{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T21:51:32Z","timestamp":1782251492723,"version":"3.54.5"},"reference-count":106,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"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 & Applic"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s00521-020-05244-4","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T16:03:29Z","timestamp":1597075409000},"page":"4501-4532","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":410,"title":["A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength"],"prefix":"10.1007","volume":"33","author":[{"given":"Danial Jahed","family":"Armaghani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7142-4981","authenticated-orcid":false,"given":"Panagiotis G.","family":"Asteris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,8,10]]},"reference":[{"key":"5244_CR1","unstructured":"Apostolopoulou M, Douvika MG, Kanellopoulos IN, Moropoulou A, Asteris PG (2018) Prediction of compressive strength of mortars using artificial neural networks. In: 1st international conference TMM_CH, transdisciplinary multispectral modeling and cooperation for the preservation of cultural heritage, Athens, Greece"},{"key":"5244_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-019-01292-3","author":"M Wo\u017aniak","year":"2020","unstructured":"Wo\u017aniak M, Po\u0142ap D (2020) Soft trees with neural components as image-processing technique for archeological excavations. Pers Ubiquit Comput. https:\/\/doi.org\/10.1007\/s00779-019-01292-3","journal-title":"Pers Ubiquit Comput"},{"key":"5244_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2951089","author":"M Wo\u017aniak","year":"2019","unstructured":"Wo\u017aniak M, Po\u0142ap D (2019) Intelligent home systems for ubiquitous user support by using neural networks and rule based approach. IEEE Trans Indus Inform. https:\/\/doi.org\/10.1109\/TII.2019.2951089","journal-title":"IEEE Trans Indus Inform"},{"key":"5244_CR4","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.neunet.2017.04.013","volume":"93","author":"M Wo\u017aniak","year":"2017","unstructured":"Wo\u017aniak M, Po\u0142ap D (2017) Hybrid neuro-heuristic methodology for simulation and control of dynamic systems over time interval. Neural Net 93:45\u201356","journal-title":"Neural Net"},{"key":"5244_CR5","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.ssci.2019.05.046","volume":"118","author":"J Zhou","year":"2019","unstructured":"Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505\u2013518","journal-title":"Saf Sci"},{"issue":"1","key":"5244_CR6","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11069-015-1842-3","volume":"79","author":"J Zhou","year":"2015","unstructured":"Zhou J, Li X, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79(1):291\u2013316","journal-title":"Nat Hazards"},{"key":"5244_CR7","first-page":"262","volume":"136","author":"M Aghaabbasi","year":"2020","unstructured":"Aghaabbasi M, Shekari ZA, Shah MZ, Olakunle O, Armaghani DJ, Moeinaddini M (2020) Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques. Transport Res A-Pol 136:262\u2013281","journal-title":"Transport Res A-Pol"},{"issue":"6","key":"5244_CR8","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, Hasanipanah M, Tarinejad R, Rashid ASA, Huynh VV (2020) On the use of neuro-swarm system to forecast the pile settlement. Appl Sci 10(6):1904","journal-title":"Appl Sci"},{"issue":"6","key":"5244_CR9","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.3390\/su12062229","volume":"12","author":"D Jahed Armaghani","year":"2020","unstructured":"Jahed Armaghani D, Asteris PG, Askarian B, Hasanipanah M, Tarinejad R, Huynh VV (2020) Examining hybrid and single SVM models with different kernels to predict rock brittleness. Sustainability 12(6):2229","journal-title":"Sustainability"},{"key":"5244_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-01003-0","author":"J Duan","year":"2020","unstructured":"Duan J, Asteris PG, Nguyen H, Bui XN, Moayedi H (2020) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-01003-0","journal-title":"Eng Comput"},{"key":"5244_CR11","doi-asserted-by":"crossref","first-page":"1350029","DOI":"10.1142\/S0129065713500299","volume":"23","author":"A Alexandridis","year":"2013","unstructured":"Alexandridis A (2013) Evolving RBF neural networks for adaptive soft-sensor design. Int J Neural Syst 23:1350029","journal-title":"Int J Neural Syst"},{"key":"5244_CR12","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/S0950-0618(01)00006-X","volume":"15","author":"WPS Dias","year":"2001","unstructured":"Dias