{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T04:44:12Z","timestamp":1722919452656},"reference-count":98,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00521-022-07214-4","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T20:03:02Z","timestamp":1651176182000},"page":"15755-15779","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A novel TS Fuzzy-GMDH model optimized by PSO to determine the deformation values of rock material"],"prefix":"10.1007","volume":"34","author":[{"given":"Hooman","family":"Harandizadeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danial Jahed","family":"Armaghani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahdi","family":"Hasanipanah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soheil","family":"Jahandari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"7214_CR1","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s10064-016-0983-2","volume":"77","author":"BY Bejarbaneh","year":"2018","unstructured":"Bejarbaneh BY, Bejarbaneh EY, Fahimifar A et al (2018) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull Eng Geol Environ 77:345\u2013361","journal-title":"Bull Eng Geol Environ"},{"key":"7214_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.12.029","author":"B Yazdani Bejarbaneh","year":"2015","unstructured":"Yazdani Bejarbaneh B, Jahed Armaghani D, Mohd Amin MF (2015) Strength characterisation of shale using Mohr\u2013Coulomb and Hoek\u2013Brown criteria. Meas J Int Meas Confed. https:\/\/doi.org\/10.1016\/j.measurement.2014.12.029","journal-title":"Meas J Int Meas Confed"},{"key":"7214_CR3","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1007\/s00521-016-2728-3","volume":"30","author":"ET Mohamad","year":"2018","unstructured":"Mohamad ET, Armaghani DJ, Momeni E et al (2018) Rock strength estimation: a PSO-based BP approach. Neural Comput Appl 30:1635\u20131646","journal-title":"Neural Comput Appl"},{"key":"7214_CR4","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":"7214_CR5","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1007\/s00603-017-1395-8","volume":"51","author":"H Yang","year":"2018","unstructured":"Yang H, Liu J, Liu B (2018) Investigation on the cracking character of jointed rock mass beneath TBM disc cutter. Rock Mech Rock Eng 51:1263\u20131277","journal-title":"Rock Mech Rock Eng"},{"key":"7214_CR6","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1139\/t94-081","volume":"31","author":"CD Martin","year":"1994","unstructured":"Martin CD, Stimpson B (1994) The effect of sample disturbance on laboratory properties of Lac du Bonnet granite. Can Geotech J 31:692\u2013702","journal-title":"Can Geotech J"},{"key":"7214_CR7","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.enggeo.2013.04.004","volume":"160","author":"DA Mishra","year":"2013","unstructured":"Mishra DA, Basu A (2013) Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Eng Geol 160:54\u201368","journal-title":"Eng Geol"},{"key":"7214_CR8","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.asoc.2011.09.010","volume":"12","author":"R Singh","year":"2012","unstructured":"Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12:40\u201345","journal-title":"Appl Soft Comput"},{"key":"7214_CR9","unstructured":"Mitri HS, Edrissi R, Henning J (1994) Finite element modeling of cable-bolted stopes in hard rock underground mines. In: Presented at the SME annual meeting. Albuquerque, New Mex 4"},{"key":"7214_CR10","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.ijrmms.2003.01.006","volume":"41","author":"H Sonmez","year":"2004","unstructured":"Sonmez H, Gokceoglu C, Ulusay R (2004) Indirect determination of the modulus of deformation of rock masses based on the GSI system. Int J Rock Mech Min Sci 41:849\u2013857","journal-title":"Int J Rock Mech Min Sci"},{"key":"7214_CR11","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S1365-1609(02)00011-4","volume":"39","author":"N Barton","year":"2002","unstructured":"Barton N (2002) Some new Q-value correlations to assist in site characterisation and tunnel design. Int J Rock Mech Min Sci 39:185\u2013216","journal-title":"Int J Rock Mech Min Sci"},{"key":"7214_CR12","doi-asserted-by":"crossref","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 (2016) 