{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:52:33Z","timestamp":1762005153465,"version":"3.40.5"},"reference-count":65,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T00:00:00Z","timestamp":1580342400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2020,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests,<jats:italic>in situ<\/jats:italic>field CPT data, and soil\u2013pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter &amp; Bringen, and LPC\/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter &amp; Beringen model, and Bustamante &amp; Gianeselli for predicting driven pile ultimate bearing capacity.<\/jats:p>","DOI":"10.1017\/s0890060420000025","type":"journal-article","created":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T09:58:58Z","timestamp":1580378338000},"page":"114-126","source":"Crossref","is-referenced-by-count":7,"title":["Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data"],"prefix":"10.1017","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9337-0267","authenticated-orcid":false,"given":"Hooman","family":"Harandizadeh","sequence":"first","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2020,1,30]]},"reference":[{"key":"S0890060420000025_ref65","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060417000324"},{"key":"S0890060420000025_ref64","doi-asserted-by":"publisher","DOI":"10.1061\/40865(197)20"},{"key":"S0890060420000025_ref62","doi-asserted-by":"publisher","DOI":"10.1142\/9789814261302_0021"},{"key":"S0890060420000025_ref61","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060417000166"},{"key":"S0890060420000025_ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2017.06.012"},{"key":"S0890060420000025_ref56","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060415000335"},{"key":"S0890060420000025_ref55","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060413000176"},{"key":"S0890060420000025_ref53","doi-asserted-by":"publisher","DOI":"10.1017\/S089006041700018X"},{"key":"S0890060420000025_ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.compgeo.2009.04.003"},{"key":"S0890060420000025_ref49","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060403174033"},{"key":"S0890060420000025_ref48","doi-asserted-by":"publisher","DOI":"10.1080\/1064119X.2017.1355944"},{"key":"S0890060420000025_ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-014-0144-8"},{"key":"S0890060420000025_ref39","article-title":"Neuro-fuzzy GMDH systems to predict the scour pile groups due to waves","author":"Najafzadeh","year":"2013","journal-title":"Journal of Computing in Civil Engineering"},{"key":"S0890060420000025_ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-017-0542-x"},{"key":"S0890060420000025_ref33","first-page":"1","article-title":"Prediction of drained soil shear strength parameters of marine deposit from CPTu data using GMDH-type neural network","volume":"37","author":"Mola-Abasi","year":"2018","journal-title":"Marine Georesources and Geotechnology"},{"key":"S0890060420000025_ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-017-0545-7"},{"key":"S0890060420000025_ref27","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060415000396"},{"key":"S0890060420000025_ref26","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1061\/JSFEAQ.0000479","article-title":"Hyperbolic stress-strain response: cohesive soils","volume":"89","author":"Kondner","year":"1963","journal-title":"Journal of the Soil Mechanics and Foundations Division"},{"key":"S0890060420000025_ref25","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)1090-0241(1998)124:12(1177)"},{"key":"S0890060420000025_ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.enggeo.2008.09.006"},{"key":"S0890060420000025_ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.11.028"},{"key":"S0890060420000025_ref14","first-page":"1","article-title":"Application of an evolutionary-based approach in evaluating pile bearing capacity using CPT results","volume":"12","author":"Ebrahimian","year":"2016","journal-title":"Ships and Offshore Structures"},{"key":"S0890060420000025_ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MHS.1995.494215"},{"key":"S0890060420000025_ref11","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060401020054"},{"key":"S0890060420000025_ref9","article-title":"The use of the static soil penetrometer in Holland","volume":"18","author":"Begemann","year":"1963","journal-title":"New Zealand Engineering"},{"key":"S0890060420000025_ref51","doi-asserted-by":"publisher","DOI":"10.1007\/s10706-013-9662-2"},{"key":"S0890060420000025_ref31","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060417000221"},{"key":"S0890060420000025_ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-007-0002-0"},{"key":"S0890060420000025_ref21","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060413000504"},{"key":"S0890060420000025_ref3","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060416000482"},{"key":"S0890060420000025_ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2018.02.068"},{"key":"S0890060420000025_ref10","unstructured":"Bustamante, M and Gianeselli, L (1982) Pile bearing capacity prediction by means of static penetrometer CPT. In Proceedings of the 2-nd European symposium on penetration testing, pp. 493\u2013500."