WPS, Pooliyadda SP (2001) Neural networks for predicting properties of concretes with admixtures. Constr Build Mater 15:371\u2013379","journal-title":"Constr Build Mater"},{"key":"5244_CR13","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/S0141-0296(03)00004-X","volume":"25","author":"SC Lee","year":"2003","unstructured":"Lee SC (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25:849\u2013857","journal-title":"Eng Struct"},{"key":"5244_CR14","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.commatsci.2007.04.009","volume":"41","author":"IB Top\u00e7u","year":"2008","unstructured":"Top\u00e7u IB, Saridemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 41:305\u2013311","journal-title":"Comput Mater Sci"},{"key":"5244_CR15","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.ultras.2008.05.001","volume":"49","author":"G Trtnik","year":"2009","unstructured":"Trtnik G, Kav\u010di\u010d F, Turk G (2009) Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 49:53\u201360","journal-title":"Ultrasonics"},{"key":"5244_CR16","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1016\/S0045-7949(01)00083-9","volume":"79","author":"Z Waszczyszyn","year":"2001","unstructured":"Waszczyszyn Z, Ziemia\u0144ski L (2001) Neural networks in mechanics of structures and materials\u2014new results and prospects of applications. Comput Struct 79:2261\u20132276","journal-title":"Comput Struct"},{"key":"5244_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2368-7","author":"O Belalia Douma","year":"2016","unstructured":"Belalia Douma O, Boukhatem B, Ghrici M, Tagnit-Hamou A (2016) Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-016-2368-7","journal-title":"Neural Comput Appl"},{"key":"5244_CR18","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.conbuildmat.2016.05.034","volume":"119","author":"H Mashhadban","year":"2016","unstructured":"Mashhadban H, Kutanaei SS, Sayarinejad MA (2016) Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network. Constr Build Mater 119:277\u2013287","journal-title":"Constr Build Mater"},{"key":"5244_CR19","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s13369-014-1549-x","volume":"40","author":"M A\u00e7ikgen\u00e7","year":"2015","unstructured":"A\u00e7ikgen\u00e7 M, Ula\u015f M, Alyama\u00e7 KE (2015) Using an artificial neural network to predict mix compositions of steel fiber-reinforced concrete. Arab J Sci Eng 40:407\u2013419","journal-title":"Arab J Sci Eng"},{"key":"5244_CR20","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1080\/19648189.2016.1246693","volume":"20","author":"PG Asteris","year":"2016","unstructured":"Asteris PG, Kolovos KG, Douvika MG, Roinos K (2016) Prediction of self-compacting concrete strength using artificial neural networks. Eur J Environ Civ Eng 20:102\u2013122","journal-title":"Eur J Environ Civ Eng"},{"key":"5244_CR21","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.1016\/j.cemconres.2004.03.028","volume":"34","author":"A Baykaso\u01e7lu","year":"2004","unstructured":"Baykaso\u01e7lu A, Dereli TU, Tani\u015f S (2004) Prediction of cement strength using soft computing techniques. Cem Concr Res 34:2083\u20132090","journal-title":"Cem Concr Res"},{"key":"5244_CR22","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.cemconres.2004.01.020","volume":"34","author":"S Akkurt","year":"2004","unstructured":"Akkurt S, Tayfur G, Can S (2004) Fuzzy logic model for the prediction of cement compressive strength. Cem Concr Res 34:1429\u20131433","journal-title":"Cem Concr Res"},{"key":"5244_CR23","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.advengsoft.2009.01.005","volume":"40","author":"F \u00d6zcan","year":"2009","unstructured":"\u00d6zcan F, Ati\u015f CD, Karahan O, Uncuo\u011flu E, Tanyildizi H (2009) Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv Eng Softw 40:856\u2013863","journal-title":"Adv Eng Softw"},{"issue":"9","key":"5244_CR24","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1016\/j.advengsoft.2008.12.008","volume":"40","author":"M Saridemir","year":"2009","unstructured":"Saridemir M (2009) Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Adv Eng Softw 40(9):920\u2013927","journal-title":"Adv Eng Softw"},{"key":"5244_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.conbuildmat.2017.01.132","volume":"138","author":"H Eskandari-Naddaf","year":"2017","unstructured":"Eskandari-Naddaf H, Kazemi R (2017) ANN prediction of cement mortar compressive strength, influence of cement strength class. Constr Build Mater 138:1\u201311","journal-title":"Constr Build