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":"7214_CR13","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.ijrmms.2004.01.012","volume":"41","author":"E Yasar","year":"2004","unstructured":"Yasar E, Erdogan Y (2004) Correlating sound velocity with the density, compressive strength and Young\u2019s modulus of carbonate rocks. Int J Rock Mech Min Sci 41:871\u2013875","journal-title":"Int J Rock Mech Min Sci"},{"key":"7214_CR14","first-page":"1","volume":"1","author":"DJ Armaghani","year":"2020","unstructured":"Armaghani DJ, Momeni E, Asteris P (2020) Application of group method of data handling technique in assessing deformation of rock mass. Metaheuristic Comput Appl 1:1\u201318","journal-title":"Metaheuristic Comput Appl"},{"key":"7214_CR15","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S0013-7952(02)00041-8","volume":"66","author":"I Y\u0131lmaz","year":"2002","unstructured":"Y\u0131lmaz I, Send\u0131r H (2002) Correlation of Schmidt hardness with unconfined compressive strength and Young\u2019s modulus in gypsum from Sivas (Turkey). Eng Geol 66:211\u2013219","journal-title":"Eng Geol"},{"key":"7214_CR16","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s10064-004-0230-0","volume":"63","author":"I Din\u00e7er","year":"2004","unstructured":"Din\u00e7er I, Acar A, \u00c7obano\u011flu I, Uras Y (2004) Correlation between Schmidt hardness, uniaxial compressive strength and Young\u2019s modulus for andesites, basalts and tuffs. Bull Eng Geol Environ 63:141\u2013148","journal-title":"Bull Eng Geol Environ"},{"key":"7214_CR17","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1007\/s12517-015-2057-3","volume":"9","author":"DJ Armaghani","year":"2016","unstructured":"Armaghani DJ, Mohamad ET, Momeni E et al (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9:48","journal-title":"Arab J Geosci"},{"key":"7214_CR18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s100640100116","volume":"61","author":"GR Lashkaripour","year":"2002","unstructured":"Lashkaripour GR (2002) Predicting mechanical properties of mudrock from index parameters. Bull Eng Geol Environ 61:73\u201377","journal-title":"Bull Eng Geol Environ"},{"key":"7214_CR19","first-page":"159","volume":"63","author":"M Beiki","year":"2013","unstructured":"Beiki M, Majdi A, Givshad A (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. J Rock Mech Min 63:159\u2013169","journal-title":"J Rock Mech Min"},{"issue":"5","key":"7214_CR20","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/s00603-007-0138-7","volume":"41","author":"I Y\u0131lmaz","year":"2008","unstructured":"Y\u0131lmaz I, Yuksek A (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781","journal-title":"Rock Mech Rock Eng"},{"key":"7214_CR21","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1016\/j.ijrmms.2008.09.002","volume":"46","author":"I Yilmaz","year":"2009","unstructured":"Yilmaz I, Yuksek G (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int J Rock Mech Min Sci 46:803\u2013810","journal-title":"Int J Rock Mech Min Sci"},{"key":"7214_CR22","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s00366-015-0429-7","volume":"32","author":"M Liang","year":"2016","unstructured":"Liang M, Mohamad ET, Faradonbeh RS et al (2016) Rock strength assessment based on regression tree technique. Eng Comput 32:343\u2013354. https:\/\/doi.org\/10.1007\/s00366-015-0429-7","journal-title":"Eng Comput"},{"key":"7214_CR23","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":"7214_CR24","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/s12517-018-3929-0","volume":"11","author":"Y Abdi","year":"2018","unstructured":"Abdi Y, Garavand AT, Sahamieh RZ (2018) Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis. Arab J Geosci 11:587","journal-title":"Arab J Geosci"},{"key":"7214_CR25","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s00366-015-0410-5","volume":"32","author":"DJ Armaghani","year":"2016","unstructured":"Armaghani DJ, Mohamad ET, Hajihassani M et al (2016) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comput 32:189\u2013206","journal-title":"Eng Comput"},{"key":"7214_CR26","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.engappai.2003.11.006","volume":"17","author":"C Gokceoglu","year":"2004","unstructured":"Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17:61\u201372","journal-title":"Eng