{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T08:44:45Z","timestamp":1779180285918,"version":"3.51.4"},"reference-count":178,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T00:00:00Z","timestamp":1595635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T00:00:00Z","timestamp":1595635200000},"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":[[2021,4]]},"DOI":"10.1007\/s00521-020-05214-w","type":"journal-article","created":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T19:02:33Z","timestamp":1595703753000},"page":"3437-3458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models"],"prefix":"10.1007","volume":"33","author":[{"given":"Hai-Bang","family":"Ly","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9707-840X","authenticated-orcid":false,"given":"Binh Thai","family":"Pham","sequence":"additional","affiliation":[]},{"given":"Lu Minh","family":"Le","sequence":"additional","affiliation":[]},{"given":"Tien-Thinh","family":"Le","sequence":"additional","affiliation":[]},{"given":"Vuong Minh","family":"Le","sequence":"additional","affiliation":[]},{"given":"Panagiotis G.","family":"Asteris","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,25]]},"reference":[{"key":"5214_CR1","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1080\/13632469.2015.1104752","volume":"20","author":"H G\u00fcll\u00fc","year":"2016","unstructured":"G\u00fcll\u00fc H, Recep \u0130 (2016) A seismic hazard study through the comparison of ground motion prediction equations using the weighting factor of logic tree. J Earthq Eng 20:861\u2013884","journal-title":"J Earthq Eng"},{"key":"5214_CR2","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s11069-014-1039-1","volume":"72","author":"H G\u00fcll\u00fc","year":"2014","unstructured":"G\u00fcll\u00fc H, Murat P (2014) On the resonance effect by dynamic soil structure interaction a revelation study. Nat Hazards 72:827\u2013847","journal-title":"Nat Hazards"},{"key":"5214_CR3","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s11069-007-9140-3","volume":"44","author":"H G\u00fcll\u00fc","year":"2008","unstructured":"G\u00fcll\u00fc H, Atilla MA, Aydin \u00d6 (2008) Seismic hazard studies for Gaziantep city in South Anatolia of Turkey. Nat Hazards 44:19\u201350","journal-title":"Nat Hazards"},{"key":"5214_CR4","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.conbuildmat.2016.09.129","volume":"127","author":"H G\u00fcll\u00fc","year":"2016","unstructured":"G\u00fcll\u00fc H (2016) Comparison of rheological models for jet grout cement mixtures with various stabilizers. Constr Build Mater 127:220\u2013236","journal-title":"Constr Build Mater"},{"key":"5214_CR5","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s12665-012-2167-0","volume":"70","author":"H G\u00fcll\u00fc","year":"2013","unstructured":"G\u00fcll\u00fc H, Serkan G (2013) Performance of fine-grained soil treated with industrial wastewater sludge. Environ Earth Sci 70:777\u2013788","journal-title":"Environ Earth Sci"},{"key":"5214_CR6","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.conbuildmat.2018.11.140","volume":"196","author":"H G\u00fcll\u00fc","year":"2019","unstructured":"G\u00fcll\u00fc H, Cevik A, Al-Ezzi KM, G\u00fclsan ME (2019) On the rheology of using geopolymer for grouting: a comparative study with cement-based grout included fly ash and cold bonded fly ash. Constr Build Mater 196:594\u2013610","journal-title":"Constr Build Mater"},{"key":"5214_CR7","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.tws.2015.06.009","volume":"95","author":"J Yang","year":"2015","unstructured":"Yang J, Sheehan T, Dai XH, Lam D (2015) Experimental study of beam to concrete-filled elliptical steel tubular column connections. Thin-Walled Struct 95:16\u201323","journal-title":"Thin-Walled Struct"},{"key":"5214_CR8","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.tws.2010.10.008","volume":"49","author":"A Espinos","year":"2011","unstructured":"Espinos A, Gardner L, Romero ML, Hospitaler A (2011) Fire behaviour of concrete filled elliptical steel columns. Thin-Walled Struct 49:239\u2013255","journal-title":"Thin-Walled Struct"},{"key":"5214_CR9","doi-asserted-by":"publisher","DOI":"10.1080\/15376494.2018.1430874","author":"PG Asteris","year":"2018","unstructured":"Asteris PG, Nozhati S, Nikoo M et al (2018) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct. https:\/\/doi.org\/10.1080\/15376494.2018.1430874","journal-title":"Mech Adv Mater Struct"},{"key":"5214_CR10","unstructured":"Bergmann R, Matsui C, Meinsma C, Dutta D (1995) Design guide for concrete filled hollow section columns under static and seismic loading. In: CIDECT design guide 5, CIDECT"},{"issue":"20\u201320","key":"5214_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1155\/2016\/5104907","volume":"20","author":"PG Asteris","year":"2016","unstructured":"Asteris PG, Tsaris AK, Cavaleri L et al (2016) Prediction of the fundamental period of infilled RC frame structures using artificial neural networks. Intell Neurosci 20(20\u201320):20. https:\/\/doi.org\/10.1155\/2016\/5104907","journal-title":"Intell Neurosci"},{"key":"5214_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-03965-1","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. https:\/\/doi.org\/10.1007\/s00521-018-03965-1","journal-title":"Neural Comput Appl"},{"key":"5214_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jcsr.2010.07.003","volume":"67","author":"SH Lee","year":"2011","unstructured":"Lee SH, Uy B, Kim SH et al (2011) Behavior of high-strength circular concrete-filled steel tubular (CFST) column under eccentric loading. J Constr Steel Res 67:1\u201313","journal-title":"J Constr Steel Res"},{"key":"5214_CR14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jcsr.2011.11.012","volume":"71","author":"LH Han","year":"2012","unstructured":"Han LH, Hou C, Wang QL (2012) Square concrete filled steel tubular (CFST) members under loading and chloride corrosion: experiments. J Constr Steel Res 71:11\u201325","journal-title":"J Constr Steel Res"},{"key":"5214_CR15","first-page":"99","volume":"32","author":"J Nie","year":"2011","unstructured":"Nie J, Huang J, Fan J (2011) Experimental study on load-bearing behavior of rectangular CFST frame considering composite action of floor slab. J Build Struct 32:99\u2013108","journal-title":"J Build Struct"},{"key":"5214_CR16","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.jcsr.2014.03.010","volume":"99","author":"Q-X Ren","year":"2014","unstructured":"Ren Q-X, Han L-H, Lam D, Li W (2014) Tests on elliptical concrete filled steel tubular (CFST) beams and columns. J Constr Steel Res 99:149\u2013160. https:\/\/doi.org\/10.1016\/j.jcsr.2014.03.010","journal-title":"J Constr Steel Res"},{"key":"5214_CR17","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.jcsr.2013.04.002","volume":"87","author":"N Jamaluddin","year":"2013","unstructured":"Jamaluddin N, Lam D, Dai XH, Ye J (2013) An experimental study on elliptical concrete filled columns under axial compression. J Constr Steel Res 87:6\u201316. https:\/\/doi.org\/10.1016\/j.jcsr.2013.04.002","journal-title":"J Constr Steel Res"},{"key":"5214_CR18","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1260\/1369-4332.13.3.493","volume":"13","author":"D Lam","year":"2010","unstructured":"Lam D, Gardner L, Burdett M (2010) Behaviour of axially loaded concrete filled stainless steel elliptical stub columns. Adv Struct Eng 13:493\u2013500. https:\/\/doi.org\/10.1260\/1369-4332.13.3.493","journal-title":"Adv Struct Eng"},{"key":"5214_CR19","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.tws.2013.11.015","volume":"77","author":"XH Dai","year":"2014","unstructured":"Dai XH, Lam D, Jamaluddin N, Ye J (2014) Numerical