Mater"},{"key":"5244_CR26","doi-asserted-by":"crossref","first-page":"94","DOI":"10.3151\/jact.15.94","volume":"15","author":"T-K Oh","year":"2017","unstructured":"Oh T-K, Kim J, Lee C, Park S (2017) Nondestructive concrete strength estimation based on electro-mechanical impedance with artificial neural network. J Adv Concr Technol 15:94\u2013102","journal-title":"J Adv Concr Technol"},{"key":"5244_CR27","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/s11709-016-0363-9","volume":"11","author":"F Khademi","year":"2017","unstructured":"Khademi F, Akbari M, Jamal SM, Nikoo M (2017) Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28\u00a0days compressive strength of concrete. Front Struct Civ Eng 11:90\u201399","journal-title":"Front Struct Civ Eng"},{"key":"5244_CR28","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1002\/fam.2374","volume":"41","author":"\u0130 T\u00fcrkmen","year":"2017","unstructured":"T\u00fcrkmen \u0130, Bing\u00f6l AF, Tortum A, Demirbo\u011fa R, G\u00fcl R (2017) Properties of pumice aggregate concretes at elevated temperatures and comparison with ANN models. Fire Mater 41:142\u2013153","journal-title":"Fire Mater"},{"key":"5244_CR29","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s00366-013-0334-x","volume":"31","author":"M Nikoo","year":"2015","unstructured":"Nikoo M, Zarfam P, Sayahpour H (2015) Determination of compressive strength of concrete using Self Organization Feature Map (SOFM). Eng Comput 31:113\u2013121","journal-title":"Eng Comput"},{"key":"5244_CR30","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1111\/0885-9507.00219","volume":"16","author":"H Adeli","year":"2001","unstructured":"Adeli H (2001) Neural networks in civil engineering: 1989\u20132000. Comput-Aid Civ Infrastruct Eng 16:126\u2013142","journal-title":"Comput-Aid Civ Infrastruct Eng"},{"issue":"9","key":"5244_CR31","doi-asserted-by":"crossref","first-page":"4837","DOI":"10.1007\/s00521-018-03965-1","volume":"31","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Nikoo M (2019) Artificial Bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput Appl 31(9):4837\u20134847","journal-title":"Neural Comput Appl"},{"key":"5244_CR32","doi-asserted-by":"crossref","first-page":"396","DOI":"10.3390\/ma9050396","volume":"9","author":"M Safiuddin","year":"2016","unstructured":"Safiuddin M, Raman SN, Salam MA, Jumaat MZ (2016) Modeling of compressive strength for self-consolidating high-strength concrete incorporating palm oil fuel ash. Materials 9:396","journal-title":"Materials"},{"key":"5244_CR33","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.compositesb.2014.11.023","volume":"70","author":"I Mansouri","year":"2015","unstructured":"Mansouri I, Kisi O (2015) Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Compos Part B Eng 70:247\u2013255","journal-title":"Compos Part B Eng"},{"key":"5244_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s11709-017-0445-3","author":"TCS Reddy","year":"2017","unstructured":"Reddy TCS (2017) Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network. Front Struct Civ Eng. https:\/\/doi.org\/10.1007\/s11709-017-0445-3","journal-title":"Front Struct Civ Eng"},{"key":"5244_CR35","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.engstruct.2018.05.084","volume":"171","author":"H Salehi","year":"2018","unstructured":"Salehi H, Burgue\u00f1o R (2018) Emerging artificial intelligence methods in structural engineering. Eng Struct 171:170\u2013189","journal-title":"Eng Struct"},{"key":"5244_CR36","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1016\/j.asoc.2008.09.006","volume":"9","author":"M Zounemat-Kermani","year":"2009","unstructured":"Zounemat-Kermani M, Beheshti A-A, Ataie-Ashtiani B, Sabbagh-Yazdi S-R (2009) Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Appl Soft Comput 9:746\u2013755","journal-title":"Appl Soft Comput"},{"key":"5244_CR37","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.compgeo.2011.09.008","volume":"40","author":"AF Cabalar","year":"2012","unstructured":"Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14\u201333","journal-title":"Comput Geotech"},{"key":"5244_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/s10064-014-0638-0","author":"ET Mohamad","year":"2014","unstructured":"Mohamad ET, Jahed Armaghani D, Momeni E, Abad SVANK (2014) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ. https:\/\/doi.org\/10.1007\/s10064-014-0638-0","journal-title":"Bull Eng Geol Environ"},{"key":"5244_CR39","doi-asserted-by":"crossref","first-page":"6639","DOI":"10.1016\/j.eswa.2010.03.057","volume":"37","author":"F Soltani","year":"2010","unstructured":"Soltani