Recent Trends in Prediction of Concrete Elements Behavior Using Soft Computing (2010–2020)

Cremen G, Baker JW (2018) Quantifying the benefits of building instruments to FEMA P-58 rapid post-earthquake damage and loss predictions. Eng Struct 176:243–253 Kahandawa K, Domingo N, Park K, Uma S (2018) Earthquake damage estimation systems: literature review. Procedia Eng 212:622–628 Chen J, Zeng G-Q, Zhou W, Du W, Lu K-D (2018) Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Convers Manag 165:681–695 Khosravi A, Koury R, Machado L, Pabon J (2018) Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustain Energy Technol Assess 25:146–160 Khosravi A, Machado L, Nunes R (2018) Time-series prediction of wind speed using machine learning algorithms: a case study Osorio wind farm, Brazil. Appl Energy 224:550–566 Martínez-Álvarez F, Troncoso A, Morales-Esteban A, Riquelme JC (2011) Computational intelligence techniques for predicting earthquakes. In: International conference on hybrid artificial intelligence systems, 2011. Springer, pp 287–294 Mirrashid M (2014) Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Nat Hazards 74(3):1577–1593. https://doi.org/10.1007/s11069-014-1264-7 Mirrashid M, Givehchi M, Miri M, Madandoust R (2016) Performance investigation of neuro-fuzzy system for earthquake prediction. Asian J Civ Eng (BHRC) 17(2):213–223 Chandrasekaran M, Muralidhar M, Krishna CM, Dixit U (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46(5–8):445–464 Kudus SA, Bunnori NM, Basri SR, Shahiron S, Jamil MNM, Noorsuhada MN (2012) An Overview Current Application Of Artificial Neural Network In Concrete. Adv Mater Res 626:372–375. https://doi.org/10.4028/www.scientific.net/AMR.626.372 Naderpour H, Mirrashid M (2020) Evaluation and verification of finite element analytical models in reinforced concrete members. Iran J Sci Technol Trans Civ Eng 44:463–480. https://doi.org/10.1007/s40996-019-00240-8 Kheyroddin A, Mirrashid M, Arshadi H (2017) An investigation on the behavior of concrete cores in suspended tall buildings. Iran J Sci Technol Trans Civ Eng 41(4):383–388. https://doi.org/10.1007/s40996-017-0075-y Zadeh LA (2001) Applied soft computing-foreword. Appl Soft Comput 1(1):1–2 Mitra S, Pal SK, Mitra P (2002) Data mining in soft computing framework: a survey. IEEE Trans Neural Networks 13(1):3–14 Magdalena L (2010) What is soft computing? Revisiting possible answers. Int J Comput Intell Syst 3(2):148–159 Siddique N, Adeli H (2013) Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing. Wiley, New York Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541 Naderpour H, Mirrashid M (2015) Application of soft computing to reinforced concrete beams strengthened with fibre reinforced polymers: a state-of-the-art review. Comput Tech Civ Struct Eng 38:305–323 Gunes O, Buyukozturk O, Karaca E (2009) A fracture-based model for FRP debonding in strengthened beams. Eng Fract Mech 76(12):1897–1909 Nasrollahzadeh K, Afzali S (2019) Fuzzy logic model for pullout capacity of near-surface-mounted FRP reinforcement bonded to concrete. Neural Comput Appl 31:7837–7865 Golafshani EM, Rahai A, Sebt MH, Akbarpour H (2012) Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic. Constr Build Mater 36:411–418. https://doi.org/10.1016/j.conbuildmat.2012.04.046 Yan F, Lin Z, Wang X, Azarmi F, Sobolev K (2017) Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm. Compos Struct 161:441–452. https://doi.org/10.1016/j.compstruct.2016.11.068 Golafshani EM, Rahai A, Sebt MH (2014) Artificial neural network and genetic programming for predicting the bond strength of GFRP bars in concrete. Mater Struct 48(5):1581–1602. https://doi.org/10.1617/s11527-014-0256-0 Köroğlu MA (2019) Artificial neural network for predicting the flexural bond strength of FRP bars in concrete. Sci Eng Compos Mater 