analysis of slender elliptical concrete filled columns under axial compression. Thin-Walled Struct 77:26\u201335. https:\/\/doi.org\/10.1016\/j.tws.2013.11.015","journal-title":"Thin-Walled Struct"},{"key":"5214_CR20","unstructured":"AS3600 AS (2001) Concrete structures. Standards Australia, Sydney"},{"key":"5214_CR21","unstructured":"AS4100 AS (1998) Steel structures. Standards Australia, NSW, Australia"},{"key":"5214_CR22","volume-title":"Specification for structural steel buildings ANSI\/AISC 360-16","author":"AISC","year":"2010","unstructured":"AISC (2010) Specification for structural steel buildings ANSI\/AISC 360-16. American Institute of Steel Construction, Chicago"},{"key":"5214_CR23","volume-title":"Standards for Structural Calculation of Steel Reinforced Concrete Structures","author":"AIJ A","year":"2001","unstructured":"AIJ A (2001) Standards for Structural Calculation of Steel Reinforced Concrete Structures, 5th edn. Architectural Institute of Japan, Tokyo","edition":"5"},{"key":"5214_CR24","unstructured":"Eurocode 4 (2004) Design of composite steel and concrete structures. Part 1.1, General rules and rules for buildings. European Committee for Standardization, British Standards Institution, London, UK"},{"key":"5214_CR25","unstructured":"Chinese Code DLT (1999) Chinese design code for steel-concrete composite structures. DL\/T 5085-1999, Chinese Electricity Press, Beijing, China"},{"key":"5214_CR26","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1016\/j.jcsr.2003.10.001","volume":"60","author":"G Giakoumelis","year":"2004","unstructured":"Giakoumelis G, Lam D (2004) Axial capacity of circular concrete-filled tube columns. J Constr Steel Res 60:1049\u20131068. https:\/\/doi.org\/10.1016\/j.jcsr.2003.10.001","journal-title":"J Constr Steel Res"},{"key":"5214_CR27","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.jcsr.2009.12.014","volume":"66","author":"Z-H Lu","year":"2010","unstructured":"Lu Z-H, Zhao Y-G (2010) Suggested empirical models for the axial capacity of circular CFT stub columns. J Constr Steel Res 66:850\u2013862","journal-title":"J Constr Steel Res"},{"key":"5214_CR28","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1016\/j.tws.2004.03.016","volume":"42","author":"L-H Han","year":"2004","unstructured":"Han L-H, Yao G-H (2004) Experimental behaviour of thin-walled hollow structural steel (HSS) columns filled with self-consolidating concrete (SCC). Thin-Walled Struct 42:1357\u20131377","journal-title":"Thin-Walled Struct"},{"key":"5214_CR29","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1061\/(ASCE)0733-9445(2004)130:2(180)","volume":"130","author":"Sakino Kenji","year":"2004","unstructured":"Kenji Sakino, Hiroyuki Nakahara, Shosuke Morino, Isao Nishiyama (2004) Behavior of centrally loaded concrete-filled steel-tube short columns. J Struct Eng 130:180\u2013188. https:\/\/doi.org\/10.1061\/(ASCE)0733-9445(2004)130:2(180)","journal-title":"J Struct Eng"},{"key":"5214_CR30","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1016\/j.engstruct.2007.11.001","volume":"30","author":"GD Hatzigeorgiou","year":"2008","unstructured":"Hatzigeorgiou GD (2008) Numerical model for the behavior and capacity of circular CFT columns, Part I: theory. Eng Struct 30:1573\u20131578. https:\/\/doi.org\/10.1016\/j.engstruct.2007.11.001","journal-title":"Eng Struct"},{"key":"5214_CR31","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1016\/j.engstruct.2007.11.002","volume":"30","author":"GD Hatzigeorgiou","year":"2008","unstructured":"Hatzigeorgiou GD (2008) Numerical model for the behavior and capacity of circular CFT columns, Part II: verification and extension. Eng Struct 30:1579\u20131589. https:\/\/doi.org\/10.1016\/j.engstruct.2007.11.002","journal-title":"Eng Struct"},{"key":"5214_CR32","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s13296-016-3009-9","volume":"16","author":"EM G\u00fcneyisi","year":"2016","unstructured":"G\u00fcneyisi EM, G\u00fcltekin A, Mermerda\u015f K (2016) Ultimate capacity prediction of axially loaded CFST short columns. Int J Steel Struct 16:99\u2013114. https:\/\/doi.org\/10.1007\/s13296-016-3009-9","journal-title":"Int J Steel Struct"},{"key":"5214_CR33","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s12665-017-6689-3","volume":"76","author":"BT Pham","year":"2017","unstructured":"Pham BT, Bui DT, Prakash I et al (2017) A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS. Environ Earth Sci 76:371","journal-title":"Environ Earth Sci"},{"key":"5214_CR34","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.ecoinf.2017.12.006","volume":"43","author":"A Jaafari","year":"2018","unstructured":"Jaafari A, Zenner EK, Pham BT (2018) Wildfire spatial pattern analysis in the Zagros Mountains, Iran: a comparative study of decision tree based classifiers. Ecol Inform 43:200\u2013211","journal-title":"Ecol Inform"},{"key":"5214_CR35","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.scitotenv.2019.05.061","volume":"679","author":"BT Pham","year":"2019","unstructured":"Pham BT, Nguyen MD, Dao DV et al (2019) Development of artificial intelligence models for the prediction of compression coefficient of soil: an application of monte carlo sensitivity analysis. Sci Total Environ 679:172\u2013184. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.05.061","journal-title":"Sci Total Environ"},{"key":"5214_CR36","doi-asserted-by":"publisher","first-page":"983","DOI":"10.3390\/ma12060983","volume":"12","author":"DV Dao","year":"2019","unstructured":"Dao DV, Ly H-B, Trinh SH et al (2019) Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials 12:983. https:\/\/doi.org\/10.3390\/ma12060983","journal-title":"Materials"},{"key":"5214_CR37","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.3390\/app9061113","volume":"9","author":"DV Dao","year":"2019","unstructured":"Dao DV, Trinh SH, Ly H-B, Pham BT (2019) Prediction of compressive strength of geopolymer concrete using entirely steel slag aggregates: novel hybrid artificial intelligence approaches. Appl Sci 9:1113. https:\/\/doi.org\/10.3390\/app9061113","journal-title":"Appl Sci"},{"key":"5214_CR38","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.3390\/ma12091544","volume":"12","author":"H-B Ly","year":"2019","unstructured":"Ly H-B, Monteiro E, Le T-T et al (2019) Prediction and sensitivity analysis of bubble dissolution time in 3D selective laser sintering using ensemble decision trees. Materials 12:1544. https:\/\/doi.org\/10.3390\/ma12091544","journal-title":"Materials"},{"key":"5214_CR39","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.enggeo.2012.05.010","volume":"141","author":"H G\u00fcll\u00fc","year":"2012","unstructured":"G\u00fcll\u00fc H (2012) Prediction of peak ground acceleration by genetic expression programming and regression: a comparison using likelihood-based measure. Eng Geol 141:92\u2013113","journal-title":"Eng Geol"},{"key":"5214_CR40","doi-asserted-by":"publisher","first-page":"2258","DOI":"10.3390\/app9112258","volume":"9","author":"H-B Ly","year":"2019","unstructured":"Ly H-B, Le LM, Duong HT et al (2019) Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections. Appl Sci 9:2258. https:\/\/doi.org\/10.3390\/app9112258","journal-title":"Appl Sci"},{"key":"5214_CR41","doi-asserted-by":"publisher","first-page":"1670","DOI":"10.3390\/ma12101670","volume":"12","author":"LM Le","year":"2019","unstructured":"Le LM, Ly H-B, Pham BT et al (2019) Hybrid artificial intelligence approaches for predicting buckling damage of steel columns under axial compression. Materials 12:1670. https:\/\/doi.org\/10.3390\/ma12101670","journal-title":"Materials"},{"key":"5214_CR42","doi-asserted-by":"publisher","first-page":"3841","DOI":"10.3390\/app9183841","volume":"9","author":"H-B