F, Kerachian R, Shirangi E (2010) Developing operating rules for reservoirs considering the water quality issues: application of ANFIS-based surrogate models. Exp Syst Appl 37:6639\u20136645","journal-title":"Exp Syst Appl"},{"key":"5244_CR40","first-page":"12","volume":"23","author":"XX Ma","year":"2007","unstructured":"Ma XX, Guo HF, Chen X (2007) Water quality evaluation model based on ANFIS and its application. Water Resour Prot 23:12\u201314","journal-title":"Water Resour Prot"},{"key":"5244_CR41","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/14680629.2015.1108218","volume":"17","author":"H Ziari","year":"2016","unstructured":"Ziari H, Sobhani J, Ayoubinejad J, Hartmann T (2016) Analysing the accuracy of pavement performance models in the short and long terms: GMDH and ANFIS methods. Road Mater Pave Des 17:619\u2013637","journal-title":"Road Mater Pave Des"},{"key":"5244_CR42","doi-asserted-by":"crossref","first-page":"40","DOI":"10.31181\/oresta19012010140s","volume":"1","author":"M Stoj\u010di\u0107","year":"2018","unstructured":"Stoj\u010di\u0107 M (2018) Application of ANFIS model in road traffic and transportation: A literature review from 1993 to 2018. Oper Res Eng Sci Theory Appl 1:40\u201361","journal-title":"Oper Res Eng Sci Theory Appl"},{"key":"5244_CR43","first-page":"2001","volume":"22","author":"C \u00d6zel","year":"2015","unstructured":"\u00d6zel C, Topsakal A (2015) Comparison of ANFIS and ANN for estimation of thermal conductivity coefficients of construction materials. Sci Iran 22:2001\u20132011","journal-title":"Sci Iran"},{"key":"5244_CR44","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1007\/s00521-015-2159-6","volume":"28","author":"MM Yadollahi","year":"2017","unstructured":"Yadollahi MM, Benli A, Demirboga R (2017) Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites. Neural Comput Appl 28:1453\u20131461","journal-title":"Neural Comput Appl"},{"key":"5244_CR45","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s10661-018-6966-y","volume":"190","author":"T Abunama","year":"2018","unstructured":"Abunama T, Othman F, Younes MK (2018) Predicting sanitary landfill leachate generation in humid regions using ANFIS modeling. Environ Monit Assess 190:597","journal-title":"Environ Monit Assess"},{"key":"5244_CR46","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s10661-017-6374-8","volume":"190","author":"DY Kebria","year":"2018","unstructured":"Kebria DY, Ghavami M, Javadi S, Goharimanesh M (2018) Combining an experimental study and ANFIS modeling to predict landfill leachate transport in underlying soil\u2014a case study in north of Iran. Environ Monit Assess 190:26","journal-title":"Environ Monit Assess"},{"key":"5244_CR47","doi-asserted-by":"crossref","first-page":"679","DOI":"10.12989\/scs.2016.21.3.679","volume":"21","author":"M Safa","year":"2016","unstructured":"Safa M, Shariati M, Ibrahim Z et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam\u2019s shear strength. Steel Compos Struct 21:679\u2013688","journal-title":"Steel Compos Struct"},{"key":"5244_CR48","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3221\/IGF-ESIS.50.18","volume":"13","author":"F Jafari","year":"2019","unstructured":"Jafari F, Badarloo B (2019) Finite Element Analysis and ANFIS investigation of seismic behavior of sandwich panels with different concrete material in two story steel building. Frat ed Integrit\u00e0 Strutt 13:209\u2013230","journal-title":"Frat ed Integrit\u00e0 Strutt"},{"key":"5244_CR49","doi-asserted-by":"crossref","first-page":"809","DOI":"10.3390\/app9040809","volume":"9","author":"MA Mashrei","year":"2019","unstructured":"Mashrei MA, Mahdi AM (2019) An adaptive neuro-fuzzy inference model to predict punching shear strength of flat concrete slabs. Appl Sci 9:809","journal-title":"Appl Sci"},{"key":"5244_CR50","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.conbuildmat.2015.08.096","volume":"98","author":"KM Darain","year":"2015","unstructured":"Darain KM, Shamshirband S, Jumaat MZ, Obaydullah M (2015) Adaptive neuro fuzzy prediction of deflection and cracking behavior of NSM strengthened RC beams. Constr Build Mater 98:276\u2013285","journal-title":"Constr Build Mater"},{"key":"5244_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-019-00118-2","author":"H Naderpour","year":"2019","unstructured":"Naderpour H, Mirrashid M (2019) Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Compl Intell Syst. https:\/\/doi.org\/10.1007\/s40747-019-00118-2","journal-title":"Compl Intell