{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T23:10:13Z","timestamp":1783725013447,"version":"3.55.0"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s10462-021-10065-5","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T22:17:17Z","timestamp":1630707437000},"page":"2313-2350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8171-6403","authenticated-orcid":false,"given":"Danial Jahed","family":"Armaghani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hooman","family":"Harandizadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4084-485X","authenticated-orcid":false,"given":"Ehsan","family":"Momeni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harnedi","family":"Maizir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"10065_CR1","doi-asserted-by":"publisher","first-page":"8087","DOI":"10.1007\/s00521-018-3661-4","volume":"31","author":"R Acharyya","year":"2019","unstructured":"Acharyya R, Dey A (2019) Assessment of bearing capacity for strip footing located near sloping surface considering ANN model. Neural Comput Appl 31:8087\u20138100","journal-title":"Neural Comput Appl"},{"key":"10065_CR2","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1016\/j.asoc.2012.10.009","volume":"13","author":"MA Ahmadi","year":"2013","unstructured":"Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13:1085\u20131098","journal-title":"Appl Soft Comput"},{"key":"10065_CR3","doi-asserted-by":"publisher","first-page":"2863","DOI":"10.1007\/s10462-020-09915-5","volume":"54","author":"M Alizamir","year":"2021","unstructured":"Alizamir M, Kim S, Zounemat-Kermani M et al (2021) Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model. Artif Intell Rev 54:2863\u20132890","journal-title":"Artif Intell Rev"},{"key":"10065_CR4","doi-asserted-by":"publisher","first-page":"106167","DOI":"10.1016\/j.cemconres.2020.106167","volume":"136","author":"M Apostolopoulou","year":"2020","unstructured":"Apostolopoulou M, Asteris PG, Armaghani DJ et al (2020) Mapping and holistic design of natural hydraulic lime mortars. Cem Concr Res 136:106167","journal-title":"Cem Concr Res"},{"key":"10065_CR94","doi-asserted-by":"publisher","first-page":"5383","DOI":"10.1007\/s12517-013-1174-0","volume":"7","author":"DJ Armaghani","year":"2014","unstructured":"Armaghani DJ, Hajihassani M, Mohamad ET, Marto A,  Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383\u20135396","journal-title":"Arab J Geosci"},{"key":"10065_CR6","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s00366-015-0408-z","volume":"32","author":"DJ Armaghani","year":"2016","unstructured":"Armaghani DJ, Hasanipanah M, Mohamad ET (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32:155\u2013171. https:\/\/doi.org\/10.1007\/s00366-015-0408-z","journal-title":"Eng Comput"},{"key":"10065_CR7","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s00521-015-2072-z","volume":"28","author":"DJ Armaghani","year":"2017","unstructured":"Armaghani DJ, Bin RRSNS, Faizi K, Rashid ASA (2017) Developing a hybrid PSO\u2013ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391\u2013405","journal-title":"Neural Comput Appl"},{"key":"10065_CR5","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.3390\/app10061904","volume":"10","author":"DJ Armaghani","year":"2020","unstructured":"Armaghani DJ, Asteris PG, Fatemi SA et al (2020) On the use of neuro-swarm system to forecast the pile settlement. Appl Sci 10:1904","journal-title":"Appl Sci"},{"key":"10065_CR95","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-021-01380-0","author":"DJ Armaghani","year":"2021","unstructured":"Armaghani DJ, Harandizadeh H,  Momeni E (2021) Load carrying capacity assessment of thin-walled foundations: an ANFIS\u2013PNN model optimized by genetic algorithm. Eng Comput.