},{"key":"S0890060420000025_ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICABME.2015.7323238"},{"key":"S0890060420000025_ref19","first-page":"1","article-title":"Application of improved ANFIS approaches to estimate bearing capacity of piles","volume":"23","author":"Harandizadeh","year":"2018","journal-title":"Soft Computing"},{"key":"S0890060420000025_ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-012-1258-x"},{"key":"S0890060420000025_ref28","first-page":"1","article-title":"Predicting tunnel boring machine performance through a new model based on the group method of data handling","volume":"78","author":"Koopialipoor","year":"2018","journal-title":"Bulletin of Engineering Geology and the Environment"},{"key":"S0890060420000025_ref20","doi-asserted-by":"publisher","DOI":"10.1049\/el:19990177"},{"key":"S0890060420000025_ref63","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060413000632"},{"key":"S0890060420000025_ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2015.01.014"},{"key":"S0890060420000025_ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.scient.2011.11.017"},{"key":"S0890060420000025_ref45","first-page":"406","article-title":"Group method of data handling to predict scour depth around vertical piles under regular waves","volume":"20","author":"Najafzadeh","year":"2013","journal-title":"Scientia Iranica"},{"key":"S0890060420000025_ref30","first-page":"1","article-title":"Efficient hybrid group search optimizer for assembling printed circuit boards","author":"Lin","year":"2018","journal-title":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing"},{"key":"S0890060420000025_ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2618-8"},{"key":"S0890060420000025_ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2012.12.004"},{"volume-title":"Bearing Capacity of Piles From Cone Penetration Test Data","year":"1997","author":"Eslami","key":"S0890060420000025_ref16"},{"key":"S0890060420000025_ref12","doi-asserted-by":"publisher","DOI":"10.1080\/10641197909379805"},{"key":"S0890060420000025_ref6","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060417000269"},{"key":"S0890060420000025_ref57","unstructured":"Schmertmann, JH (1978) Guidelines for cone penetration test: performance and design. United States. Federal Highway Administration. Office of Research and Development https:\/\/rosap.ntl.bts.gov\/view\/dot\/958."},{"key":"S0890060420000025_ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s12665-015-4877-6"},{"key":"S0890060420000025_ref35","doi-asserted-by":"publisher","DOI":"10.15446\/esrj.v19n1.38712"},{"key":"S0890060420000025_ref18","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060407000327"},{"key":"S0890060420000025_ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.compgeo.2007.03.001"},{"key":"S0890060420000025_ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2015.05.016"},{"key":"S0890060420000025_ref60","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/TFUZZ.1993.390281","article-title":"A fuzzy logic approach to qualitative modelling","volume":"1","author":"Takagi","year":"1993","journal-title":"IEEE Trans. Fuzzy Systems"},{"key":"S0890060420000025_ref59","doi-asserted-by":"publisher","DOI":"10.1139\/T09-094"},{"key":"S0890060420000025_ref42","doi-asserted-by":"publisher","DOI":"10.1080\/1064119X.2018.1443355"},{"key":"S0890060420000025_ref24","unstructured":"Kennedy, J and Eberhart, R (1995) Perth, Australia. In Proc. IEEE International Conference on Neural Networks."},{"key":"S0890060420000025_ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10706-011-9413-1"},{"key":"S0890060420000025_ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compgeo.2013.08.001"},{"key":"S0890060420000025_ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.08.007"},{"key":"S0890060420000025_ref2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)1090-0241(1998)124:12(1177)"},{"key":"S0890060420000025_ref1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)1090-0241(2004)130:9(935)"},{"key":"S0890060420000025_ref23","doi-asserted-by":"publisher","DOI":"10.1017\/S089006041700052X"},{"key":"S0890060420000025_ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2011.08.009"},{"key":"S0890060420000025_ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-2072-z"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060420000025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T22:17:24Z","timestamp":1614205044000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060420000025\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,30]]},"references-count":65,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["S0890060420000025"],"URL":"https:\/\/doi.org\/10.1017\/s0890060420000025","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"type":"print","value":"0890-0604"},{"type":"electronic","value":"1469-1760"}],"subject":[],"published":{"date-parts":[[2020,1,30]]}}}