Syst"},{"issue":"15","key":"5244_CR52","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1016\/j.engfracmech.2003.12.004","volume":"71","author":"R Ince","year":"2004","unstructured":"Ince R (2004) Prediction of fracture parameters of concrete by Artificial Neural Networks. Eng Fract Mech 71(15):2143\u20132159","journal-title":"Eng Fract Mech"},{"issue":"9","key":"5244_CR53","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.conbuildmat.2005.01.047","volume":"20","author":"BB Adhikary","year":"2006","unstructured":"Adhikary BB, Mutsuyoshi H (2006) Prediction of shear strength of steel fiber RC beams using neural networks. Constr Build Mater 20(9):801\u2013811","journal-title":"Constr Build Mater"},{"issue":"3","key":"5244_CR54","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.autcon.2005.07.003","volume":"15","author":"MA Kewalramani","year":"2006","unstructured":"Kewalramani MA, Gupta R (2006) Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Autom Constr 15(3):374\u2013379","journal-title":"Autom Constr"},{"issue":"2","key":"5244_CR55","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.conbuildmat.2005.08.009","volume":"21","author":"M Pala","year":"2007","unstructured":"Pala M, \u00d6zbay E, \u00d6zta\u015f A, Yuce MI (2007) Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Constr Build Mater 21(2):384\u2013394","journal-title":"Constr Build Mater"},{"issue":"1","key":"5244_CR56","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.commatsci.2007.03.010","volume":"41","author":"IB Top\u00e7u","year":"2007","unstructured":"Top\u00e7u IB, Saridemir M (2007) Prediction of properties of waste AAC aggregate concrete using artificial neural network. Comput Mater Sci 41(1):117\u2013125","journal-title":"Comput Mater Sci"},{"issue":"7","key":"5244_CR57","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1016\/j.conbuildmat.2007.04.004","volume":"22","author":"F Demir","year":"2008","unstructured":"Demir F (2008) Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Constr Build Mater 22(7):1428\u20131435","journal-title":"Constr Build Mater"},{"issue":"2","key":"5244_CR58","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.commatsci.2007.07.011","volume":"42","author":"F Altun","year":"2008","unstructured":"Altun F, Ki\u015fi O, Aydin K (2008) Predicting the compressive strength of steel fiber added lightweight concrete using neural network. Comput Mater Sci 42(2):259\u2013265","journal-title":"Comput Mater Sci"},{"issue":"6","key":"5244_CR59","first-page":"627","volume":"20","author":"U Gazder","year":"2017","unstructured":"Gazder U, Al-Amoudi OSB, Saad Khan SM, Maslehuddin M (2017) Predicting compressive strength of blended cement concrete with ANNs. Comput Concr 20(6):627\u2013634","journal-title":"Comput Concr"},{"key":"5244_CR60","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.conbuildmat.2018.08.079","volume":"187","author":"EK Onyari","year":"2018","unstructured":"Onyari EK, Ikotun BD (2018) Prediction of compressive and flexural strengths of a modified zeolite additive mortar using artificial neural network. Constr Build Mater 187:1232\u20131241","journal-title":"Constr Build Mater"},{"key":"5244_CR61","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.jobe.2018.05.012","volume":"19","author":"H Naderpour","year":"2018","unstructured":"Naderpour H, Mirrashid M (2018) An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals. J Build Eng 19:205\u2013215","journal-title":"J Build Eng"},{"key":"5244_CR62","volume-title":"Introduction to artificial neural systems","author":"JM Zurada","year":"1992","unstructured":"Zurada JM (1992) Introduction to artificial neural systems. West St, Paul"},{"issue":"2","key":"5244_CR63","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s00521-015-2072-z","volume":"28","author":"DJ Armaghani","year":"2017","unstructured":"Armaghani DJ, Raja RSNSB, 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","journal-title":"Neural Comput Appl"},{"issue":"5","key":"5244_CR64","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, Yazdavar AH, Ebrahimi M (2018) Rock strength estimation: a PSO-based BP approach. Neural Comput Appl 30(5):1635\u20131646","journal-title":"Neural Comput Appl"},{"key":"5244_CR65","first-page":"2501","volume":"5","author":"ET Mohamad","year":"2012","unstructured":"Mohamad ET, Hajihassani M, Armaghani DJ, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Modell Simul 5:2501\u20132506","journal-title":"Int Rev Modell Simul"},{"key":"5244_CR66","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, New Jersey"},{"key":"5244_CR67","volume-title":"Neural networks: methodology and application","author":"G Dreyfus","year":"2005","unstructured":"Dreyfus G (2005) Neural networks: methodology and application. Springer, Berlin"},{"key":"5244_CR68","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"J-S Jang","year":"1993","unstructured":"Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665\u2013685","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"5244_CR69","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1109\/5.364486","volume":"83","author":"J-S Jang","year":"1995","unstructured":"Jang J-S, Sun C-T (1995) Neuro-fuzzy modeling and control. Proc IEEE 83:378\u2013406","journal-title":"Proc IEEE"},{"key":"5244_CR70","first-page":"76","volume":"76","author":"OAM Ali","year":"2015","unstructured":"Ali OAM, Ali AY, Sumait BS (2015) Comparison between the effects of different types of membership functions on fuzzy logic controller performance. Int J 76:76\u201383","journal-title":"Int J"},{"key":"5244_CR71","first-page":"13","volume":"123","author":"N Walia","year":"2015","unstructured":"Walia N, Singh H, Sharma A (2015) ANFIS: adaptive neuro-fuzzy inference system-a survey. Int J Comput Appl 123:13","journal-title":"Int J Comput Appl"},{"key":"5244_CR72","doi-asserted-by":"publisher","first-page":"10937","DOI":"10.1007\/s12517-015-1984-3","volume":"8","author":"DJ Armaghani","year":"2015","unstructured":"Armaghani DJ, Hajihassani M, Sohaei H et al (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 8:10937\u201310950. https:\/\/doi.org\/10.1007\/s12517-015-1984-3","journal-title":"Arab J Geosci"},{"key":"5244_CR73","doi-asserted-by":"crossref","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 (2018) Prediction of bearing capacity of thin-walled foundation: a simulation approach. Eng Comput 34:319\u2013327","journal-title":"Eng Comput"},{"issue":"6","key":"5244_CR74","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0958-9465(00)00091-3","volume":"23","author":"DD Vu","year":"2001","unstructured":"Vu DD, Stroeven P, Bui VB (2001) Strength and durability aspects of calcined kaolin-blended Portland cement mortar and concrete. Cem Concr Compos 23(6):471\u2013478","journal-title":"Cem Concr Compos"},{"issue":"9","key":"5244_CR75","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1016\/S0008-8846(03)00090-5","volume":"33","author":"L Courard","year":"2003","unstructured":"Courard L, Darimont A, Schouterden M, Ferauche F, Willem X, Degeimbre R (2003) Durability of mortars modified with metakaolin. Cem Concr Res 33(9):1473\u20131479","journal-title":"Cem Concr Res"},{"issue":"3","key":"5244_CR76","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.conbuildmat.2006.10.003","volume":"22","author":"AK Parande","year":"2008","unstructured":"Parande AK, Ramesh Babu B, AswinKarthik M, Deepak Kumaar KK, Palaniswamy N (2008) Study on strength and corrosion performance for steel embedded in metakaolin blended concrete\/mortar. Constr Build Mater 22(3):127\u2013134","journal-title":"Constr Build Mater"},{"issue":"3","key":"5244_CR77","first-page":"501","volume":"4","author":"C Sumasree","year":"2016","unstructured":"Sumasree C, Sajja S (2016) Effect of Metakaolin and Cerafibermix on mechanical and durability properties of mortars. Int J Sci Eng Technol 4(3):501\u2013506","journal-title":"Int J Sci Eng Technol"},{"issue":"1","key":"5244_CR78","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.cemconcomp.2004.02.041","volume":"27","author":"G Batis","year":"2005","unstructured":"Batis G, Pantazopoulou P, Tsivilis S, Badogiannis E (2005) The effect of metakaolin on the corrosion behavior of cement mortars. Cem Concr Compos 27(1):125\u2013130","journal-title":"Cem Concr Compos"},{"issue":"4","key":"5244_CR79","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/j.clay.2011.06.008","volume":"53","author":"EH Kadri","year":"2011","unstructured":"Kadri EH, Kenai S, Ezziane K, Siddique R, De Schutter G (2011) Influence of metakaolin and silica fume on the heat of hydration and compressive strength development of mortar. Appl Clay Sci 53(4):704\u2013708","journal-title":"Appl Clay Sci"},{"key":"5244_CR80","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.conbuildmat.2014.07.089","volume":"70","author":"A Mardani-Aghabaglou","year":"2014","unstructured":"Mardani-Aghabaglou A, Sezer G\u0130, Ramyar K (2014) Comparison of fly ash, silica fume and metakaolin from mechanical properties and durability performance of mortar mixtures view point. Constr Build Mater 70:17\u201325","journal-title":"Constr Build Mater"},{"issue":"4","key":"5244_CR81","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1061\/(ASCE)0899-1561(2006)18:4(619)","volume":"18","author":"SS