\u00a0https:\/\/doi.org\/10.1007\/s00366-021-01380-0","journal-title":"Eng Comput"},{"key":"10065_CR8","first-page":"137","volume":"24","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Ashrafian A, Rezaie-Balf M (2019) Prediction of the compressive strength of self-compacting concrete using surrogate models. Comput Concr 24:137\u2013150","journal-title":"Comput Concr"},{"key":"10065_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.32604\/CMES.2020.013280","volume":"124","author":"PG Asteris","year":"2020","unstructured":"Asteris PG, Douvika MG, Karamani CA et al (2020) A novel heuristic algorithm for the modeling and risk assessment of the covid-19 pandemic phenomenon. C Comput Model Eng Sci 124:1\u201314. https:\/\/doi.org\/10.32604\/CMES.2020.013280","journal-title":"C Comput Model Eng Sci"},{"key":"10065_CR11","volume-title":"Standard test method for high strain testing of piles","author":"ASTM D 4945\u201313","year":"2013","unstructured":"ASTM D 4945\u201313 (2013) Standard test method for high strain testing of piles. American Society for Testing and Materials, West Conshohocken"},{"key":"10065_CR12","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, 2007. CEC 2007. IEEE, pp 4661\u20134667","DOI":"10.1109\/CEC.2007.4425083"},{"key":"10065_CR13","volume-title":"Neurofuzzy adaptive modelling and control","author":"M Brown","year":"1994","unstructured":"Brown M, Harris CJ (1994) Neurofuzzy adaptive modelling and control. Prentice Hall, Hoboken"},{"key":"10065_CR14","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.cageo.2011.10.031","volume":"45","author":"DT Bui","year":"2012","unstructured":"Bui DT, Pradhan B, Lofman O et al (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199\u2013211","journal-title":"Comput Geosci"},{"key":"10065_CR15","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s12665-012-1783-z","volume":"68","author":"N Ceryan","year":"2013","unstructured":"Ceryan N, Okkan U, Kesimal A (2013) Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environ Earth Sci 68:807\u2013819","journal-title":"Environ Earth Sci"},{"key":"10065_CR16","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.catena.2017.05.034","volume":"157","author":"W Chen","year":"2017","unstructured":"Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310\u2013324","journal-title":"CATENA"},{"key":"10065_CR17","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1007\/s00366-019-0075","volume":"36","author":"W Chen","year":"2020","unstructured":"Chen W, Sarir P, Bui X-N et al (2020) Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Eng Comput 36:1101\u20131115. https:\/\/doi.org\/10.1007\/s00366-019-0075","journal-title":"Eng Comput"},{"key":"10065_CR18","doi-asserted-by":"publisher","first-page":"3665","DOI":"10.1007\/s10462-020-09935-1","volume":"54","author":"S Choubey","year":"2021","unstructured":"Choubey S, Karmakar GP (2021) Artificial intelligence techniques and their application in oil and gas industry. Artif Intell Rev 54:3665\u20133683","journal-title":"Artif Intell Rev"},{"key":"10065_CR19","unstructured":"Darrag AA (1987) Capacity of driven piles in cohesionless soils including residual stresses. PhD, Thesis, School of Civil Engineering, Purdue University, USA"},{"key":"10065_CR20","volume-title":"Charles Darwin\u2019s natural selection: being the second part of his big species book written from 1856 to 1858","author":"C Darwin","year":"1987","unstructured":"Darwin C (1987) Charles Darwin\u2019s natural selection: being the second part of his big species book written from 1856 to 1858. Cambridge University Press, Cambridge"},{"key":"10065_CR21","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.catena.2015.07.020","volume":"135","author":"A Dehnavi","year":"2015","unstructured":"Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA 135:122\u2013148","journal-title":"CATENA"},{"key":"10065_CR22","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.engappai.2014.07.016","volume":"36","author":"MB Dowlatshahi","year":"2014","unstructured":"Dowlatshahi MB, Nezamabadi-Pour H (2014) GGSA: a grouping gravitational search algorithm for data clustering. Eng Appl Artif Intell 36:114\u2013121","journal-title":"Eng Appl Artif Intell"},{"key":"10065_CR23","first-page":"1","volume-title":"Self-organizing methods in modeling: GMDH type algorithms","author":"SJ Farlow","year":"1984","unstructured":"Farlow SJ (1984) The GMDH algorithm. In: Farlow SJ (ed) Self-organizing methods in modeling: GMDH type algorithms. Marcel Dekker, New York, pp 1\u201324"},{"key":"10065_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40703-017-0067-6","volume":"9","author":"M Fatehnia","year":"2018","unstructured":"Fatehnia M, Amirinia G (2018) A review of genetic programming and artificial neural network applications in pile foundations. Int J Geo-Eng 9:1\u201320","journal-title":"Int J