Ly","year":"2019","unstructured":"Ly H-B, Pham BT, Dao DV et al (2019) Improvement of ANFIS model for prediction of compressive strength of manufactured sand concrete. Appl Sci 9:3841. https:\/\/doi.org\/10.3390\/app9183841","journal-title":"Appl Sci"},{"key":"5214_CR43","doi-asserted-by":"publisher","first-page":"830","DOI":"10.3390\/su12030830","volume":"12","author":"DV Dao","year":"2020","unstructured":"Dao DV, Adeli H, Ly H-B et al (2020) A Sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a Monte Carlo simulation. Sustainability 12:830. https:\/\/doi.org\/10.3390\/su12030830","journal-title":"Sustainability"},{"key":"5214_CR44","doi-asserted-by":"publisher","first-page":"5458","DOI":"10.3390\/app9245458","volume":"9","author":"H-B Ly","year":"2019","unstructured":"Ly H-B, Le T-T, Le LM et al (2019) Development of hybrid machine learning models for predicting the critical buckling load of I-shaped cellular beams. Appl Sci 9:5458. https:\/\/doi.org\/10.3390\/app9245458","journal-title":"Appl Sci"},{"key":"5214_CR45","doi-asserted-by":"crossref","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":"5214_CR46","first-page":"1","volume":"18","author":"J Duan","year":"2020","unstructured":"Duan J, Asteris PG, Nguyen H et al (2020) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput 18:1\u201318","journal-title":"Eng Comput"},{"key":"5214_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04663-2","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Mokos VG (2019) Concrete compressive strength using artificial neural networks. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04663-2","journal-title":"Neural Comput Appl"},{"key":"5214_CR48","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.sandf.2017.05.006","volume":"57","author":"H G\u00fcll\u00fc","year":"2017","unstructured":"G\u00fcll\u00fc H (2017) A new prediction method for the rheological behavior of grout with bottom ash for jet grouting columns. Soils Found 57:384\u2013396","journal-title":"Soils Found"},{"key":"5214_CR49","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s00521-016-2360-2","volume":"28","author":"H G\u00fcll\u00fc","year":"2017","unstructured":"G\u00fcll\u00fc H (2017) A novel approach to prediction of rheological characteristics of jet grout cement mixtures via genetic expression programming. Neural Comput Appl 28:407\u2013420","journal-title":"Neural Comput Appl"},{"key":"5214_CR50","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s10518-013-9425-8","volume":"11","author":"H G\u00fcll\u00fc","year":"2013","unstructured":"G\u00fcll\u00fc H (2013) On the prediction of shear wave velocity at local site of strong ground motion stations: an application using artificial intelligence. Bull Earthq Eng 11:969\u2013997","journal-title":"Bull Earthq Eng"},{"key":"5214_CR51","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.engappai.2014.06.020","volume":"35","author":"H G\u00fcll\u00fc","year":"2014","unstructured":"G\u00fcll\u00fc H (2014) Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom Ash. Eng Appl Artif Intell 35:143\u2013157","journal-title":"Eng Appl Artif Intell"},{"key":"5214_CR52","doi-asserted-by":"crossref","first-page":"441","DOI":"10.12989\/gae.2017.12.3.441","volume":"12","author":"H G\u00fcll\u00fc","year":"2017","unstructured":"G\u00fcll\u00fc H, Fedakar HI (2017) On the prediction of unconfined compressive strength of silty soil stabilized with bottom ash, jute and steel fibers via artificial intelligence. Geomech Eng 12:441\u2013464","journal-title":"Geomech Eng"},{"key":"5214_CR53","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1016\/j.scitotenv.2018.06.130","volume":"642","author":"K Khosravi","year":"2018","unstructured":"Khosravi K, Sartaj M, Tsai FT-C et al (2018) A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Sci Total Environ 642:1032\u20131049","journal-title":"Sci Total Environ"},{"key":"5214_CR54","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.scitotenv.2019.02.422","volume":"668","author":"DT Bui","year":"2019","unstructured":"Bui DT, Tsangaratos P, Ngo P-TT et al (2019) Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 668:1038\u20131054","journal-title":"Sci Total Environ"},{"key":"5214_CR55","first-page":"1","volume":"12","author":"BT Pham","year":"2019","unstructured":"Pham BT, Prakash I, Dou J et al (2019) A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int 12:1\u201325","journal-title":"Geocarto Int"},{"key":"5214_CR56","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1007\/s12524-018-0791-1","volume":"46","author":"BT Pham","year":"2018","unstructured":"Pham BT, Prakash I, Jaafari A, Bui DT (2018) Spatial prediction of rainfall-induced landslides using aggregating one-dependence estimators classifier. J Indian Soc Remote Sens 46:1457\u20131470","journal-title":"J Indian Soc Remote Sens"},{"key":"5214_CR57","first-page":"189","volume":"42","author":"BT Pham","year":"2020","unstructured":"Pham BT, Singh SK, Ly H-B (2020) Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation. Vietnam J Earth Sci 42:189","journal-title":"Vietnam J Earth Sci"},{"key":"5214_CR58","first-page":"1","volume":"6","author":"P Yariyan","year":"2020","unstructured":"Yariyan P, Janizadeh S, Van Phong T et al (2020) Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping. Water Resources Manag 6:1\u201317","journal-title":"Water Resources Manag"},{"key":"5214_CR59","doi-asserted-by":"crossref","first-page":"103225","DOI":"10.1016\/j.earscirev.2020.103225","volume":"11","author":"A Merghadi","year":"2020","unstructured":"Merghadi A, Yunus AP, Dou J et al (2020) Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Sci Rev 11:103225","journal-title":"Earth-Sci Rev"},{"key":"5214_CR60","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.3390\/sym12061022","volume":"12","author":"BT Pham","year":"2020","unstructured":"Pham BT, Jaafari A, Avand M et al (2020) Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry 12:1022","journal-title":"Symmetry"},{"key":"5214_CR61","first-page":"3","volume":"42","author":"T Van Phong","year":"2020","unstructured":"Van Phong T, Ly H-B, Trinh PT et al (2020) Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach. Vietnam J Earth Sci 42:3","journal-title":"Vietnam J Earth Sci"},{"key":"5214_CR62","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1016\/S0142-1123(01)00207-9","volume":"24","author":"ME Haque","year":"2002","unstructured":"Haque ME, Sudhakar KV (2002) ANN back-propagation prediction model for fracture toughness in microalloy steel. Int J Fatigue 24:1003\u20131010","journal-title":"Int J Fatigue"},{"key":"5214_CR63","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.compstruct.2007.05.009","volume":"83","author":"E-S Mahdi","year":"2008","unstructured":"Mahdi E-S, Hany EK (2008) Crushing behavior of laterally compressed composite elliptical tubes: experiments and predictions using artificial neural networks. Compos Struct 83:399\u2013412","journal-title":"Compos Struct"},{"key":"5214_CR64","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.engstruct.2009.10.007","volume":"32","author":"J Kim","year":"2010","unstructured":"Kim J, Ghaboussi J, Elnashai AS (2010) Mechanical and informational modeling of steel beam-to-column connections. Eng Struct 32:449\u2013458","journal-title":"Eng Struct"},{"key":"5214_CR65","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.commatsci.2009.09.019","volume":"47","author":"M Younesi","year":"2010","unstructured":"Younesi M, Bahrololoom ME, Ahmadzadeh M (2010) Prediction of wear behaviors of nickel free stainless steel\u2013hydroxyapatite bio-composites using artificial neural network. Comput Mater Sci 47:645\u2013654","journal-title":"Comput Mater