Potgieter-Vermaak","year":"2006","unstructured":"Potgieter-Vermaak SS, Potgieter JH (2006) Metakaolin as an extender in South African cement. J Mater Civ Eng 18(4):619\u2013623","journal-title":"J Mater Civ Eng"},{"key":"5244_CR82","doi-asserted-by":"crossref","unstructured":"Cizer O, Van Balen K, Van Gemert D, Elsen J (2008) Blended lime-cement mortars for conservation purposes: microstructure and strength development. In: Structural analysis of historic construction: preserving safety and significance\u2014proceedings of the 6th international conference on structural analysis of historic construction, SAHC08, 2, pp 965\u2013972","DOI":"10.1201\/9781439828229.ch110"},{"key":"5244_CR83","doi-asserted-by":"crossref","first-page":"33","DOI":"10.2174\/1874836801307010033","volume":"7","author":"GK Al-Chaar","year":"2013","unstructured":"Al-Chaar GK, Alkadi M, Asteris PG (2013) Natural pozzolan as a partial substitute for cement in concrete. Open Constr Build Technol J 7:33\u201342","journal-title":"Open Constr Build Technol J"},{"key":"5244_CR84","unstructured":"SPSS Inc (2007) SPSS for windows (version 16.0). SPSS Inc., Chicago"},{"issue":"9","key":"5244_CR85","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s12665-016-5524-6","volume":"75","author":"M Khandelwal","year":"2016","unstructured":"Khandelwal M, Armaghani DJ, Faradonbeh RS, Ranjith PG, Ghoraba S (2016) A new model based on gene expression programming to estimate air flow in a single rock joint. Environ Earth Sci 75(9):739","journal-title":"Environ Earth Sci"},{"issue":"1","key":"5244_CR86","doi-asserted-by":"crossref","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(1):85\u201393","journal-title":"Earth Sci Res J"},{"issue":"12","key":"5244_CR87","doi-asserted-by":"crossref","first-page":"10937","DOI":"10.1007\/s12517-015-1984-3","volume":"8","author":"DJ Armaghani","year":"2015","unstructured":"Armaghani DJ, Hajihassani M, Sohaei H, Mohamad ET, Marto A, Motaghedi H, Moghaddam MR (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 8(12):10937\u201310950","journal-title":"Arab J Geosci"},{"issue":"6","key":"5244_CR88","first-page":"629","volume":"31","author":"H Yang","year":"2019","unstructured":"Yang H, Koopialipoor M, Armaghani DJ, Gordan B, Khorami M, Tahir MM (2019) Intelligent design of retaining wall structures under dynamic conditions. Steel Compos Struct 31(6):629\u2013640","journal-title":"Steel Compos Struct"},{"key":"5244_CR89","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00849-3","author":"H Harandizadeh","year":"2019","unstructured":"Harandizadeh H, Armaghani DJ, Khari M (2019) A new development of ANFIS\u2013GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00849-3","journal-title":"Eng Comput"},{"key":"5244_CR90","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00752-x","author":"W Chen","year":"2019","unstructured":"Chen W, Sarir P, Bui XN, Nguyen H, Tahir MM, Armaghani DJ (2019) 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":"18","key":"5244_CR91","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.3390\/app9183715","volume":"9","author":"H Xu","year":"2019","unstructured":"Xu H, Zhou J, Asteris GP, 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":"5244_CR92","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-020-09676-6","author":"BR Murlidhar","year":"2020","unstructured":"Murlidhar BR, Kumar D, Jahed Armaghani D, Mohamad ET, Roy B, Pham BT (2020) A novel intelligent ELM-BBO technique for predicting distance of mine blasting-induced flyrock. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-020-09676-6","journal-title":"Nat Resour Res"},{"key":"5244_CR93","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1016\/j.prostr.2019.08.123","volume":"17","author":"DJ Armaghani","year":"2019","unstructured":"Armaghani DJ, Hatzigeorgiou GD, Karamani Ch, Skentou A, Zoumpoulaki I, Asteris PG (2019) Soft computing based techniques for concrete beams shear strength. Proc Struct Integr 17:924\u2013933. https:\/\/doi.org\/10.1016\/j.prostr.2019.08.123","journal-title":"Proc Struct Integr"},{"issue":"5","key":"5244_CR94","doi-asserted-by":"publisher","first-page":"469","DOI":"10.12989\/cac.2019.24.5.469","volume":"24","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Armaghani DJ, Hatzigeorgiou GD, Karayannis CG, Pilakoutas K (2019) Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks. Comput Concr 24(5):469\u2013488. https:\/\/doi.org\/10.12989\/cac.2019.24.5.469","journal-title":"Comput Concr"},{"key":"5244_CR95","first-page":"914","volume":"17","author":"M