Appl Artif Intell"},{"key":"7214_CR27","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.measurement.2015.07.019","volume":"75","author":"M Hasanipanah","year":"2015","unstructured":"Hasanipanah M, Monjezi M, Shahnazar A, Armaghani DJ, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289\u2013297","journal-title":"Measurement"},{"key":"7214_CR28","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.ijrmms.2015.10.012","volume":"100","author":"DA Mishra","year":"2015","unstructured":"Mishra DA, Srigyan M, Basu A, Rokade PJ (2015) Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests. Int J Rock Mech Min Sci 100:418\u2013424","journal-title":"Int J Rock Mech Min Sci"},{"key":"7214_CR29","volume":"145","author":"J Zhou","year":"2021","unstructured":"Zhou J, Qiu Y, Khandelwal M et al (2021) Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. Int J Rock Mech Min Sci 145:104856","journal-title":"Int J Rock Mech Min Sci"},{"key":"7214_CR30","doi-asserted-by":"crossref","first-page":"4016003","DOI":"10.1061\/(ASCE)CP.1943-5487.0000553","volume":"30","author":"J Zhou","year":"2016","unstructured":"Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30:4016003","journal-title":"J Comput Civ Eng"},{"key":"7214_CR31","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.104015","volume":"97","author":"J Zhou","year":"2021","unstructured":"Zhou J, Qiu Y, Zhu S, Armaghani DJ, Li C, Nguyen H, Yagiz S (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"},{"key":"7214_CR32","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.tust.2016.12.009","volume":"63","author":"DJ Armaghani","year":"2017","unstructured":"Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Technol 63:29\u201343. https:\/\/doi.org\/10.1016\/j.tust.2016.12.009","journal-title":"Tunn Undergr Sp Technol"},{"key":"7214_CR33","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/S1365-1609(00)00078-2","volume":"38","author":"V Singh","year":"2001","unstructured":"Singh V, Singh D, Singh T (2001) Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38:269\u2013284","journal-title":"Int J Rock Mech Min Sci"},{"key":"7214_CR34","doi-asserted-by":"crossref","DOI":"10.1016\/j.soildyn.2020.106390","volume":"139","author":"J Zhou","year":"2020","unstructured":"Zhou J, Asteris PG, Armaghani DJ, Pham BT (2020) 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","journal-title":"Soil Dyn Earthq Eng"},{"key":"7214_CR35","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"},{"issue":"18","key":"7214_CR36","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.3390\/app9183715","volume":"9","author":"H Xu","year":"2019","unstructured":"Xu H, Zhou J, Asteris PG, Jahed Armaghani D, Tahir MM (2019) Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate. Appl Sci 9(18):3715","journal-title":"Appl Sci"},{"key":"7214_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06217-x","author":"H Harandizadeh","year":"2021","unstructured":"Harandizadeh H, Armaghani D, Asteris PGGA (2021) TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06217-x","journal-title":"Neural Comput Appl"},{"key":"7214_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmst.2021.07.011","author":"J Zhou","year":"2021","unstructured":"Zhou J, Chen C, Wang M, Khandelwal M (2021) Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors. Int J Min Sci Technol. https:\/\/doi.org\/10.1016\/j.ijmst.2021.07.011","journal-title":"Int J Min Sci Technol"},{"key":"7214_CR39","doi-asserted-by":"crossref","first-page":"6874","DOI":"10.1016\/j.eswa.2008.08.002","volume":"36","author":"S Kahraman","year":"2009","unstructured":"Kahraman S, Gunaydin O, Alber M, Fener M (2009) Evaluating the strength and deformability properties of Misis fault breccia using artificial neural networks. Expert Syst Appl 36:6874\u20136878","journal-title":"Expert Syst Appl"},{"key":"7214_CR40","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1002\/nag.1066","volume":"36","author":"S Yagiz","year":"2012","unstructured":"Yagiz S, Sezer EA, Gokceoglu C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. Int J Numer Anal Methods Geomech 36:1636\u20131650","journal-title":"Int J Numer Anal Methods Geomech"},{"key":"7214_CR41","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.measurement.2014.09.075","volume":"60","author":"E