Geo-Eng"},{"key":"10065_CR25","first-page":"19","volume":"13","author":"BH Fellenius","year":"1980","unstructured":"Fellenius BH (1980) The analysis of results from routine pile load tests. Gr Eng 13:19\u201331","journal-title":"Gr Eng"},{"key":"10065_CR26","first-page":"49","volume":"1","author":"BH Fellenius","year":"1984","unstructured":"Fellenius BH (1984) Wave equation analysis and dynamic monitoring. Deep Found J 1:49\u201355","journal-title":"Deep Found J"},{"key":"10065_CR27","volume-title":"A comparison of static and dynamic load test result. Application of stress wave theory to piles","author":"BH Fellenius","year":"1992","unstructured":"Fellenius BH, Riker RE (1992) A comparison of static and dynamic load test result. Application of stress wave theory to piles. FBJ Barends, Rotterdam"},{"key":"10065_CR28","unstructured":"Goble GG, Rausche F, Moses F (1970) Dynamics studies on the bearing capacity of piles: final report to the Ohio Department of Highways. Cleveland, Ohio Case West Reserv University"},{"key":"10065_CR29","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/s00366-019-00833-x","volume":"37","author":"H Guo","year":"2021","unstructured":"Guo H, Nguyen H, Bui X-N, Armaghani DJ (2021) A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET. Eng Comput 37:421\u2013435. https:\/\/doi.org\/10.1007\/s00366-019-00833-x","journal-title":"Eng Comput"},{"key":"10065_CR30","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s11053-019-09611-","volume":"29","author":"H Han","year":"2020","unstructured":"Han H, Armaghani DJ, Tarinejad R et al (2020) Random forest and Bayesian network techniques for probabilistic prediction of flyrock induced by blasting in quarry sites. Nat Resour Res 29:655\u2013667. https:\/\/doi.org\/10.1007\/s11053-019-09611-","journal-title":"Nat Resour Res"},{"key":"10065_CR31","doi-asserted-by":"publisher","first-page":"106904","DOI":"10.1016\/j.asoc.2020.106904","volume":"99","author":"H Harandizadeh","year":"2020","unstructured":"Harandizadeh H, Armaghani DJ (2020) Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Appl Soft Comput 99:106904","journal-title":"Appl Soft Comput"},{"key":"10065_CR33","doi-asserted-by":"publisher","first-page":"9537","DOI":"10.1007\/s00500-018-3517-","volume":"23","author":"H Harandizadeh","year":"2019","unstructured":"Harandizadeh H, Toufigh MM, Toufigh V (2019) Application of improved ANFIS approaches to estimate bearing capacity of piles. Soft Comput 23:9537\u20139549. https:\/\/doi.org\/10.1007\/s00500-018-3517-","journal-title":"Soft Comput"},{"key":"10065_CR32","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s00366-019-00849-","volume":"37","author":"H Harandizadeh","year":"2021","unstructured":"Harandizadeh H, Armaghani DJ, 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-","journal-title":"Eng Comput"},{"key":"10065_CR34","doi-asserted-by":"publisher","first-page":"122230","DOI":"10.1016\/j.conbuildmat.2020.122230","volume":"276","author":"J Huang","year":"2021","unstructured":"Huang J, Kumar GS, Sun Y (2021a) Evaluation of workability and mechanical properties of asphalt binder and mixture modified with waste toner. Constr Build Mater 276:122230","journal-title":"Constr Build Mater"},{"key":"10065_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-021-01305-x","author":"J Huang","year":"2021","unstructured":"Huang J, Sun Y, Zhang J (2021b) Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-021-01305-x","journal-title":"Eng Comput"},{"key":"10065_CR39","first-page":"43","volume":"13","author":"AG Ivakhnenko","year":"1968","unstructured":"Ivakhnenko AG (1968) The group method of data of handling; a rival of the method of stochastic approximation. Sov Autom Control 13:43\u201355","journal-title":"Sov Autom Control"},{"key":"10065_CR40","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ijrmms.2016.03.018","volume":"85","author":"D Jahed Armaghani","year":"2016","unstructured":"Jahed Armaghani D, Mohd Amin MF, Yagiz S et al (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174\u2013186. https:\/\/doi.org\/10.1016\/j.ijrmms.2016.03.018","journal-title":"Int J Rock Mech Min Sci"},{"key":"10065_CR41","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1016\/j.engappai.2008.11.005","volume":"22","author":"A Jamali","year":"2009","unstructured":"Jamali A, Nariman-Zadeh N, Darvizeh A et al (2009) Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Eng Appl Artif Intell 22:676\u2013687","journal-title":"Eng Appl Artif Intell"},{"key":"10065_CR42","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1109\/72.159060","volume":"3","author":"J-SR Jang","year":"1992","unstructured":"Jang J-SR (1992) Self-learning fuzzy controllers based on temporal backpropagation. IEEE Trans Neural Netw 3:714\u2013723","journal-title":"IEEE Trans Neural Netw"},{"key":"10065_CR43","volume-title":"Neuro-fuzzy and soft computation","author":"R Jang","year":"1997","unstructured":"Jang R, Sun C, Mizutani E (1997) Neuro-fuzzy and soft computation. PrenticeHall, Hoboken"},{"key":"10065_CR44","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1007\/s10462-017-9610-2","volume":"52","author":"D Karaboga","year":"2019","unstructured":"Karaboga D, Kaya E (2019) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev 52:2263\u20132293","journal-title":"Artif Intell Rev"},{"key":"10065_CR46","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1016\/j.ijrmms.2009.03.004","volume":"46","author":"M Khandelwal","year":"2013","unstructured":"Khandelwal M, Singh TN (2013) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214\u20131222. https:\/\/doi.org\/10.1016\/j.ijrmms.2009.03.004","journal-title":"Int J Rock Mech Min Sci"},{"key":"10065_CR45","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00366-016-0452-3","volume":"33","author":"M Khandelwal","year":"2017","unstructured":"Khandelwal M, Faradonbeh RS, Monjezi M et al (2017) Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models. Eng Comput 33:13\u201321","journal-title":"Eng Comput"},{"key":"10065_CR48","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.measurement.2019.04.081","volume":"146","author":"M Khari","year":"2019","unstructured":"Khari M, Dehghanbandaki A, Motamedi S, Armaghani DJ (2019) Computational estimation of lateral pile displacement in layered sand using experimental data. Measurement 146:110\u2013118","journal-title":"Measurement"},{"key":"10065_CR47","doi-asserted-by":"publisher","first-page":"3499","DOI":"10.1007\/s13369-019-0413","volume":"45","author":"M Khari","year":"2020","unstructured":"Khari M, Armaghani DJ, Dehghanbanadaki A (2020) prediction of lateral deflection of small-scale piles using hybrid PSO\u2013ANN model. Arab J Sci Eng 45:3499\u20133509. https:\/\/doi.org\/10.1007\/s13369-019-0413","journal-title":"Arab J Sci Eng"},{"key":"10065_CR49","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 124:1177\u20131185","journal-title":"J Geotech Geoenviron Eng"},{"key":"10065_CR50","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.compgeo.2013.08.001","volume":"55","author":"A Kordjazi","year":"2015","unstructured":"Kordjazi A, Pooya Nejad F, Jaksa MB (2015) Prediction of load-carrying capacity of piles using a support vector machine and improved data collection. Comput Geotech 55:91\u2013102","journal-title":"Comput Geotech"},{"key":"10065_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/s00603-019-01947-w","author":"B Liu","year":"2019","unstructured":"Liu B, Yang H, Karekal S (2019) Effect of water content on argillization of mudstone during the tunnelling process. Rock Mech Rock Eng. https:\/\/doi.org\/10.1007\/s00603-019-01947-w","journal-title":"Rock Mech Rock Eng"},{"key":"10065_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00858-2","author":"Z Luo","year":"2019","unstructured":"Luo Z, Hasanipanah M, Amnieh HB et al (2019) 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":"10065_CR53","doi-asserted-by":"publisher","first-page":"2549","DOI":"10.1007\/s10462-019-09741-4","volume":"53","author":"A Mahdavi-Meymand","year":"2020","unstructured":"Mahdavi-Meymand A, Zounemat-Kermani M (2020) A new integrated model of the group method of data handling and the firefly algorithm (GMDH-FA): application to aeration modelling on spillways. Artif Intell Rev 53:2549\u20132569","journal-title":"Artif Intell Rev"},{"key":"10065_CR54","doi-asserted-by":"publisher","first-page":"681","DOI":"10.4028\/www.scientific.net\/AMM.567.681","volume":"567","author":"A Marto","year":"2014","unstructured":"Marto A, Hajihassani M, Momeni E (2014) Bearing capacity of shallow foundation\u2019s prediction through hybrid artificial neural networks. Appl Mech Mater 567:681\u2013686","journal-title":"Appl