Sci"},{"key":"5214_CR66","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1061\/(ASCE)0733-9445(2001)127:7(818)","volume":"127","author":"A Sanad","year":"2001","unstructured":"Sanad A, Saka MP (2001) Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks. J Struct Eng 127:818\u2013828","journal-title":"J Struct Eng"},{"key":"5214_CR67","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.engstruct.2004.01.011","volume":"26","author":"MY Mansour","year":"2004","unstructured":"Mansour MY, Dicleli M, Lee JY, Zhang J (2004) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Eng Struct 26:781\u2013799","journal-title":"Eng Struct"},{"key":"5214_CR68","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.conbuildmat.2005.01.047","volume":"20","author":"BB Adhikari","year":"2006","unstructured":"Adhikari BB, Mutsuyoshi H (2006) Prediction of shear strength of steel fiber RC beams using neural networks. Constr Build Mater 20:801\u2013811","journal-title":"Constr Build Mater"},{"key":"5214_CR69","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.engstruct.2004.02.011","volume":"26","author":"A Cladera","year":"2004","unstructured":"Cladera A, Mari AR (2004) Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part II: beams with stirrups. Eng Struct 26:927\u2013936","journal-title":"Eng Struct"},{"key":"5214_CR70","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/S0141-0296(03)00004-X","volume":"25","author":"S-C Lee","year":"2003","unstructured":"Lee S-C (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25:849\u2013857","journal-title":"Eng Struct"},{"key":"5214_CR71","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.conbuildmat.2005.01.054","volume":"20","author":"A \u00d6zta\u015fa","year":"2006","unstructured":"\u00d6zta\u015fa A, Pala M, \u00d6zbay E et al (2006) Predicting the compressive strength and slump of high strength concrete using neural network. Constr Build Mater 20:769\u2013775","journal-title":"Constr Build Mater"},{"key":"5214_CR72","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s00521-017-3007-7","volume":"31","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Kolovos KG (2019) Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 31:409\u2013424. https:\/\/doi.org\/10.1007\/s00521-017-3007-7","journal-title":"Neural Comput Appl"},{"key":"5214_CR73","doi-asserted-by":"publisher","first-page":"s102","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:s102\u2013s122. https:\/\/doi.org\/10.1080\/19648189.2016.1246693","journal-title":"Eur J Environ Civ Eng"},{"key":"5214_CR74","doi-asserted-by":"publisher","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 17:1344. https:\/\/doi.org\/10.3390\/s17061344","journal-title":"Sensors"},{"key":"5214_CR75","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/S0927-0256(03)00092-2","volume":"29","author":"Z Guo","year":"2004","unstructured":"Guo Z, Sha W (2004) Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network. Comput Mater Sci 29:12\u201328","journal-title":"Comput Mater Sci"},{"key":"5214_CR76","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.compstruct.2006.04.047","volume":"75","author":"A Kesavan","year":"2006","unstructured":"Kesavan A, Deivasigamani M, John S, Herszberg I (2006) Damage detection in T-joint composite structures. Compos Struct 75:313\u2013320","journal-title":"Compos Struct"},{"key":"5214_CR77","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.engfailanal.2017.07.011","volume":"81","author":"S Nasiri","year":"2017","unstructured":"Nasiri S, Khosravani MR, Weinberg K (2017) Fracture mechanics and mechanical fault detection by artificial intelligence methods: a review. Eng Fail Anal 81:270\u2013293","journal-title":"Eng Fail Anal"},{"key":"5214_CR78","doi-asserted-by":"crossref","first-page":"159","DOI":"10.12989\/sss.2014.14.2.159","volume":"14","author":"SJS Hakim","year":"2014","unstructured":"Hakim SJS, Razak HA (2014) Modal parameters based structural damage detection using artificial neural networks\u2014a review. Smart Struct Syst 14:159\u2013189","journal-title":"Smart Struct Syst"},{"key":"5214_CR79","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.conbuildmat.2014.01.041","volume":"55","author":"V Plevris","year":"2014","unstructured":"Plevris V, Asteris PG (2014) Modeling of masonry failure surface under biaxial compressive stress using Neural Networks. Constr Build Mater 55:447\u2013461. https:\/\/doi.org\/10.1016\/j.conbuildmat.2014.01.041","journal-title":"Constr Build Mater"},{"key":"5214_CR80","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.3390\/app9061042","volume":"9","author":"H Chen","year":"2019","unstructured":"Chen H, Asteris PG, Jahed Armaghani D et al (2019) Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models. Appl Sci 9:1042. https:\/\/doi.org\/10.3390\/app9061042","journal-title":"Appl Sci"},{"key":"5214_CR81","unstructured":"Kheyroddin A, Naderpour H, Ahmadi M (2013) Performance of circular concrete filled steel tube members subjected to axial loading. In: Proceedings of the fourth international conference on concrete & development, Tehran, Iran"},{"key":"5214_CR82","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s11704-016-5113-6","volume":"11","author":"Y Du","year":"2017","unstructured":"Du Y, Chen Z, Zhang C, Xiaochun C (2017) Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks. Front Comput Sci 11:863\u2013873","journal-title":"Front Comput Sci"},{"key":"5214_CR83","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00808-y","author":"P Sarir","year":"2019","unstructured":"Sarir P, Chen J, Asteris PG et al (2019) Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00808-y","journal-title":"Eng Comput"},{"key":"5214_CR84","doi-asserted-by":"publisher","DOI":"10.1109\/21.256541","author":"J-SR Jang","year":"1993","unstructured":"Jang J-SR (1993) ANFIS adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybernet. https:\/\/doi.org\/10.1109\/21.256541","journal-title":"IEEE Trans Syst Man Cybernet"},{"key":"5214_CR85","doi-asserted-by":"crossref","first-page":"81","DOI":"10.31181\/dmame1802079s","volume":"1","author":"S Sremac","year":"2018","unstructured":"Sremac S, Tanackov I, Kopi\u0107 M, Radovi\u0107 D (2018) ANFIS model for determining the economic order quantity. Decis Mak Appl Manag Eng 1:81\u201392","journal-title":"Decis Mak Appl Manag Eng"},{"key":"5214_CR86","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3390\/info9100240","volume":"9","author":"M Stoj\u010di\u0107","year":"2018","unstructured":"Stoj\u010di\u0107 M, Pamu\u010dar D, Mahmutagi\u0107 E, Stevi\u0107 \u017d (2018) Development of an ANFIS model for the optimization of a queuing system in warehouses. Information 9:240","journal-title":"Information"},{"key":"5214_CR87","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.eswa.2017.08.034","volume":"90","author":"V Lukovac","year":"2017","unstructured":"Lukovac V, Pamu\u010dar D, Popovi\u0107 M, \u0110Jorovi\u0107 B (2017) Portfolio model for analyzing human resources: an approach based on neuro-fuzzy modeling and the simulated annealing algorithm. Expert Syst Appl 90:318\u2013331","journal-title":"Expert Syst Appl"},{"key":"5214_CR88","unstructured":"Committee ACI (2005) Building code requirements for structural concrete (ACI 318-05) and commentary (ACI 318R-05). American Concrete Institute, Farmington Hills"},{"key":"5214_CR89","unstructured":"Japan (AIJ) AI of (1997) Recommendations for design and construction of concrete filled steel tubular structures. Architectural Institute of Japan, Japan"},{"key":"5214_CR90","unstructured":"Construction) C (Canadian I of S (2010) Handbook of steel construction.. CISC Toronto, ON, Canada"},{"key":"5214_CR91","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.tws.2014.01.023","volume":"78","author":"K