Momeni","year":"2015","unstructured":"Momeni E, Armaghani DJ, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50\u201363","journal-title":"Measurement"},{"key":"7214_CR42","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s10064-014-0638-0","volume":"74","author":"ET Mohamad","year":"2015","unstructured":"Mohamad ET, Armaghani DJ, Momeni E, Abad SVANK (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 74:745\u2013757","journal-title":"Bull Eng Geol Environ"},{"key":"7214_CR43","doi-asserted-by":"crossref","first-page":"3523","DOI":"10.1007\/s00521-017-2939-2","volume":"30","author":"DJ Armaghani","year":"2018","unstructured":"Armaghani DJ, Safari V, Fahimifar A et al (2018) Uniaxial compressive strength prediction through a new technique based on gene expression programming. Neural Comput Appl 30:3523\u20133532","journal-title":"Neural Comput Appl"},{"key":"7214_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s00603-021-02723-5","author":"H Yang","year":"2022","unstructured":"Yang H, Song K, Zhou J (2022) Automated recognition model of geomechanical information based on operational data of tunneling boring machines. Rock Mech Rock Eng. https:\/\/doi.org\/10.1007\/s00603-021-02723-5","journal-title":"Rock Mech Rock Eng"},{"key":"7214_CR45","doi-asserted-by":"crossref","first-page":"100588","DOI":"10.1016\/j.trgeo.2021.100588","volume":"29","author":"PG Asteris","year":"2021","unstructured":"Asteris PG, Mamou A, Hajihassani M et al (2021) Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transp Geotech 29:100588","journal-title":"Transp Geotech"},{"key":"7214_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-021-01329-3","author":"N Kardani","year":"2021","unstructured":"Kardani N, Bardhan A, Samui P et al (2021) A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-021-01329-3","journal-title":"Eng Comput"},{"key":"7214_CR47","first-page":"317","volume":"25","author":"DJ Armaghani","year":"2021","unstructured":"Armaghani DJ, Mamou A, Maraveas C et al (2021) Predicting the unconfined compressive strength of granite using only two non-destructive test indexes. Geomech Eng 25:317\u2013330","journal-title":"Geomech Eng"},{"key":"7214_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10065-5","author":"DJ Armaghani","year":"2021","unstructured":"Armaghani DJ, Harandizadeh H, Momeni E et al (2021) An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-021-10065-5","journal-title":"Artif Intell Rev"},{"key":"7214_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/J.TRGEO.2021.100652","volume":"31","author":"M Parsajoo","year":"2021","unstructured":"Parsajoo M, Armaghani DJ, Mohammed AS et al (2021) Tensile strength prediction of rock material using non-destructive tests: a comparative intelligent study. Transp Geotech 31:100652. https:\/\/doi.org\/10.1016\/J.TRGEO.2021.100652","journal-title":"Transp Geotech"},{"key":"7214_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.trgeo.2020.100508","author":"BT Pham","year":"2020","unstructured":"Pham BT, Nguyen MD, Nguyen-Thoi T et al (2020) A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling. Transp Geotech. https:\/\/doi.org\/10.1016\/j.trgeo.2020.100508","journal-title":"Transp Geotech"},{"key":"7214_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.undsp.2020.05.008","author":"J Zhou","year":"2020","unstructured":"Zhou J, Qiu Y, Zhu S et al (2020) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Undergr Sp. https:\/\/doi.org\/10.1016\/j.undsp.2020.05.008","journal-title":"Undergr Sp"},{"key":"7214_CR52","doi-asserted-by":"crossref","first-page":"100446","DOI":"10.1016\/j.trgeo.2020.100446","volume":"26","author":"E Momeni","year":"2020","unstructured":"Momeni E, Yarivand A, Bagher Dowlatshahi M, Jahed Armaghani D (2020) An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures. Transp Geotech 26:100446","journal-title":"Transp Geotech"},{"key":"7214_CR53","doi-asserted-by":"crossref","first-page":"4014068","DOI":"10.1061\/(ASCE)CP.1943-5487.0000376","volume":"29","author":"M Najafzadeh","year":"2015","unstructured":"Najafzadeh M, Azamathulla HM (2015) Neuro-fuzzy GMDH to predict the scour pile groups due to waves. J Comput Civ Eng 29:4014068","journal-title":"J Comput Civ Eng"},{"key":"7214_CR54","doi-asserted-by":"crossref","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":"7214_CR55","doi-asserted-by":"crossref","first-page":"6016003","DOI":"10.1061\/(ASCE)PS.1949-1204.0000249","volume":"8","author":"M Najafzadeh","year":"2016","unstructured":"Najafzadeh M, Bonakdari H (2016) Application of a neuro-fuzzy GMDH model for predicting the velocity at limit of deposition in storm sewers. J Pipeline Syst Eng Pract 8:6016003","journal-title":"J Pipeline Syst Eng Pract"},{"key":"7214_CR56","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh LA (1965) Fuzzy sets. Inf Control 8:338\u2013353","journal-title":"Inf Control"},{"key":"7214_CR57","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/TSMC.1973.5408575","volume":"1","author":"LA Zadeh","year":"1973","unstructured":"Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 1:28\u201344","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"7214_CR58","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1109\/TAC.1997.633847","volume":"42","author":"J-SR Jang","year":"1997","unstructured":"Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Trans Autom Control 42:1482\u20131484","journal-title":"IEEE Trans Autom Control"},{"key":"7214_CR59","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-35781-0","volume-title":"Introduction to fuzzy logic using MATLAB","author":"SN Sivanandam","year":"2007","unstructured":"Sivanandam SN, Sumathi S, Deepa SN (2007) Introduction to fuzzy logic using MATLAB. Springer"},{"key":"7214_CR60","volume-title":"Advanced fuzzy logic technologies in industrial applications","author":"Y Bai","year":"2007","unstructured":"Bai Y, Zhuang H, Wang D (2007) Advanced fuzzy logic technologies in industrial applications. Springer, Berlin"},{"key":"7214_CR61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0020-7373(75)80002-2","volume":"7","author":"EH Mamdani","year":"1975","unstructured":"Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1\u201313","journal-title":"Int J Man Mach Stud"},{"key":"7214_CR62","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","volume":"1","author":"T Takagi","year":"1985","unstructured":"Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116\u2013132","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"7214_CR63","doi-asserted-by":"crossref","first-page":"10819","DOI":"10.1007\/s12517-015-1952-y","volume":"8","author":"S Shams","year":"2015","unstructured":"Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8:10819\u201310832","journal-title":"Arab J Geosci"},{"key":"7214_CR64","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"1999","unstructured":"Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River"},{"key":"7214_CR65","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-01530-4","volume-title":"Hybrid self-organizing modeling systems","author":"GC Onwubolu","year":"2009","unstructured":"Onwubolu GC (2009) Hybrid self-organizing modeling systems. Springer"},{"key":"7214_CR66","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s00170-014-5835-2","volume":"73","author":"F Rayegani","year":"2014","unstructured":"Rayegani F, Onwubolu GC (2014) Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE). Int J Adv Manuf Technol 73:509\u2013519","journal-title":"Int J Adv Manuf Technol"},{"key":"7214_CR67","unstructured":"Barron AR, Barron RL (1988) Statistical learning networks: a unifying view. In: Symposium on the interface: statistics and computing science, Reston, Virginia"},{"key":"7214_CR68","unstructured":"Elder JF, Brown DE (1995) Induction and polynomial networks. In: 1995 IEEE international conference on systems, man and cybernetics. Intelligent systems for the 21st century. IEEE, pp 874\u2013879"},{"key":"7214_CR69","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/0232929031000136135","volume":"43","author":"F Lemke","year":"2003","unstructured":"Lemke F, M\u00fcller J-A (2003) Self-organising data mining. Syst Anal Model Simul 43:231\u2013240","journal-title":"Syst Anal Model Simul"},{"key":"7214_CR70","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.jbi.2005.03.003","volume":"38","author":"RE Abdel-Aal","year":"2005","unstructured":"Abdel-Aal RE (2005) GMDH-based feature ranking and selection for improved classification of medical