{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T15:30:40Z","timestamp":1777649440408,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121M736"],"award-info":[{"award-number":["121M736"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s00521-023-09053-3","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T20:01:51Z","timestamp":1697486511000},"page":"697-715","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Re-evaluation of machine learning models for predicting ultimate bearing capacity of piles through SHAP and Joint Shapley methods"],"prefix":"10.1007","volume":"36","author":[{"given":"S.","family":"Karaka\u015f","sequence":"first","affiliation":[]},{"given":"G.","family":"Ta\u015fk\u0131n","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7632-2303","authenticated-orcid":false,"given":"M. B. C.","family":"\u00dclker","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"issue":"5","key":"9053_CR1","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1007\/s10706-011-9413-1","volume":"29","author":"I Alkroosh","year":"2011","unstructured":"Alkroosh I, Nikraz H (2011) Correlation of pile axial capacity and CPT data using gene expression programming. Geotech Geol Eng 29(5):725\u2013748. https:\/\/doi.org\/10.1007\/s10706-011-9413-1","journal-title":"Geotech Geol Eng"},{"issue":"3","key":"9053_CR2","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1016\/j.engappai.2011.08.009","volume":"25","author":"I Alkroosh","year":"2012","unstructured":"Alkroosh I, Nikraz H (2012) Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Eng Appl Artif Intell 25(3):618\u2013627. https:\/\/doi.org\/10.1016\/j.engappai.2011.08.009","journal-title":"Eng Appl Artif Intell"},{"issue":"5","key":"9053_CR3","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.jrmge.2015.06.011","volume":"7","author":"IS Alkroosh","year":"2015","unstructured":"Alkroosh IS, Bahadori M, Nikraz H, Bahadori A (2015) Regressive approach for predicting bearing capacity of bored piles from cone penetration test data. J Rock Mech Geotech Eng 7(5):214. https:\/\/doi.org\/10.1016\/j.jrmge.2015.06.011","journal-title":"J Rock Mech Geotech Eng"},{"key":"9053_CR4","unstructured":"Altinok E (2021) Data-driven modeling of ultimate load capacity of closedand open-ended piles using machine learning. Master\u2019s thesis, Istanbul Technical University, Turkey"},{"issue":"4","key":"9053_CR5","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.compgeo.2008.09.003","volume":"36","author":"H Ardalan","year":"2009","unstructured":"Ardalan H, Eslami A, Nariman-Zadeh N (2009) Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Comput Geotech 36(4):616\u2013625. https:\/\/doi.org\/10.1016\/j.compgeo.2008.09.003","journal-title":"Comput Geotech"},{"issue":"2","key":"9053_CR6","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s00521-015-2072","volume":"28","author":"D Armaghani","year":"2017","unstructured":"Armaghani D, Shoib RSNSBR, Faizi K, Rashid ASA (2017) Developing a hybrid PSO\u2013ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28(2):391\u2013405. https:\/\/doi.org\/10.1007\/s00521-015-2072","journal-title":"Neural Comput Appl"},{"key":"9053_CR7","doi-asserted-by":"publisher","unstructured":"Asghari V, Leung AY, Hsu MS (2020) Deep neural networks for prediction of undrained shear strength of clays. https:\/\/doi.org\/10.3850\/978-981-11-2725-0-ms2-4-cd","DOI":"10.3850\/978-981-11-2725-0-ms2-4-cd"},{"key":"9053_CR8","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Lear. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Lear"},{"key":"9053_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2021.100245","author":"D Chakraborty","year":"2021","unstructured":"Chakraborty D, Awolusi I, Gutierrez L (2021) An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete. Res Eng. https:\/\/doi.org\/10.1016\/j.rineng.2021.100245","journal-title":"Res Eng"},{"key":"9053_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00752-x","author":"W Chen","year":"2020","unstructured":"Chen W, Sarir P, Bui XN, Nguyen H, Tahir MM, Jahed Armaghani D (2020) Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00752-x","journal-title":"Eng Comput"},{"issue":"7","key":"9053_CR11","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1080\/17445302.2015.1116243","volume":"12","author":"B Ebrahimian","year":"2017","unstructured":"Ebrahimian B, Movahed V (2017) Application of an evolutionary-based approach in evaluating pile bearing capacity using CPT results. Ships Offshore Struct 12(7):214. https:\/\/doi.org\/10.1080\/17445302.2015.1116243","journal-title":"Ships Offshore Struct"},{"key":"9053_CR12","unstructured":"Friedman JH (1999), Greedy function approximation: a gradient boosting machine. Technical Report, Department of Statistics, Stanford University."