26(1):12–29. https://doi.org/10.1515/secm-2017-0155 Alizadeh F, Naderpour H, Mirrashid M (2020) Bond strength prediction of the composite rebars in concrete using innovative bio-inspired models. Eng Rep. https://doi.org/10.1002/eng2.12260 Marijana L, Ana T-G, Milos K, Todorka S, Meri C (2012) Neural network prognostic model for RC beams strengthened with CFRP strips. Appl Eng Sci 10(1):27–30. https://doi.org/10.5937/jaes10-1661 Choi K-K, Sim W-C, Kim H-S (2016) Shear strength prediction of reinforced-concrete beams based on fuzzy theory. Proc Inst Civ Eng Struct Build 169(5):357–372. https://doi.org/10.1680/jstbu.14.00128 Tanarslan HM, Kumanlioglu A, Sakar G (2015) An anticipated shear design method for reinforced concrete beams strengthened with anchoraged carbon fiber-reinforced polymer by using neural network. Struct Des Tall Spec Build 24(1):19–39. https://doi.org/10.1002/tal.1152 Iruansi O, Guadagnini M, Pilakoutas K, Neocleous K (2010) Predicting the shear strength of RC beams without stirrups using Bayesian neural network. In: Proceedings of the 4th international workshop on reliable engineering computing, robust design—coping with hazards, risk and uncertainty, Singapore, pp 3–5 Bashir R, Ashour A (2012) Neural network modelling for shear strength of concrete members reinforced with FRP bars. Compos B Eng 43(8):3198–3207. https://doi.org/10.1016/j.compositesb.2012.04.011 Naderpour H, Poursaeidi O, Ahmadi M (2018) Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks. Measurement 126:299–308. https://doi.org/10.1016/j.measurement.2018.05.051 Al-Shather LM, Redah SMAM (2018) Prediction of shear strength of reinforced concrete beams using artificial neural network and evaluated by finite element software. J Constr Eng Technol Manag 8(1):34–42 Amani J, Moeini R (2012) Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Sci Iran 19(2):242–248. https://doi.org/10.1016/j.scient.2012.02.009 Naik U, Kute S (2013) Span-to-depth ratio effect on shear strength of steel fiber-reinforced high-strength concrete deep beams using ANN model. Int J Adv Struct Eng 5(1):29 Lee S, Lee C (2014) Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks. Eng Struct 61:99–112. https://doi.org/10.1016/j.engstruct.2014.01.001 Hossain KM, Gladson LR, Anwar MS (2017) Modeling shear strength of medium-to ultra-high-strength steel fiber-reinforced concrete beams using artificial neural network. Neural Comput Appl 28(1):1119–1130 Liu H, Cao P, Zhang X (2011) Application of fuzzy comprehensive evaluation in the analysis of shearing capacity of deep beam. In: 2011 international conference on multimedia technology, 2011. IEEE, pp 4326–4329 Mohammadhassani M, Nezamabadi-Pour H, Suhatril M, Shariati M (2014) An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Smart Struct Syst Int J 14(5):785–809 Naderpour H, Mirrashid M (2020) Bio-inspired predictive models for shear strength of reinforced concrete beams having steel stirrups. Soft Comput 24:12587–12597. https://doi.org/10.1007/s00500-020-04698-x Naderpour H, Mirrashid M (2020) Shear strength prediction of RC beams using adaptive neuro-fuzzy inference system. Sci Iran 27(2):657–670. https://doi.org/10.24200/SCI.2018.50308.1624 Naderpour H, Alavi SA (2017) A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of adaptive neuro-fuzzy inference system. Compos Struct 170:215–227. https://doi.org/10.1016/j.compstruct.2017.03.028 Naderpour H, Haji M, Mirrashid M (2020) Shear capacity estimation of FRP-reinforced concrete beams using computational intelligence. Structures 28:321–328. https://doi.org/10.1016/j.istruc.2020.08.076 Akbari J (2013) Evaluation of ultimate torsional strength of reinforcement concrete beams using finite element analysis and artificial neural network. Int J Eng. https://doi.org/10.5829/idosi.ije.2013.26.05b.06 Arslan MH (2010) Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes. Adv Eng Softw 41(7–8):946–955. https://doi.org/10.1016/j.advengsoft.2010.05.009 Huang HC (2012) Using a hybrid neural network to predict the torsional strength of reinforced concrete beams. Adv Mater Res 538–541:2749–2753. https://doi.org/10.4028/www.scientific.net/AMR.538-541.2749 Ilkhani MH, Naderpour H, Kheyroddin A (2019) A proposed novel approach for torsional strength prediction of RC beams. J Build Eng 25:100810. https://doi.org/10.1016/j.jobe.2019.100810 Li Z, Wang J (2010) Application of genetic neural network for predicting bearing capacity of RC beams strengthened by carbon fiber sheets, In 2010 Second WRI Global Congress on Intelligent Systems, IEEE, Vol. 1, pp 267–271. https://doi.org/10.1109/gcis.2010.149 Imam A, Anifowose F, Azad AK (2015) Residual strength of corroded reinforced concrete beams using an adaptive model based on ANN. Int J Concrete Struct Mater 9(2):159–172 Ongpeng J, Oreta A, Hirose S (2018) Investigation on the sensitivity of ultrasonic test applied to reinforced concrete beams using neural network. Appl Sci 8(3):405. https://doi.org/10.3390/app8030405 Umeonyiagu IE, Nwobi-Okoye CC (2019) Modelling and multi objective optimization of bamboo reinforced concrete beams using ANN and genetic algorithms. Eur J Wood Wood Prod 77:931–947 Ongpeng JMC, Oreta WC, Hirose S (2016) Effect of load pattern in the generation of higher harmonic amplitude in concrete using nonlinear ultrasonic test. J Adv Concrete Technol 14(5):205–214 Yao J, Cao L, Huang JF (2010) Prediction of Diagonal Crack Widths Of High-Strength Reinforced Concrete Beam By Artificial Neural Network. Adv Mater Res 163–167:992–997. https://doi.org/10.4028/www.scientific.net/AMR.163-167.992 Elbahy YI, Nehdi M, Youssef MA (2010) Artificial neural network model for deflection analysis of superelastic shape memory alloy reinforced concrete beams. Can J Civ Eng 37(6):855–865. https://doi.org/10.1139/l10-039 Yang XM, Li F (2012) Crack damage identification of reinforced concrete simply supported beam based on BP neural network. Adv Mater Res 468–471:738–741. https://doi.org/10.4028/www.scientific.net/AMR.468-471.738 Zhang J, Huang J, Tang HM, Li XH, Zhao XW (2014) Numerical Studies of reinforced concrete beam damage detection based on the piezoelectric impedance and neural network technology. Appl Mech Mater 687–691:920–924. https://doi.org/10.4028/www.scientific.net/AMM.687-691.920 Agarwalla DK, Dash AK, Tripathy B (2015) Experimental validation of a fuzzy model for damage prediction of composite beam structure. In Proceedings of the international conference on computational intelligence in data mining - 3:109–122. Smart Innovation, Systems and Technologies, vol 33. Springer, New Delhi Saathappan VRA, Raghunath PN, Suguna K (2011) Adaptive neuro-fuzzy model for performance evaluation of RC T-beams with externally bonded GFRP reinforcement. J Reinf Plast Compos 30(24):2015–2023. https://doi.org/10.1177/0731684408081448 Darain KMu, Shamshirband S, Jumaat MZ, Obaydullah M (2015) Adaptive neuro fuzzy prediction of deflection and cracking behavior of NSM strengthened RC beams. Constr Build Mater 98:276–285. https://doi.org/10.1016/j.conbuildmat.2015.08.096 Liang BL, Tian Y (2010) A novel defect evaluation method for concrete structures in infrared based on ANN and PSO algorithm. Key Eng Mater 439–440:552–557. https://doi.org/10.4028/www.scientific.net/KEM.439-440.552 Yeh J-P, Chen K (2012) Forecasting the lowest cost and steel ratio of reinforced concrete simple beams using the neural network. J Civ Eng Constr Technol 3(3):99–107 Naser M, Abu-Lebdeh G, Hawileh R (2012) Analysis of RC T-beams strengthened with CFRP plates under fire loading using ANN. Constr Build Mater 37:301–309. https://doi.org/10.1016/j.conbuildmat.2012.07.001 Mohammadhassani M, Nezamabadi-pour H, Suhatril