data. J Biomed Inform 38:456\u2013468","journal-title":"J Biomed Inform"},{"key":"7214_CR71","doi-asserted-by":"crossref","first-page":"232","DOI":"10.18267\/j.pep.398","volume":"20","author":"J Tau\u0161er","year":"2011","unstructured":"Tau\u0161er J, Buryan P (2011) Exchange rate predictions in international financial management by enhanced GMDH algorithm. Prague Econ Pap 20:232\u2013249","journal-title":"Prague Econ Pap"},{"key":"7214_CR72","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1016\/j.engappai.2006.12.005","volume":"20","author":"V Puig","year":"2007","unstructured":"Puig V, Witczak M, Nejjari F et al (2007) A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test. Eng Appl Artif Intell 20:886\u2013897","journal-title":"Eng Appl Artif Intell"},{"key":"7214_CR73","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.apor.2012.12.004","volume":"40","author":"M Najafzadeh","year":"2013","unstructured":"Najafzadeh M, Barani G-A, Azamathulla HM (2013) GMDH to predict scour depth around a pier in cohesive soils. Appl Ocean Res 40:35\u201341","journal-title":"Appl Ocean Res"},{"key":"7214_CR74","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.geoderma.2004.05.007","volume":"124","author":"F Ungaro","year":"2005","unstructured":"Ungaro F, Calzolari C, Busoni E (2005) Development of pedotransfer functions using a group method of data handling for the soil of the Pianura Padano-Veneta region of North Italy: water retention properties. Geoderma 124:293\u2013317","journal-title":"Geoderma"},{"key":"7214_CR75","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.knosys.2009.06.005","volume":"22","author":"J Xiao","year":"2009","unstructured":"Xiao J, He C, Jiang X (2009) Structure identification of Bayesian classifiers based on GMDH. Knowl-Based Syst 22:461\u2013470","journal-title":"Knowl-Based Syst"},{"key":"7214_CR76","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.asoc.2015.02.011","volume":"30","author":"H Basser","year":"2015","unstructured":"Basser H, Karami H, Shamshirband S et al (2015) Hybrid ANFIS\u2013PSO approach for predicting optimum parameters of a protective spur dike. Appl Soft Comput 30:642\u2013649","journal-title":"Appl Soft Comput"},{"key":"7214_CR77","first-page":"835","volume":"10","author":"O Nelles","year":"2000","unstructured":"Nelles O, Fink A, Babuska R, Setnes M (2000) Comparision of two construction algorithms for Takagi\u2013Sugeno fuzzy models. Int J Appl Math Comput Sci 10:835\u2013855","journal-title":"Int J Appl Math Comput Sci"},{"key":"7214_CR78","doi-asserted-by":"crossref","unstructured":"Ghomsheh VS, Shoorehdeli MA, Teshnehlab M (2007) Training ANFIS structure with modified PSO algorithm. In: 2007 Mediterranean conference on control and automation. IEEE, pp 1\u20136","DOI":"10.1109\/MED.2007.4433927"},{"key":"7214_CR79","unstructured":"Kennedy J, Eberhart RC (1995) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics, 1997. Computational cybernetics and simulation. IEEE, pp 4104\u20134108"},{"key":"7214_CR80","doi-asserted-by":"crossref","unstructured":"Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence. IEEE, pp 69\u201373","DOI":"10.1109\/ICEC.1998.699146"},{"key":"7214_CR81","doi-asserted-by":"crossref","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":"7214_CR82","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.apacoust.2014.09.007","volume":"89","author":"R Barbieri","year":"2015","unstructured":"Barbieri R, Barbieri N, De Lima KF (2015) Some applications of the PSO for optimization of acoustic filters. Appl Acoust 89:62\u201370","journal-title":"Appl Acoust"},{"key":"7214_CR83","doi-asserted-by":"crossref","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":"7214_CR84","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.3390\/app9183715","volume":"9","author":"H Xu","year":"2019","unstructured":"Xu H, Zhou J, Asteris G, P, et al (2019) Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate. Appl Sci 9:3715","journal-title":"Appl Sci"},{"key":"7214_CR85","unstructured":"Ulusay R, Hudson JA ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974\u20132006. In: Comm Test methods Int Soc Rock Mech Compil arranged by ISRM Turkish Natl Group, Ankara, Turkey 628."