georesources & Geotechnology, 28(4):324-344.
[43]Randolph MF, Murphy BS, 1985. Shaft capacity of driven piles in clay. Proceedings of the 17th Annual Offshore Technology Conference, p.371-378.
[44]Sak H, Senior AW, Beaufays F, 2014. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association, p.338-342.
[45]Santos Jr OJ, Celestino TB, 2008. Artificial neural networks analysis of São Paulo subway tunnel settlement data. Tunnelling and Underground Space Technology, 23(5):481-491.
[46]Sarir P, Shen SL, Wang ZF, et al., 2019. Optimum model for bearing capacity of concrete-steel columns with AI technology via incorporating the algorithms of IWO and ABC. Engineering with Computers, in press.
[47]Shahin MA, 2010. Intelligent computing for modeling axial capacity of pile foundations. Canadian Geotechnical Journal, 47(2):230-243.
[48]Shen MF, Martin JR, Ku CS, et al., 2018. A case study of the effect of dynamic compaction on liquefaction of reclaimed ground. Engineering Geology, 240:48-61.
[49]Shen SL, Chai JC, Hong ZS, et al., 2005. Analysis of field performance of embankments on soft clay deposit with and without PVD-improvement. Geotextiles and Geomembranes, 23(6):463-485.
[50]Shen SL, Wu YX, Misra A, 2017. Calculation of head difference at two sides of a cut-off barrier during excavation dewatering. Computers and Geotechnics, 91:192-202.
[51]Shi JS, Ortigao JAR, Bai JL, 1998. Modular neural networks for predicting settlements during tunneling. Journal of Geotechnical and Geoenvironmental Engineering, 124(5):389-395.
[52]Suwansawat S, Einstein HH, 2006. Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunnelling and Underground Space Technology, 21(2):133-150.
[53]Teh CI, Wong KS, Goh ATC, et al., 1997. Prediction of pile capacity using neural networks. Journal of Computing in Civil Engineering, 11(2):129-138.
[54]Wang K, Sun WC, 2018. A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning. Computer Methods in Applied Mechanics and Engineering, 334:337-380.
[55]Wang XW, Yang TL, Xu YS, et al., 2019. Evaluation of optimized depth of waterproof curtain to mitigate negative impacts during dewatering. Journal of Hydrology, 577: 123969.
[56]Wang ZF, Shen JS, Cheng WC, 2018. Simple method to predict ground displacements caused by installing horizontal jet-grouting columns. Mathematical Problems in Engineering, 2018:1897394.
[57]Wang ZF, Shen SL, Modoni G, 2019. Enhancing discharge of spoil to mitigate disturbance induced by horizontal jet grouting in clayey soil: theoretical model and application. Computers and Geotechnics, 111:222-228.
[58]Wu YX, Shen SL, Lyu HM, et al., 2020. Analyses of leakage effect of waterproof curtain during excavation dewatering. Journal of Hydrology, 583:124582.
[59]Xu YS, Yan XX, Shen SL, et al., 2019. Experimental investigation on the blocking of groundwater seepage from a waterproof curtain during pumped dewatering in an excavation. Hydrogeology Journal, 27(7):2659-2672.
[60]Yan SW, Chu J, 2005. Soil improvement for a storage yard using the combined vacuum and fill preloading method. Canadian Geotechnical Journal, 42(4):1094-1104.
[61]Yao YP, Yamamoto H, Wang ND, 2008a. Constitutive model considering sand crushing. Soils and Foundations, 48(4):603-608.
[62]Yao YP, Sun DA, Matsuoka H, 2008b. A unified constitutive model for both clay and sand with hardening parameter independent on stress path. Computers and Geotechnics, 35(2):210-222.
[63]Yin ZY, Jin YF, Shen SL, et al., 2017. An efficient optimization method for identifying parameters of soft structured clay by an enhanced genetic algorithm and elastic-viscoplastic model. Acta Geotechnica, 12(4):849-867.
[64]Yin ZY, Wu ZY, Hicher PY, 2018a. Modeling monotonic and cyclic behavior of granular materials by exponential constitutive function. Journal of Engineering Mechanics, 144(4):04018014.
[65]Yin ZY, Jin YF, Shen SL, et al., 2018b. Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement. International Journal for Numerical and Analytical Methods in Geomechanics, 42(1):70-94.
[66]Zhang N, Shen SL, Wu HN, et al., 2015. Evaluation of effect of basal geotextile reinforcement under embankment loading on soft marine deposits. Geotextiles and Geomembranes, 43(6):506-514.
[67]Zhang N, Shen SL, Zhou AN, et al., 2019. Investigation on performance of neural networks using quadratic relative error cost function. IEEE Access, 7:106642-106652.
[68]Zhou AN, Huang RQ, Sheng DC, 2016. Capillary water retention curve and shear strength of unsaturated soils. Canadian Geotechnical Journal, 53(6):974-987.
[69]Zhou AN, Wu SS, Li J, et al., 2018. Including degree of capillary saturation into constitutive modelling of unsaturated soils. Computers and Geotechnics, 95:82-98.
[70]Zhou JJ, Wang KH, Gong XN, et al., 2013. Bearing capacity and load transfer mechanism of a static drill rooted nodular pile in soft soil areas. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 14(10):705-719.
[71]Zhou M, Liu HL, Hossain MS, et al., 2016. Numerical simulation of plug formation during casing installation of cast-in-place concrete pipe (PCC) piles. Canadian Geotechnical Journal, 53(7):1093-1109.
[72]Zhou XH, Shen SL, Xu YS, et al., 2019. Analysis of production safety in the construction industry of China in 2018. Sustainability, 11(17):4537.
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