Uenaka","year":"2014","unstructured":"Uenaka K (2014) Experimental study on concrete filled elliptical\/oval steel tubular stub columns under compression. Thin-Walled Struct 78:131\u2013137. https:\/\/doi.org\/10.1016\/j.tws.2014.01.023","journal-title":"Thin-Walled Struct"},{"key":"5214_CR92","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.tws.2017.08.018","volume":"120","author":"H Yang","year":"2017","unstructured":"Yang H, Liu F, Chan T, Wang W (2017) Behaviours of concrete-filled cold-formed elliptical hollow section beam-columns with varying aspect ratios. Thin-Walled Struct 120:9\u201328. https:\/\/doi.org\/10.1016\/j.tws.2017.08.018","journal-title":"Thin-Walled Struct"},{"key":"5214_CR93","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.tws.2016.10.013","volume":"110","author":"F Liu","year":"2017","unstructured":"Liu F, Wang Y, Chan T (2017) Behaviour of concrete-filled cold-formed elliptical hollow sections with varying aspect ratios. Thin-Walled Struct 110:47\u201361. https:\/\/doi.org\/10.1016\/j.tws.2016.10.013","journal-title":"Thin-Walled Struct"},{"key":"5214_CR94","doi-asserted-by":"crossref","unstructured":"Jamaluddin N (2011) Behaviour of elliptical concrete-filled steel tube (CFT) columns under axial compression load. Phd, University of Leeds","DOI":"10.1201\/b10564-45"},{"key":"5214_CR95","doi-asserted-by":"publisher","first-page":"3771","DOI":"10.1016\/j.engstruct.2008.07.004","volume":"30","author":"H Yang","year":"2008","unstructured":"Yang H, Lam D, Gardner L (2008) Testing and analysis of concrete-filled elliptical hollow sections. Eng Struct 30:3771\u20133781. https:\/\/doi.org\/10.1016\/j.engstruct.2008.07.004","journal-title":"Eng Struct"},{"key":"5214_CR96","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.jcsr.2015.06.013","volume":"113","author":"F McCann","year":"2015","unstructured":"McCann F, Gardner L, Qiu W (2015) Experimental study of slender concrete-filled elliptical hollow section beam-columns. J Constr Steel Res 113:185\u2013194","journal-title":"J Constr Steel Res"},{"key":"5214_CR97","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1016\/j.tws.2008.11.004","volume":"47","author":"XL Zhao","year":"2009","unstructured":"Zhao XL, Packer JA (2009) Tests and design of concrete-filled elliptical hollow section stub columns. Thin-Walled Struct 47:617\u2013628. https:\/\/doi.org\/10.1016\/j.tws.2008.11.004","journal-title":"Thin-Walled Struct"},{"key":"5214_CR98","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1680\/stbu.2007.160.1.13","volume":"160","author":"S De Nardin","year":"2007","unstructured":"De Nardin S, El Debs ALHC (2007) Axial load behaviour of concrete-filled steel tubular columns. Proc Inst Civ Eng Struct Build 160:13\u201322. https:\/\/doi.org\/10.1680\/stbu.2007.160.1.13","journal-title":"Proc Inst Civ Eng Struct Build"},{"key":"5214_CR99","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.tws.2016.09.004","volume":"108","author":"Y Ye","year":"2016","unstructured":"Ye Y, Han L-H, Sheehan T, Guo Z-X (2016) Concrete-filled bimetallic tubes under axial compression: experimental investigation. Thin-Walled Struct 108:321\u2013332. https:\/\/doi.org\/10.1016\/j.tws.2016.09.004","journal-title":"Thin-Walled Struct"},{"key":"5214_CR100","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.tws.2016.10.015","volume":"110","author":"M Mahgub","year":"2017","unstructured":"Mahgub M, Ashour A, Lam D, Dai X (2017) Tests of self-compacting concrete filled elliptical steel tube columns. Thin-Walled Struct 110:27\u201334","journal-title":"Thin-Walled Struct"},{"key":"5214_CR101","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.jcsr.2015.08.029","volume":"115","author":"T-M Chan","year":"2015","unstructured":"Chan T-M, Huai Y-M, Wang W (2015) Experimental investigation on lightweight concrete-filled cold-formed elliptical hollow section stub columns. J Constr Steel Res 115:434\u2013444. https:\/\/doi.org\/10.1016\/j.jcsr.2015.08.029","journal-title":"J Constr Steel Res"},{"key":"5214_CR102","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.jcsr.2017.10.025","volume":"140","author":"L He","year":"2018","unstructured":"He L, Zhao Y, Lin S (2018) Experimental study on axially compressed circular CFST columns with improved confinement effect. J Constr Steel Res 140:74\u201381. https:\/\/doi.org\/10.1016\/j.jcsr.2017.10.025","journal-title":"J Constr Steel Res"},{"key":"5214_CR103","doi-asserted-by":"crossref","unstructured":"Yi S, Young B (2017) Experimental investigation of concrete-filled cold-formed steel elliptical stub columns. In: Tubular structures XVI, Proceedings of the 16th international symposium for tubular structures. Melbourne, Australia, pp 109\u2013115","DOI":"10.1201\/9781351210843-14"},{"key":"5214_CR104","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.engstruct.2014.02.013","volume":"67","author":"MH Lai","year":"2014","unstructured":"Lai MH, Ho JCM (2014) Confinement effect of ring-confined concrete-filled-steel-tube columns under uni-axial load. Eng Struct 67:123\u2013141. https:\/\/doi.org\/10.1016\/j.engstruct.2014.02.013","journal-title":"Eng Struct"},{"key":"5214_CR105","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1002\/tal.1046","volume":"23","author":"MH Lai","year":"2014","unstructured":"Lai MH, Ho JCM (2014) Behaviour of uni-axially loaded concrete-filled-steel-tube columns confined by external rings. Struct Des Tall Spec Build 23:403\u2013426. https:\/\/doi.org\/10.1002\/tal.1046","journal-title":"Struct Des Tall Spec Build"},{"key":"5214_CR106","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1061\/(ASCE)0733-9445(2002)128:9(1222)","volume":"128","author":"CS Huang","year":"2002","unstructured":"Huang CS, Yeh Y-K, Liu G-Y et al (2002) Axial load behavior of stiffened concrete-filled steel columns. J Struct Eng 128:1222\u20131230. https:\/\/doi.org\/10.1061\/(ASCE)0733-9445(2002)128:9(1222)","journal-title":"J Struct Eng"},{"key":"5214_CR107","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1061\/(ASCE)0733-9445(1998)124:10(1125)","volume":"124","author":"Stephen P Schneider","year":"1998","unstructured":"Schneider Stephen P (1998) Axially loaded concrete-filled steel tubes. J Struct Eng 124:1125\u20131138. https:\/\/doi.org\/10.1061\/(ASCE)0733-9445(1998)124:10(1125)","journal-title":"J Struct Eng"},{"key":"5214_CR108","doi-asserted-by":"publisher","first-page":"04016057","DOI":"10.1061\/(ASCE)ST.1943-541X.0001513","volume":"142","author":"Konstantinos A Skalomenos","year":"2016","unstructured":"Skalomenos Konstantinos A, Kazuhiro Hayashi, Ryosuke Nishi et al (2016) Experimental behavior of concrete-filled steel tube columns using ultrahigh-strength steel. J Struct Eng 142:04016057. https:\/\/doi.org\/10.1061\/(ASCE)ST.1943-541X.0001513","journal-title":"J Struct Eng"},{"key":"5214_CR109","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.conbuildmat.2011.06.062","volume":"26","author":"J Xiao","year":"2012","unstructured":"Xiao J, Huang Y, Yang J, Zhang Ch (2012) Mechanical properties of confined recycled aggregate concrete under axial compression. Constr Build Mater 26:591\u2013603. https:\/\/doi.org\/10.1016\/j.conbuildmat.2011.06.062","journal-title":"Constr Build Mater"},{"key":"5214_CR110","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.tws.2011.09.007","volume":"50","author":"YF Yang","year":"2012","unstructured":"Yang YF, Han LH (2012) Concrete filled steel tube (CFST) columns subjected to concentrically partial compression. Thin-Walled Struct 50:147\u2013156. https:\/\/doi.org\/10.1016\/j.tws.2011.09.007","journal-title":"Thin-Walled Struct"},{"key":"5214_CR111","first-page":"1724","volume":"9","author":"AA