},{"key":"7214_CR86","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/69.494162","volume":"8","author":"CG Looney","year":"1996","unstructured":"Looney CG (1996) Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8:211\u2013226","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7214_CR87","volume-title":"Applying neural networks: a practical guide","author":"K Swingler","year":"1996","unstructured":"Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York"},{"key":"7214_CR88","doi-asserted-by":"crossref","first-page":"267","DOI":"10.3233\/IFS-1994-2306","volume":"2","author":"SL Chiu","year":"1994","unstructured":"Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell fuzzy Syst 2:267\u2013278","journal-title":"J Intell fuzzy Syst"},{"key":"7214_CR89","unstructured":"MATLAB R (2018) version 9.4. 0.813654 (R2018a). MathWorks R Natick, MA, USA"},{"key":"7214_CR90","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.enggeo.2007.10.009","volume":"96","author":"K Zorlu","year":"2008","unstructured":"Zorlu K, Gokceoglu C, Ocakoglu F et al (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141\u2013158","journal-title":"Eng Geol"},{"key":"7214_CR91","doi-asserted-by":"crossref","first-page":"2364","DOI":"10.3390\/app10072364","volume":"10","author":"D Li","year":"2020","unstructured":"Li D, Moghaddam MR, Monjezi M et al (2020) Development of a group method of data handling technique to forecast iron ore price. Appl Sci 10:2364","journal-title":"Appl Sci"},{"key":"7214_CR92","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1007\/s10064-018-1349-8","volume":"78","author":"M Koopialipoor","year":"2018","unstructured":"Koopialipoor M, Nikouei SS, Marto A et al (2018) Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bull Eng Geol Environ 78:3799\u20133813","journal-title":"Bull Eng Geol Environ"},{"key":"7214_CR93","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.apacoust.2014.01.005","volume":"80","author":"M Hajihassani","year":"2014","unstructured":"Hajihassani M, Jahed Armaghani D, Sohaei H et al (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57\u201367. https:\/\/doi.org\/10.1016\/j.apacoust.2014.01.005","journal-title":"Appl Acoust"},{"key":"7214_CR94","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.measurement.2014.06.001","volume":"55","author":"D Jahed Armaghani","year":"2014","unstructured":"Jahed Armaghani D, Hajihassani M, Yazdani Bejarbaneh B et al (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Meas J Int Meas Confed 55:487\u2013498. https:\/\/doi.org\/10.1016\/j.measurement.2014.06.001","journal-title":"Meas J Int Meas Confed"},{"key":"7214_CR95","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.ins.2005.02.003","volume":"176","author":"F Van Den Bergh","year":"2006","unstructured":"Van Den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci (NY) 176:937\u2013971","journal-title":"Inf Sci (NY)"},{"key":"7214_CR96","first-page":"3","volume":"1","author":"X Cai","year":"2009","unstructured":"Cai X, Cui Y, Tan Y (2009) Predicted modified PSO with time-varying accelerator coefficients. Cognition 1:3","journal-title":"Cognition"},{"key":"7214_CR97","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrmge.2019.01.002","author":"DJ Armaghani","year":"2019","unstructured":"Armaghani DJ, Koopialipoor M, Marto A, Yagiz S (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J Rock Mech Geotech Eng. https:\/\/doi.org\/10.1016\/j.jrmge.2019.01.002","journal-title":"J Rock Mech Geotech Eng"},{"key":"7214_CR98","unstructured":"Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 congress on evolutionary computation. CEC00 (Cat. No. 00TH8512). IEEE, pp 84\u201388"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07214-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07214-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07214-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T18:30:53Z","timestamp":1662057053000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07214-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,28]]},"references-count":98,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["7214"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07214-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,28]]},"assertion":[{"value":"15 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}