M, Shariati M (2013) Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams. Struct Eng Mech 46(6):853–868. https://doi.org/10.12989/sem.2013.46.6.853 Anoop MB, Raghuprasad BK, Balaji Rao K (2012) A refined methodology for durability-based service life estimation of reinforced concrete structural elements considering fuzzy and random uncertainties. Comput Aided Civ Infrastruct Eng 27(3):170–186. https://doi.org/10.1111/j.1467-8667.2011.00730.x Hoque N, Jumaat MZ, Shukri AA (2017) Critical curtailment location of EBR FRP bonded RC beams using dimensional analysis and fuzzy logic expert system. Compos Struct 166:87–95. https://doi.org/10.1016/j.compstruct.2017.01.025 Köroğlu MA, Ceylan M, Arslan MH, İlki A (2012) Estimation of flexural capacity of quadrilateral FRP-confined RC columns using combined artificial neural network. Eng Struct 42:23–32. https://doi.org/10.1016/j.engstruct.2012.04.013 Yi-bin W, Guo-fang J, Ting D, Dong M (2010) Modeling confinement efficiency of FRP-confined concrete column using radial basis function neural network. In: 2010 2nd international workshop on intelligent systems and applications, 2010 Alacalı SN, Akbaş B, Doran B (2011) Prediction of lateral confinement coefficient in reinforced concrete columns using neural network simulation. Appl Soft Comput 11(2):2645–2655. https://doi.org/10.1016/j.asoc.2010.10.013 Naderpour H, Mirrashid M (2020) Confinement coefficient predictive modeling of FRP-confined RC columns. Adv Civ Eng Mater 9(1):1–21. https://doi.org/10.1520/ACEM20190145 Doran B, Yetilmezsoy K, Murtazaoglu S (2015) Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP. Eng Struct 88:74–91. https://doi.org/10.1016/j.engstruct.2015.01.039 Jakubek M (2017) Neural network prediction of load capacity for eccentrically loaded reinforced concrete columns. Comput Assist Methods Eng Sci 19(4):339–349 Lazarevska M, Cvetkovska M, Knežević M, Trombeva Gavriloska A, Milanovic M, Murgul V, Vatin N (2014) Neural network prognostic model for predicting the fire resistance of eccentrically loaded RC columns. In: Applied mechanics and materials, 2014. Trans Tech Publications, pp 276–282 Liu X, Wei H, Wu T, Liu BQ, Zhang Y (2014) Shear Strength Of Reinforced Concrete Frame Column By Neural Network Model. Adv Mater Res 1065–1069:1234–1239. https://doi.org/10.4028/www.scientific.net/AMR.1065-1069.1234 Naderpour H, Mirrashid M (2020) Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Complex Intell Syst 6(1):97–107. https://doi.org/10.1007/s40747-019-00118-2 Naderpour H, Mirrashid M (2020) Proposed soft computing models for moment capacity prediction of reinforced concrete columns. Soft Comput 24:11715–11729. https://doi.org/10.1007/s00500-019-04634-8 Ma CK, Lee YH, Awang AZ, Omar W, Mohammad S, Liang M (2019) Artificial neural network models for FRP-repaired concrete subjected to pre-damaged effects. Neural Comput Appl 31(3):711–717 Ozkul S, Ayoub A, Altunkaynak A (2014) Fuzzy-logic based inelastic displacement ratios of degrading RC structures. Eng Struct 75:590–603 Senniappan V, Subramanian J, Papageorgiou EI, Mohan S (2017) Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures. Neural Comput Appl 28(1):107–117 Naderpour H, Nagai K, Haji M, Mirrashid M (2019) Adaptive neuro-fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer-strengthened circular reinforced concrete columns. Expert Syst. https://doi.org/10.1111/exsy.12410 Xu Y, Wei S, Bao Y, Li H (2019) Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network. Struct Control Health Monit 26(3):e2313. https://doi.org/10.1002/stc.2313 Xu Y, Wei S, Bao Y, Li H (2019) Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network. Struct Control and Health Monit 26(3):e2313 Choudhary GK, Dey S Crack detection in concrete surfaces using image processing, fuzzy logic, and neural networks. In: 2012 IEEE