},{"issue":"4","key":"9053_CR13","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10706-018-0445-7","volume":"36","author":"B Ghorbani","year":"2018","unstructured":"Ghorbani B, Sadrossadat E, Bolouri Bazaz J, Rahimzadeh Oskooei P (2018) Numerical ANFIS-based formulation for prediction of the ultimate axial load bearing capacity of piles through CPT data. Geotech Geol Eng 36(4):245. https:\/\/doi.org\/10.1007\/s10706-018-0445-7","journal-title":"Geotech Geol Eng"},{"key":"9053_CR14","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s00366-019-00849-3","volume":"37","author":"H Harandizadeh","year":"2021","unstructured":"Harandizadeh H, Jahed Armaghani D, Khari M (2021) A new development of ANFIS\u2013GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets. Eng Comput 37:685\u2013700. https:\/\/doi.org\/10.1007\/s00366-019-00849-3","journal-title":"Eng Comput"},{"key":"9053_CR15","doi-asserted-by":"publisher","unstructured":"Harris C, Pymar R, Rowat C (2022). Joint shapley values: a measure of joint feature importance. https:\/\/doi.org\/10.48550\/arXiv.2107.11357","DOI":"10.48550\/arXiv.2107.11357"},{"key":"9053_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/J.JRMGE.2022.01.002","author":"KKPM Kannangara","year":"2022","unstructured":"Kannangara KKPM, Zhou W, Ding Z, Hng Z (2022) Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method. J Rock Mech Geotech Eng. https:\/\/doi.org\/10.1016\/J.JRMGE.2022.01.002","journal-title":"J Rock Mech Geotech Eng"},{"issue":"2","key":"9053_CR17","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/s10706-019-01085-8","volume":"38","author":"N Kardani","year":"2020","unstructured":"Kardani N, Zhou A, Nazem M, Shen SL (2020) Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotech Geol Eng 38(2):254. https:\/\/doi.org\/10.1007\/s10706-019-01085-8","journal-title":"Geotech Geol Eng"},{"key":"9053_CR18","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Liu TY (2017) LightGBM: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems i\u00e7inde."},{"issue":"12","key":"9053_CR19","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1061\/(ASCE)1090-0241(1998)124:12(1177)","volume":"124","author":"MAA Kiefa","year":"1998","unstructured":"Kiefa MAA (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Eng 124(12):1177\u20131185. https:\/\/doi.org\/10.1061\/(ASCE)1090-0241(1998)124:12(1177)","journal-title":"J Geotech Geoenviron Eng"},{"key":"9053_CR20","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.compgeo.2013.08.001","volume":"55","author":"A Kordjazi","year":"2014","unstructured":"Kordjazi A, Pooya Nejad F, Jaksa MB (2014) Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data. Comput Geotech 55:91\u2013102. https:\/\/doi.org\/10.1016\/j.compgeo.2013.08.001","journal-title":"Comput Geotech"},{"issue":"10","key":"9053_CR21","doi-asserted-by":"publisher","first-page":"21","DOI":"10.5762\/kais.2021.22.10.268","volume":"22","author":"B-S Lee","year":"2021","unstructured":"Lee B-S (2021) A study of machine learning models to estimate a pile load capacity. J Korea Academia-Industrial Coop Soc 22(10):21. https:\/\/doi.org\/10.5762\/kais.2021.22.10.268","journal-title":"J Korea Academia-Industrial Coop Soc"},{"issue":"3","key":"9053_CR22","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/0266-352X(95)00027-8","volume":"18","author":"IM Lee","year":"1996","unstructured":"Lee IM, Lee JH (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18(3):189\u2013200. https:\/\/doi.org\/10.1016\/0266-352X(95)00027-8","journal-title":"Comput Geotech"},{"key":"9053_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cemconcomp.2021.104295","author":"M Liang","year":"2022","unstructured":"Liang M, Chang Z, Wan Z, Gan Y, Schlangen E, \u0160avija B (2022) Interpretable ensemble-machine-learning models for predicting creep behavior of concrete. Cement Concrete Compos. https:\/\/doi.org\/10.1016\/j.cemconcomp.2021.104295","journal-title":"Cement Concrete Compos"},{"key":"9053_CR24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1705.07874","author":"SM Lundberg","year":"2017","unstructured":"Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv Neural Inform Process Syst. https:\/\/doi.org\/10.48550\/arXiv.1705.07874","journal-title":"Adv Neural Inform Process Syst"},{"key":"9053_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00858-2","author":"Z Luo","year":"2021","unstructured":"Luo Z, Hasanipanah M, Bakhshandeh Amnieh H, Brindhadevi K, Tahir MM (2021) GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00858-2","journal-title":"Eng Comput"},{"key":"9053_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ringps.2021.100034","author":"H Nasiri","year":"2021","unstructured":"Nasiri H, Homafar A, Chelgani SC (2021) Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using an explainable artificial intelligence. Res Geophys Sci. https:\/\/doi.org\/10.1016\/j.ringps.2021.100034","journal-title":"Res Geophys Sci"},{"issue":"3","key":"9053_CR27","doi-asserted-by":"publisher","first-page":"265","DOI":"10.15625\/0866-7187\/42\/3\/15182","volume":"42","author":"TA Nguyen","year":"2020","unstructured":"Nguyen T-A, Ly H-B, Jaafari A, Pham TB (2020) Estimation of friction capacity of driven piles in clay using artificial neural network. Vietnam J Earth Sci 42(3):265\u2013275. https:\/\/doi.org\/10.15625\/0866-7187\/42\/3\/15182","journal-title":"Vietnam J Earth Sci"},{"key":"9053_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compgeo.2010.07.012","author":"M Pal","year":"2010","unstructured":"Pal M, Deswal S (2010) Modelling pile capacity using Gaussian process regression. Comput Geotech. https:\/\/doi.org\/10.1016\/j.compgeo.2010.07.012","journal-title":"Comput Geotech"},{"key":"9053_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0243030","author":"TA Pham","year":"2020","unstructured":"Pham TA, Tran VQ, Vu HLT, Ly HB (2020) Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.0243030","journal-title":"PLoS ONE"},{"key":"9053_CR30","doi-asserted-by":"publisher","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, ve Gulin A (2018) Catboost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems i\u00e7inde (C. 2018-December). https:\/\/doi.org\/10.48550\/arXiv.1706.09516","DOI":"10.48550\/arXiv.1706.09516"},{"key":"9053_CR31","doi-asserted-by":"publisher","unstructured":"Sakib M, Inan K, Rahman I (2022) Integration of explainable artificial intelligence to identify significant landslide causal factors for extreme gradient boosting based landslide susceptibility mapping with improved feature selection. https:\/\/doi.org\/10.48550\/arXiv.2201.03225","DOI":"10.48550\/arXiv.2201.03225"},{"key":"9053_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compgeo.2007.06.014","author":"P Samui","year":"2008","unstructured":"Samui P (2008) Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Geotech. https:\/\/doi.org\/10.1016\/j.compgeo.2007.06.014","journal-title":"Comput Geotech"},{"key":"9053_CR33","doi-asserted-by":"publisher","DOI":"10.1080\/00036846.2021.1934389","author":"C Schalck","year":"2021","unstructured":"Schalck C, Yankol-Schalck M (2021) Predicting French SME failures: new evidence from machine learning techniques. Appl Econ. https:\/\/doi.org\/10.1080\/00036846.2021.1934389","journal-title":"Appl Econ"},{"key":"9053_CR34","doi-asserted-by":"publisher","unstructured":"Shahin MA (2013) Artificial intelligence in geotechnical engineering. Metaheuristics in Water, Geotechnical and Transport Engineering i\u00e7inde (ss. 169\u2013204). Elsevier. https:\/\/doi.org\/10.1016\/B978-0-12-398296-4.00008-8","DOI":"10.1016\/B978-0-12-398296-4.00008-8"},{"issue":"2","key":"9053_CR35","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1061\/(ASCE)0887-3801(1997)11:2(129)","volume":"11","author":"CI Teh","year":"1997","unstructured":"Teh CI, Wong KS, Goh ATC, Jaritngam S (1997) Prediction of pile capacity using neural networks. J Comput Civ Eng 11(2):129\u2013138. https:\/\/doi.org\/10.1061\/(ASCE)0887-3801(1997)11:2(129)","journal-title":"J Comput Civ Eng"},{"key":"9053_CR36","doi-asserted-by":"publisher","DOI":"10.3389\/feart.2021.807317","author":"L Wang","year":"2021","unstructured":"Wang L, Wu J, Zhang W, Wang L, ve Cui, W. (2021) efficient seismic stability analysis of embankment slopes subjected to water level changes using gradient boosting algorithms. Front Earth Sci. https:\/\/doi.org\/10.3389\/feart.2021.807317","journal-title":"Front Earth Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09053-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09053-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09053-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T07:14:59Z","timestamp":1704438899000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09053-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,16]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["9053"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09053-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,16]]},"assertion":[{"value":"27 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors have no known conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}