Mech Mater"},{"key":"10065_CR55","first-page":"196","volume":"102","author":"GG Mayerhof","year":"1976","unstructured":"Mayerhof GG (1976) Bearing capacity and settlemtn of pile foundations. J Geotech Geoenviron Eng 102:196\u2013228","journal-title":"J Geotech Geoenviron Eng"},{"key":"10065_CR56","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1080\/19386362.2016.1269043","volume":"12","author":"R Mohanty","year":"2018","unstructured":"Mohanty R, Suman S, Das SK (2018) Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques. Int J Geotech Eng 12:209\u2013216","journal-title":"Int J Geotech Eng"},{"key":"10065_CR59","doi-asserted-by":"publisher","first-page":"15","DOI":"10.11113\/jt.v61.1777","volume":"61","author":"E Momeni","year":"2013","unstructured":"Momeni E, Maizir H, Gofar N, Nazir R (2013) Comparative study on prediction of axial bearing capacity of driven piles in granular materials. J Teknol (sci Eng) 61:15\u201320. https:\/\/doi.org\/10.11113\/jt.v61.1777","journal-title":"J Teknol (sci Eng)"},{"key":"10065_CR60","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.measurement.2014.08.007","volume":"57","author":"E Momeni","year":"2014","unstructured":"Momeni E, Nazir R, Armaghani DJ, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122\u2013131","journal-title":"Measurement"},{"key":"10065_CR61","doi-asserted-by":"publisher","first-page":"85","DOI":"10.15446\/esrj.v19n1.38712","volume":"19","author":"E Momeni","year":"2015","unstructured":"Momeni E, Nazir R, Armaghani DJ, Maizir H (2015) Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sci Res J 19:85\u201393","journal-title":"Earth Sci Res J"},{"key":"10065_CR57","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s00366-017-0542-x","volume":"34","author":"E Momeni","year":"2018","unstructured":"Momeni E, Armaghani DJ, Fatemi SA, Nazir R (2018a) Prediction of bearing capacity of thin-walled foundation: a simulation approach. Eng Comput 34:319\u2013327","journal-title":"Eng Comput"},{"key":"10065_CR62","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.compgeo.2018.03.012","volume":"100","author":"E Momeni","year":"2018","unstructured":"Momeni E, Poormoosavian M, Mahdiyar A, Fakher A (2018b) Evaluating random set technique for reliability analysis of deep urban excavation using Monte Carlo simulation. Comput Geotech 100:203\u2013215","journal-title":"Comput Geotech"},{"key":"10065_CR58","doi-asserted-by":"publisher","first-page":"8255","DOI":"10.1007\/s13369-020-04683-4","volume":"45","author":"E Momeni","year":"2020","unstructured":"Momeni E, Dowlatshahi MB, Omidinasab F et al (2020) Gaussian process regression technique to estimate the pile bearing capacity. Arab J Sci Eng 45:8255\u20138267. https:\/\/doi.org\/10.1007\/s13369-020-04683-4","journal-title":"Arab J Sci Eng"},{"key":"10065_CR63","doi-asserted-by":"publisher","first-page":"100560","DOI":"10.1016\/j.trgeo.2021.100560","volume":"29","author":"E Momeni","year":"2021","unstructured":"Momeni E, Poormoosavian M, Tehrani HS, Fakher A (2021) Reliability analysis and risk assessment of deep excavations using random-set finite element method and event tree technique. Transp Geotech 29:100560","journal-title":"Transp Geotech"},{"key":"10065_CR64","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1179\/1938636213Z.00000000041","volume":"7","author":"PK Muduli","year":"2013","unstructured":"Muduli PK, Das SK, Das MR (2013) Prediction of lateral load capacity of piles using extreme learning machine. Int J Geotech Eng 7:388\u2013394","journal-title":"Int J Geotech Eng"},{"key":"10065_CR65","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1243\/09544050360673161","volume":"217","author":"N Nariman-Zadeh","year":"2003","unstructured":"Nariman-Zadeh N, Darvizeh A, Ahmad-Zadeh GR (2003) Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proc Inst Mech Eng B J Eng Manuf 217:779\u2013790","journal-title":"Proc Inst Mech Eng B J Eng Manuf"},{"key":"10065_CR66","first-page":"2477","volume":"18","author":"R Nazir","year":"2013","unstructured":"Nazir R, Momeni E, Gofar N, Maizir H (2013) Numerical modelling of skin resistance distribution with depth in driven pile. Electron J Geotech Eng 18:2477\u20132488","journal-title":"Electron J Geotech Eng"},{"key":"10065_CR93","first-page":"9","volume":"72","author":"R