Khalaf","year":"2018","unstructured":"Khalaf AA, Naser KZ, Kamil F (2018) Predicting the ultimate strength of circular concrete filled steel tubular columns by using artificial neural networks. Int J Civil Eng Technol 9:1724\u20131736","journal-title":"Int J Civil Eng Technol"},{"key":"5214_CR112","first-page":"718","volume-title":"Experimental studies on concrete-filled steel tubular stub columns under concentric loading","author":"M Tomii","year":"1977","unstructured":"Tomii M, Yoshimura K, Morishita Y (1977) Experimental studies on concrete-filled steel tubular stub columns under concentric loading. ASCE, Washington, pp 718\u2013741"},{"key":"5214_CR113","volume-title":"Study on mechanical behavior of circular confined concrete column under axial compression","author":"F Hayashi","year":"1990","unstructured":"Hayashi F (1990) Study on mechanical behavior of circular confined concrete column under axial compression. Kyushu University, Fukuoka"},{"key":"5214_CR114","volume-title":"Data mining: practical machine learning tools and techniques","author":"IH Witten","year":"2016","unstructured":"Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Fukuoka"},{"key":"5214_CR115","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1061\/(ASCE)1084-0699(2005)10:3(216)","volume":"10","author":"Jy S Wu","year":"2005","unstructured":"Wu Jy S, Jun Han, Shastri Annambhotla, Scott Bryant (2005) Artificial neural networks for forecasting watershed runoff and stream flows. J Hydrol Eng 10:216\u2013222. https:\/\/doi.org\/10.1061\/(ASCE)1084-0699(2005)10:3(216)","journal-title":"J Hydrol Eng"},{"key":"5214_CR116","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.jksues.2015.02.001","volume":"29","author":"AAM Ahmed","year":"2017","unstructured":"Ahmed AAM, Shah SMA (2017) Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. J King Saud Univ Eng Sci 29:237\u2013243. https:\/\/doi.org\/10.1016\/j.jksues.2015.02.001","journal-title":"J King Saud Univ Eng Sci"},{"key":"5214_CR117","doi-asserted-by":"publisher","first-page":"3779","DOI":"10.1016\/j.asoc.2011.02.011","volume":"11","author":"M Bilgehan","year":"2011","unstructured":"Bilgehan M (2011) Comparison of ANFIS and NN models\u2014with a study in critical buckling load estimation. Appl Soft Comput 11:3779\u20133791. https:\/\/doi.org\/10.1016\/j.asoc.2011.02.011","journal-title":"Appl Soft Comput"},{"key":"5214_CR118","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/S1474-6670(17)62005-6","volume":"16","author":"T Takagi","year":"1983","unstructured":"Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator\u2019s control actions. IFAC Proc Vol 16:55\u201360. https:\/\/doi.org\/10.1016\/S1474-6670(17)62005-6","journal-title":"IFAC Proc Vol"},{"key":"5214_CR119","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","volume":"12","author":"D Simon","year":"2008","unstructured":"Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12:702\u2013713","journal-title":"IEEE Trans Evolut Comput"},{"key":"5214_CR120","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995\u2014international conference on neural networks, vol 4, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"5214_CR121","first-page":"975","volume":"4","author":"Er Harish kundra","year":"2014","unstructured":"Harish kundra Er, Gopika kakkar Er (2014) Biogeography based optimization its applications\u2014a review. Int J Comput Sci Commun Eng 4:975","journal-title":"Int J Comput Sci Commun Eng"},{"key":"5214_CR122","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1177\/0142331214537015","volume":"37","author":"H Ma","year":"2014","unstructured":"Ma H, Fei M, Simon D, Chen Z (2014) Biogeography-based optimization in noisy environments. Trans Inst Meas Control 37:190\u2013204","journal-title":"Trans Inst Meas Control"},{"key":"5214_CR123","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1016\/j.swevo.2015.05.001","volume":"24","author":"H Garg","year":"2015","unstructured":"Garg H (2015) An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm Evolut Comput 24:1653","journal-title":"Swarm Evolut Comput"},{"key":"5214_CR124","first-page":"1","volume":"20","author":"CR Reeves","year":"2002","unstructured":"Reeves CR, Rowe JE (2002) Genetic algorithms: principles and perspectives: a guide to GA theory. Oper Res Comput Sci Interfaces Ser 20:1\u2013326","journal-title":"Oper Res Comput Sci Interfaces Ser"},{"key":"5214_CR125","volume-title":"Adaptation in natural and artificial systems","author":"J Holland","year":"1975","unstructured":"Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor"},{"key":"5214_CR126","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/3927.001.0001","volume-title":"An introduction to genentic algorithms","author":"M Mitchell","year":"1998","unstructured":"Mitchell M (1998) An introduction to genentic algorithms. MIT Press, Cambridge"},{"key":"5214_CR127","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00175354","volume":"4","author":"D Whitley","year":"1994","unstructured":"Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4:65\u201385","journal-title":"Stat Comput"},{"key":"5214_CR128","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.asoc.2006.03.004","volume":"7","author":"H Kim","year":"2007","unstructured":"Kim H, Shin K (2007) A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets. Appl Soft Comput 7:569\u2013576","journal-title":"Appl Soft Comput"},{"key":"5214_CR129","unstructured":"Eberhart R, Kennedy J (2002) A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micromachine and human science"},{"key":"5214_CR130","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.measurement.2014.09.075","volume":"60","author":"E Momeni","year":"2015","unstructured":"Momeni E, Jahed Armaghani D, Hajihassani M, Mohd Amin MF (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50\u201363. https:\/\/doi.org\/10.1016\/j.measurement.2014.09.075","journal-title":"Measurement"},{"issue":"8","key":"5214_CR131","first-page":"12","volume":"27","author":"K-TT Bui","year":"2016","unstructured":"Bui K-TT, Bui DT, Zou J et al (2016) A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput Appl 27(8):12","journal-title":"Neural Comput Appl"},{"key":"5214_CR132","unstructured":"Hu X, Eberhart R (2002) Solving constrained nonlinear optimization problems with particle swarm optimization. In: 6th world multiconference on systemics, cybernetics and informatics (SCI 2002. pp 203\u2013206"},{"key":"5214_CR133","doi-asserted-by":"publisher","first-page":"2997","DOI":"10.1016\/j.asoc.2012.11.033","volume":"13","author":"A Khare","year":"2013","unstructured":"Khare A, Rangnekar S (2013) A review of particle swarm optimization and its applications in Solar Photovoltaic system. Appl Soft Comput 13:2997\u20133006. https:\/\/doi.org\/10.1016\/j.asoc.2012.11.033","journal-title":"Appl Soft Comput"},{"key":"5214_CR134","volume-title":"Stochastic models of uncertainties in computational mechanics","author":"Pds Christian","year":"2012","unstructured":"Christian Pds (2012) Stochastic models of uncertainties in computational mechanics. Am Soc Civ Eng, Reston"},{"key":"5214_CR135","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-54339-0","volume-title":"Uncertainty quantification: an accelerated course with advanced applications in computational engineering","author":"C Soize","year":"2017","unstructured":"Soize C (2017) Uncertainty quantification: an accelerated course with advanced applications in computational engineering. Springer International Publishing, Berlin"},{"key":"5214_CR136","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.cma.2015.10.006","volume":"303","author":"TT Le","year":"2016","unstructured":"Le TT, Guilleminot J, Soize C (2016) Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite. Comput Methods Appl Mech Eng 303:430\u2013449. https:\/\/doi.org\/10.1016\/j.cma.2015.10.006","journal-title":"Comput Methods Appl Mech Eng"},{"key":"5214_CR137","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.3390\/ma12111828","volume":"12","author":"H-B Ly","year":"2019","unstructured":"Ly H-B, Desceliers C, Le LM et al (2019) Quantification of uncertainties on the critical buckling load of columns under axial compression with uncertain random materials. Materials 12:1828. https:\/\/doi.org\/10.3390\/ma12111828","journal-title":"Materials"},{"key":"5214_CR138","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s10409-013-0101-7","volume":"29","author":"J Guilleminot","year":"2013","unstructured":"Guilleminot J, Le TT, Soize C (2013) Stochastic framework for modeling the linear apparent behavior of complex materials: application to random porous materials with interphases. Acta Mech Sin 29:773\u2013782. https:\/\/doi.org\/10.1007\/s10409-013-0101-7","journal-title":"Acta Mech Sin"},{"key":"5214_CR139","doi-asserted-by":"crossref","unstructured":"Soize C, Desceliers C, Guilleminot J et al (2015) Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics. In: UNCECOMP 2015, 1st ECCOMAS thematic international conference on uncertainty quantification in computational sciences and engineering, pp 1\u201326","DOI":"10.7712\/120215.4249.527"},{"key":"5214_CR140","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1016\/j.cpc.2014.01.006","volume":"185","author":"A Cunha","year":"2014","unstructured":"Cunha A, Nasser R, Sampaio R et al (2014) Uncertainty quantification through the Monte Carlo method in a cloud computing setting. Comput Phys Commun 185:1355\u20131363. https:\/\/doi.org\/10.1016\/j.cpc.2014.01.006","journal-title":"Comput Phys Commun"},{"key":"5214_CR141","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.compgeo.2013.07.010","volume":"54","author":"H Fattahi","year":"2013","unstructured":"Fattahi H, Shojaee S, Farsangi MAE, Mansouri H (2013) Hybrid Monte Carlo simulation and ANFIS-subtractive clustering method for reliability analysis of the excavation damaged zone in underground spaces. Comput Geotech 54:210\u2013221. https:\/\/doi.org\/10.1016\/j.compgeo.2013.07.010","journal-title":"Comput Geotech"},{"key":"5214_CR142","unstructured":"The MathWorks (2018) MATLAB. Natick, MA, USA"},{"key":"5214_CR143","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?\u2013Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247\u20131250","journal-title":"Geosci Model Dev"},{"key":"5214_CR144","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79\u201382. https:\/\/doi.org\/10.3354\/cr030079","journal-title":"Clim Res"},{"key":"5214_CR145","doi-asserted-by":"publisher","first-page":"104845","DOI":"10.1016\/j.atmosres.2020.104845","volume":"237","author":"BT Pham","year":"2020","unstructured":"Pham BT, Le LM, Le T-T et al (2020) Development of advanced artificial intelligence models for daily rainfall prediction. Atmos Res 237:104845. https:\/\/doi.org\/10.1016\/j.atmosres.2020.104845","journal-title":"Atmos Res"},{"key":"5214_CR146","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.3390\/ma13051072","volume":"13","author":"DV Dao","year":"2020","unstructured":"Dao DV, Ly H-B, Vu H-LT et al (2020) Investigation and optimization of the C-ANN structure in predicting the compressive strength of foamed concrete. Materials 13:1072","journal-title":"Materials"},{"key":"5214_CR147","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.3390\/ma13051205","volume":"13","author":"HQ Nguyen","year":"2020","unstructured":"Nguyen HQ, Ly H-B, Tran VQ et al (2020) Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression. Materials 13:1205","journal-title":"Materials"},{"key":"5214_CR148","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.3390\/su12062339","volume":"12","author":"BT Pham","year":"2020","unstructured":"Pham BT, Nguyen-Thoi T, Ly H-B et al (2020) Extreme learning machine based prediction of soil shear strength: a sensitivity analysis using Monte Carlo simulations and feature backward elimination. Sustainability 12:2339","journal-title":"Sustainability"},{"key":"5214_CR149","first-page":"12","volume":"14","author":"H-B Ly","year":"2020","unstructured":"Ly H-B, Pham BT (2020) Prediction of shear strength of soil using direct shear test and support vector machine model. Open Constr Build Technol J 14:12","journal-title":"Open Constr Build Technol J"},{"key":"5214_CR150","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.3390\/su12072709","volume":"12","author":"H-B Ly","year":"2020","unstructured":"Ly H-B, Le T-T, Vu H-LT et al (2020) Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability 12:2709","journal-title":"Sustainability"},{"key":"5214_CR151","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.enggeo.2007.05.004","volume":"93","author":"H G\u00fcll\u00fc","year":"2007","unstructured":"G\u00fcll\u00fc H, Er\u00e7elebi E (2007) A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng Geol 93:65\u201381","journal-title":"Eng Geol"},{"key":"5214_CR152","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.catena.2018.04.004","volume":"166","author":"BT Pham","year":"2018","unstructured":"Pham BT, Son LH, Hoang T-A et al (2018) Prediction of shear strength of soft soil using machine learning methods. CATENA 166:181\u2013191. https:\/\/doi.org\/10.1016\/j.catena.2018.04.004","journal-title":"CATENA"},{"key":"5214_CR153","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.03.073","author":"K Khosravi","year":"2019","unstructured":"Khosravi K, Shahabi H, Thai Pham B et al (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.03.073","journal-title":"J Hydrol"},{"key":"5214_CR154","doi-asserted-by":"publisher","first-page":"2788","DOI":"10.3390\/app9142788","volume":"9","author":"L Cavaleri","year":"2019","unstructured":"Cavaleri L, Asteris PG, Psyllaki PP et al (2019) Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks. Appl Sci 9:2788. https:\/\/doi.org\/10.3390\/app9142788","journal-title":"Appl Sci"},{"key":"5214_CR155","doi-asserted-by":"publisher","first-page":"04024","DOI":"10.1051\/matecconf\/201818804024","volume":"188","author":"P Psyllaki","year":"2018","unstructured":"Psyllaki P, Stamatiou K, Iliadis I et al (2018) Surface treatment of tool steels against galling failure. MATEC Web Conf 188:04024. https:\/\/doi.org\/10.1051\/matecconf\/201818804024","journal-title":"MATEC Web Conf"},{"key":"5214_CR156","doi-asserted-by":"publisher","first-page":"169","DOI":"10.12989\/amr.2017.6.2.169","volume":"6","author":"L Cavaleri","year":"2017","unstructured":"Cavaleri L, Chatzarakis GE, Trapani FD et al (2017) Modeling of surface roughness in electro-discharge machining using artificial neural networks. Adv Mater Res 6:169. https:\/\/doi.org\/10.12989\/amr.2017.6.2.169","journal-title":"Adv Mater Res"},{"key":"5214_CR157","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s00704-015-1480-4","volume":"125","author":"S Salcedo-Sanz","year":"2016","unstructured":"Salcedo-Sanz S, Deo RC, Carro-Calvo L, Saavedra-Moreno B (2016) Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theor Appl Climatol 125:13\u201325","journal-title":"Theor Appl Climatol"},{"key":"5214_CR158","doi-asserted-by":"crossref","unstructured":"Sharma A, Goyal MK (2015) Bayesian network model for monthly rainfall forecast. In: 2015 IEEE international conference on research in computational intelligence and communication networks (ICRCICN). IEEE, pp 241\u2013246","DOI":"10.1109\/ICRCICN.2015.7434243"},{"key":"5214_CR159","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.jneumeth.2005.04.013","volume":"148","author":"\u0130 