Apostolopoulou","year":"2019","unstructured":"Apostolopoulou M, Armaghani DJ, Bakolas A, Douvika MG, Moropoulou A, Asteris PG (2019) Compressive strength of natural hydraulic lime mortars using soft computing techniques. Proc Struct Integr 17:914\u2013923","journal-title":"Proc Struct Integr"},{"issue":"2","key":"5244_CR96","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/app9020243","volume":"9","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Moropoulou A, Skentou AD, Apostolopoulou M, Mohebkhah A, Cavaleri L, Rodrigues H, Varum H (2019) Stochastic vulnerability assessment of masonry structures: concepts, modeling and restoration aspects. Appl Sci 9(2):243","journal-title":"Appl Sci"},{"key":"5244_CR97","doi-asserted-by":"crossref","first-page":"5372","DOI":"10.3390\/app9245372","volume":"9","author":"L Huang","year":"2019","unstructured":"Huang L, Asteris PG, Koopialipoor M, Armaghani DJ, Tahir MM (2019) Invasive weed optimization technique-based ANN to the prediction of rock tensile strength. Appl Sci 9:5372","journal-title":"Appl Sci"},{"key":"5244_CR98","doi-asserted-by":"crossref","unstructured":"Asteris PG, Argyropoulos I, Cavaleri L, Rodrigues H, Varum H, Thomas J, Louren\u00e7o PB (2018) Masonry compressive strength prediction using artificial neural networks. In International conference on transdisciplinary multispectral modeling and cooperation for the preservation of cultural heritage, Springer, Cham, Switzerland, pp 200\u2013224","DOI":"10.1007\/978-3-030-12960-6_14"},{"issue":"2","key":"5244_CR99","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(2):137\u2013150","journal-title":"Comput Concr"},{"issue":"13","key":"5244_CR100","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1080\/15376494.2018.1430874","volume":"26","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Nozhati S, Nikoo M, Cavaleri L, Nikoo M (2019) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 26(13):1146\u20131153","journal-title":"Mech Adv Mater Struct"},{"issue":"6","key":"5244_CR101","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.3390\/s17061344","volume":"17","author":"PG Asteris","year":"2017","unstructured":"Asteris PG, Roussis PC, Douvika MG (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17(6):1344","journal-title":"Sensors"},{"key":"5244_CR102","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, Douvika MG, Louren\u00e7o PB, Cavaleri L, Bakolas A, Moropoulou A (2020) Mapping and holistic design of natural hydraulic lime mortars. Cem Concr Res 136:106167. https:\/\/doi.org\/10.1016\/j.cemconres.2020.106167","journal-title":"Cem Concr Res"},{"key":"5244_CR103","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05214-w","author":"H Ly","year":"2020","unstructured":"Ly H, Pham BT, Le LM et al (2020) Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05214-w","journal-title":"Neural Comput Appl"},{"key":"5244_CR104","doi-asserted-by":"publisher","first-page":"1807","DOI":"10.1007\/s00521-019-04663-2","volume":"32","author":"PG Asteris","year":"2020","unstructured":"Asteris PG, Mokos VG (2020) Concrete compressive strength using artificial neural networks. Neural Comput Appl 32:1807\u201311826. https:\/\/doi.org\/10.1007\/s00521-019-04663-2","journal-title":"Neural Comput Appl"},{"issue":"4","key":"5244_CR105","first-page":"329","volume":"24","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Apostolopoulou M, Skentou AD, Antonia Moropoulou A (2019) Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars. Comput Concr 24(4):329\u2013345","journal-title":"Comput Concr"},{"key":"5244_CR106","unstructured":"Tutmez B, Dag A, Tercan AE, Kaymak U (2007) Lignite thickness estimation via adaptive fuzzy-neural network. In: Proceedings of the 20th international mining congress and exhibition of Turkey (IMCET 2007), pp 151\u2013157"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05244-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05244-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05244-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:01:29Z","timestamp":1628553689000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05244-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,10]]},"references-count":106,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["5244"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05244-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,10]]},"assertion":[{"value":"12 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors confirm that this article content has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}