Nazir","year":"2015","unstructured":"Nazir R, Momeni E, Marsono K,  Maizir H (2015) An artificial neural network approach for prediction of bearing capacity of spread foundations in sand. J Teknol 72:9\u201314","journal-title":"J Teknol"},{"key":"10065_CR67","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s00419-011-0548-6","volume":"82","author":"A Nicknam","year":"2012","unstructured":"Nicknam A, Hosseini MH (2012) Structural damage localization and evaluation based on modal data via a new evolutionary algorithm. Arch Appl Mech 82:191\u2013203","journal-title":"Arch Appl Mech"},{"key":"10065_CR70","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1061\/(ASCE)1090-0241(2008)134:7(1021)","volume":"134","author":"M Pal","year":"2008","unstructured":"Pal M, Deswal S (2008) Modeling pile capacity using support vector machines and generalized regression neural network. J Geotech Geoenviron Eng 134:1021\u20131024","journal-title":"J Geotech Geoenviron Eng"},{"key":"10065_CR69","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1016\/j.compgeo.2010.07.012","volume":"37","author":"M Pal","year":"2010","unstructured":"Pal M, Deswal S (2010) Modelling pile capacity using Gaussian process regression. Comput Geotech 37:942\u2013947","journal-title":"Comput Geotech"},{"key":"10065_CR72","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.3233\/IFS-141443","volume":"29","author":"Y Qin","year":"2015","unstructured":"Qin Y, Langari R, Gu L (2015) A new modeling algorithm based on ANFIS and GMDH. J Intell Fuzzy Syst 29:1321\u20131329","journal-title":"J Intell Fuzzy Syst"},{"key":"10065_CR73","unstructured":"Rao KM, Suresh Kumar V (1996) Measured and predicted response of laterally loaded piles. In: Proceedings of the sixth international conference and exhibition on piling and deep foundations, India, p 1"},{"key":"10065_CR74","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1061\/(ASCE)0733-9410(1985)111:3(367)","volume":"111","author":"F Rausche","year":"1985","unstructured":"Rausche F, Goble GG, Likins GE Jr (1985) Dynamic determination of pile capacity. J Geotech Eng 111:367\u2013383","journal-title":"J Geotech Eng"},{"key":"10065_CR75","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1631\/jzus.A1500033","volume":"17","author":"H Rezaei","year":"2016","unstructured":"Rezaei H, Nazir R, Momeni E (2016) Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study. J Zhejiang Univ A 17:273\u2013285","journal-title":"J Zhejiang Univ A"},{"key":"10065_CR76","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3328\/IJGE.2011.05.01.95-102","volume":"5","author":"P Samui","year":"2011","unstructured":"Samui P (2011) Prediction of pile bearing capacity using support vector machine. Int J Geotech Eng 5:95\u2013102","journal-title":"Int J Geotech Eng"},{"key":"10065_CR77","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1007\/s00521-012-1043-x","volume":"23","author":"P Samui","year":"2013","unstructured":"Samui P, Kim D (2013) Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles. Neural Comput Appl 23:1123\u20131127","journal-title":"Neural Comput Appl"},{"key":"10065_CR78","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.gsf.2014.10.002","volume":"7","author":"MA Shahin","year":"2016","unstructured":"Shahin MA (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front 7:33\u201344. https:\/\/doi.org\/10.1016\/j.gsf.2014.10.002","journal-title":"Geosci Front"},{"key":"10065_CR79","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1007\/s00366-018-0674-7","volume":"35","author":"S Shaik","year":"2019","unstructured":"Shaik S, Krishna KSR, Abbas M et al (2019) Applying several soft computing techniques for prediction of bearing capacity of driven piles. Eng Comput 35:1463\u20131474","journal-title":"Eng Comput"},{"key":"10065_CR80","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.catena.2017.05.016","volume":"157","author":"A Shirzadi","year":"2017","unstructured":"Shirzadi A, Shahabi H, Chapi K et al (2017) A comparative study between popular statistical and machine learning methods for simulating volume of landslides. CATENA 157:213\u2013226","journal-title":"CATENA"},{"key":"10065_CR81","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1016\/j.measurement.2013.04.077","volume":"46","author":"H Taghavifar","year":"2013","unstructured":"Taghavifar