G\u00fcler","year":"2005","unstructured":"G\u00fcler \u0130, \u00dcbeyli ED (2005) Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Methods 148:113\u2013121. https:\/\/doi.org\/10.1016\/j.jneumeth.2005.04.013","journal-title":"J Neurosci Methods"},{"key":"5214_CR160","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.2166\/ws.2017.208","volume":"18","author":"AF Mashaly","year":"2018","unstructured":"Mashaly AF, Alazba AA (2018) ANFIS modeling and sensitivity analysis for estimating solar still productivity using measured operational and meteorological parameters. Water Supply 18:1437\u20131448. https:\/\/doi.org\/10.2166\/ws.2017.208","journal-title":"Water Supply"},{"key":"5214_CR161","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1016\/j.apenergy.2013.04.095","volume":"111","author":"G Xiong","year":"2013","unstructured":"Xiong G, Shi D, Duan X (2013) Multi-strategy ensemble biogeography-based optimization for economic dispatch problems. Appl Energy 111:801\u2013811. https:\/\/doi.org\/10.1016\/j.apenergy.2013.04.095","journal-title":"Appl Energy"},{"key":"5214_CR162","doi-asserted-by":"publisher","first-page":"5711","DOI":"10.3390\/e17085711","volume":"17","author":"S Wang","year":"2015","unstructured":"Wang S, Zhang Y, Ji G et al (2015) Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy 17:5711\u20135728. https:\/\/doi.org\/10.3390\/e17085711","journal-title":"Entropy"},{"key":"5214_CR163","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.compstruct.2017.07.054","volume":"178","author":"JHS Almeida","year":"2017","unstructured":"Almeida JHS, Ribeiro ML, Tita V, Amico SC (2017) Stacking sequence optimization in composite tubes under internal pressure based on genetic algorithm accounting for progressive damage. Compos Struct 178:20\u201326. https:\/\/doi.org\/10.1016\/j.compstruct.2017.07.054","journal-title":"Compos Struct"},{"key":"5214_CR164","doi-asserted-by":"crossref","first-page":"e270","DOI":"10.1002\/sta4.270","volume":"9","author":"DR Bickel","year":"2020","unstructured":"Bickel DR (2020) Testing prediction algorithms as null hypotheses: application to assessing the performance of deep neural networks. Stat 9:e270","journal-title":"Stat"},{"key":"5214_CR165","unstructured":"Lu M, Ishwaran H (2017) A machine learning alternative to P-values. arXiv preprint arXiv:170104944"},{"key":"5214_CR166","unstructured":"Ryll L, Seidens S (2019) Evaluating the performance of machine learning algorithms in financial market forecasting: a comprehensive survey. arXiv preprint arXiv:190607786"},{"key":"5214_CR167","doi-asserted-by":"crossref","first-page":"699","DOI":"10.3389\/fpsyg.2018.00699","volume":"9","author":"D Trafimow","year":"2018","unstructured":"Trafimow D, Amrhein V, Areshenkoff CN et al (2018) Manipulating the alpha level cannot cure significance testing. Front Psychol 9:699","journal-title":"Front Psychol"},{"key":"5214_CR168","first-page":"21","volume":"6","author":"T Dahiru","year":"2008","unstructured":"Dahiru T (2008) P-value, a true test of statistical significance? A cautionary note. Ann Ibadan Postgraduate med 6:21\u201326","journal-title":"Ann Ibadan Postgraduate med"},{"key":"5214_CR169","first-page":"2","volume":"30","author":"A Kaur","year":"2018","unstructured":"Kaur A, Kaur I (2018) An empirical evaluation of classification algorithms for fault prediction in open source projects. J King Saud Univ Computer Inf Sci 30:2\u201317","journal-title":"J King Saud Univ Computer Inf Sci"},{"key":"5214_CR170","doi-asserted-by":"crossref","unstructured":"Jakubcov\u00e1 M, M\u00e1ca P, Pech P (2014) A Comparison of selected modifications of the particle swarm optimization algorithm. In: Journal of Applied Mathematics. https:\/\/www.hindawi.com\/journals\/jam\/2014\/293087\/. Accessed 21 Feb 2020","DOI":"10.1155\/2014\/293087"},{"key":"5214_CR171","doi-asserted-by":"publisher","first-page":"375","DOI":"10.12989\/scs.2017.25.3.375","volume":"25","author":"F Ding","year":"2017","unstructured":"Ding F, Ding X, Liu X et al (2017) Mechanical behavior of elliptical concrete-filled steel tubular stub columns under axial loading. Steel Compos Struct 25:375\u2013388. https:\/\/doi.org\/10.12989\/scs.2017.25.3.375","journal-title":"Steel Compos Struct"},{"key":"5214_CR172","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.jcsr.2005.01.004","volume":"61","author":"L-H Han","year":"2005","unstructured":"Han L-H, Yao G-H, Zhao X-L (2005) Tests and calculations for hollow structural steel (HSS) stub columns filled with self-consolidating concrete (SCC). J Constr Steel Res 61:1241\u20131269. https:\/\/doi.org\/10.1016\/j.jcsr.2005.01.004","journal-title":"J Constr Steel Res"},{"key":"5214_CR173","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.engstruct.2016.12.049","volume":"135","author":"Z-B Wang","year":"2017","unstructured":"Wang Z-B, Tao Z, Han L-H et al (2017) Strength, stiffness and ductility of concrete-filled steel columns under axial compression. Eng Struct 135:209\u2013221. https:\/\/doi.org\/10.1016\/j.engstruct.2016.12.049","journal-title":"Eng Struct"},{"key":"5214_CR174","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.jcsr.2014.03.005","volume":"98","author":"F Ding","year":"2014","unstructured":"Ding F, Fang C, Bai Y, Gong Y (2014) Mechanical performance of stirrup-confined concrete-filled steel tubular stub columns under axial loading. J Constr Steel Res 98:146\u2013157. https:\/\/doi.org\/10.1016\/j.jcsr.2014.03.005","journal-title":"J Constr Steel Res"},{"key":"5214_CR175","volume-title":"318-08: Building Code Requirements for Structural Concrete and Commentary","author":"ACI Committee","year":"2011","unstructured":"Committee ACI (2011) 318-08: Building Code Requirements for Structural Concrete and Commentary. American Concrete Institute, Farmington Hills"},{"key":"5214_CR176","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.tws.2012.03.008","volume":"56","author":"M Dundu","year":"2012","unstructured":"Dundu M (2012) Compressive strength of circular concrete filled steel tube columns. Thin-Walled Struct 56:62\u201370. https:\/\/doi.org\/10.1016\/j.tws.2012.03.008","journal-title":"Thin-Walled Struct"},{"key":"5214_CR177","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.jcsr.2004.06.006","volume":"61","author":"J Zeghiche","year":"2005","unstructured":"Zeghiche J, Chaoui K (2005) An experimental behaviour of concrete-filled steel tubular columns. J Constr Steel Res 61:53\u201366. https:\/\/doi.org\/10.1016\/j.jcsr.2004.06.006","journal-title":"J Constr Steel Res"},{"key":"5214_CR178","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.1016\/j.jcsr.2009.07.004","volume":"65","author":"WLA de Oliveira","year":"2009","unstructured":"de Oliveira WLA, De Nardin S, de Cresce El Debs ALH, El Debs MK (2009) Influence of concrete strength and length\/diameter on the axial capacity of CFT columns. J Constr Steel Res 65:2103\u20132110. https:\/\/doi.org\/10.1016\/j.jcsr.2009.07.004","journal-title":"J Constr Steel Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05214-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05214-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05214-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T14:00:17Z","timestamp":1667570417000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05214-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,25]]},"references-count":178,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5214"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05214-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,25]]},"assertion":[{"value":"23 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 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 declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}