H, Mardani A, Taghavifar L (2013) A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility. Measurement 46:2288\u20132299","journal-title":"Measurement"},{"key":"10065_CR82","unstructured":"Vesic AS (1977) Design of pile foundations. National cooperative highway research program synthesis of practice no. 42. Transp Res Board, Washington, DC, 3248"},{"key":"10065_CR86","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.enggeo.2014.11.016","volume":"185","author":"HQ Yang","year":"2015","unstructured":"Yang HQ, Lan YF, Lu L, Zhou XP (2015) A quasi-three-dimensional spring-deformable-block model for runout analysis of rapid landslide motion. Eng Geol 185:20\u201332","journal-title":"Eng Geol"},{"key":"10065_CR83","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.tust.2016.02.014","volume":"57","author":"H Yang","year":"2016","unstructured":"Yang H, Wang H, Zhou X (2016a) Analysis on the damage behavior of mixed ground during TBM cutting process. Tunn Undergr Sp Technol 57:55\u201365","journal-title":"Tunn Undergr Sp Technol"},{"key":"10065_CR84","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s00603-015-0796-9","volume":"49","author":"H Yang","year":"2016","unstructured":"Yang H, Wang H, Zhou X (2016b) Analysis on the rock\u2013cutter interaction mechanism during the TBM tunneling process. Rock Mech Rock Eng 49:1073\u20131090","journal-title":"Rock Mech Rock Eng"},{"key":"10065_CR85","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-01217-2","author":"H Yang","year":"2020","unstructured":"Yang H, Wang Z, Song K (2020) A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-01217-2","journal-title":"Eng Comput"},{"key":"10065_CR87","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-01085-w","author":"J Ye","year":"2020","unstructured":"Ye J, Dalle J, Nezami R et al (2020) Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-01085-w","journal-title":"Eng Comput"},{"key":"10065_CR88","doi-asserted-by":"publisher","first-page":"869","DOI":"10.3390\/app10030869","volume":"10","author":"H Zhang","year":"2020","unstructured":"Zhang H, Zhou J, Armaghani DJ et al (2020) A Combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration. Appl Sci 10:869","journal-title":"Appl Sci"},{"key":"10065_CR89","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-09967-1","author":"W Zhang","year":"2021","unstructured":"Zhang W, Li H, Li Y et al (2021) Application of deep learning algorithms in geotechnical engineering: a short critical review. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-021-09967-1","journal-title":"Artif Intell Rev"},{"key":"10065_CR90","doi-asserted-by":"publisher","first-page":"106390","DOI":"10.1016\/j.soildyn.2020.106390","volume":"139","author":"J Zhou","year":"2020","unstructured":"Zhou J, Asteris PG, Armaghani DJ, Pham BT (2020a) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 139:106390. https:\/\/doi.org\/10.1016\/j.soildyn.2020.106390","journal-title":"Soil Dyn Earthq Eng"},{"key":"10065_CR91","doi-asserted-by":"publisher","DOI":"10.1080\/17480930.2020.1734151","author":"J Zhou","year":"2020","unstructured":"Zhou J, Li C, Koopialipoor M et al (2020b) Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC). Int J Min Reclam Environ. https:\/\/doi.org\/10.1080\/17480930.2020.1734151","journal-title":"Int J Min Reclam Environ"},{"key":"10065_CR92","doi-asserted-by":"publisher","first-page":"104015","DOI":"10.1016\/j.engappai.2020.104015","volume":"97","author":"J Zhou","year":"2021","unstructured":"Zhou J, Qiu Y, Zhu S et al (2021) Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Eng Appl Artif Intell 97:104015","journal-title":"Eng Appl Artif Intell"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-021-10065-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-021-10065-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-021-10065-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T03:30:42Z","timestamp":1675654242000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-021-10065-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,3]]},"references-count":89,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["10065"],"URL":"https:\/\/doi.org\/10.1007\/s10462-021-10065-5","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,3]]},"assertion":[{"value":"3 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}