fifth international conference on advanced computational intelligence (ICACI), 2012. IEEE, pp 404–411 Mirrashid M (2017) Comparison study of soft computing approaches for estimation of the non-ductile RC joint shear strength. Soft Comput Civ Eng 1(1):12–28. https://doi.org/10.22115/SCCE.2017.46318 Naderpour H, Mirrashid M (2019) Shear failure capacity prediction of concrete beam-column joints in terms of ANFIS and GMDH. Pract Period Struct Des Constr 24(2):04019006. https://doi.org/10.1061/(asce)sc.1943-5576.0000417 Allahyari H, Nikbin IM, Rahimi RS, Heidarpour A (2018) A new approach to determine strength of Perfobond rib shear connector in steel-concrete composite structures by employing neural network. Eng Struct 157:235–249. https://doi.org/10.1016/j.engstruct.2017.12.007 Naderpour H, Mirrashid M, Nagai K (2020) An innovative approach for bond strength modeling in FRP strip-to-concrete joints using adaptive neuro–fuzzy inference system. Eng Comput 36:1083–1100. https://doi.org/10.1007/s00366-019-00751-y Mangalathu S, Jeon J-S (2018) Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. Eng Struct 160:85–94. https://doi.org/10.1016/j.engstruct.2018.01.008 Naderpour H, Mirrashid M (2019) Classification of failure modes in ductile and non-ductile concrete joints. Eng Fail Anal 103:361–375. https://doi.org/10.1016/j.engfailanal.2019.04.047 Razavi S, Jumaat M, Ei-Shafie AH, Mohammadi P (2011) General regression neural network (GRNN) for the first crack analysis prediction of strengthened RC one-way slab by CFRP. Int J Phys Sci 6(10):2439–2446 Ibrahim A, Salim H, Flood I (2011) Damage prediction for RC slabs under near-field blasts using artificial neural network. Int J Prot Struct 2(3):315–332 Bilgehan M, Kurtoğlu AE (2016) ANFIS-based prediction of moment capacity of reinforced concrete slabs exposed to fire. Neural Comput Appl 27(4):869–881 Sadowski Ł (2013) Non-destructive evaluation of the pull-off adhesion of concrete floor layers using Rbf neural network. J Civ Eng Manag 19(4):550–560. https://doi.org/10.3846/13923730.2013.790838 Czarnecki S (2017) Non-destructive evaluation of the bond between a concrete added repair layer with variable thickness and a substrate layer using ANN. Procedia Eng 172:194–201. https://doi.org/10.1016/j.proeng.2017.02.049 Vu D-T, Hoang N-D (2015) Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach. Struct Infrastruct Eng 12(9):1153–1161. https://doi.org/10.1080/15732479.2015.1086386 Safiee N, Ashour A (2017) Prediction of punching shear capacity of rc flat slabs using artificial neural network. Asian J Civ Eng (BHRC) 18(2):285–309 Hoang N-D (2019) Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network. Measurement 137:58–70. https://doi.org/10.1016/j.measurement.2019.01.035 Naderpour H, Mirrashid M (2019) A Neuro-fuzzy model for punching shear prediction of slab-column connections reinforced with FRP. Soft Comput Civ Eng 3(1):16–26. https://doi.org/10.22115/SCCE.2018.136068.1073 Tesfamariam S, Saatcioglu M (2010) Seismic vulnerability assessment of reinforced concrete buildings using hierarchical fuzzy rule base modeling. Earthq Spectra 26(1):235–256 Mitra G, Jain KK, Bhattacharjee B (2010) Condition assessment of corrosion-distressed reinforced concrete buildings using fuzzy logic. J Perform Constr Facil 24(6):562–570. https://doi.org/10.1061/(asce)cf.1943-5509.0000137 Zheng SS, Li ZQ, Tao QL, Hu Y, Hou PJ (2011) Fuzzy AHP comprehensive approach-based damage evaluation of reinforced concrete frame structure. Adv Mater Res 243–249:5637–5640. https://doi.org/10.4028/www.scientific.net/AMR.243-249.5637 Jain KK, Bhattacharjee B (2012) Application of fuzzy concepts to the visual assessment of deteriorating reinforced concrete structures. J Constr Eng Manag 138(3):399–408. https://doi.org/10.1061/(asce)co.1943-7862.0000430 Champiri MD, Mousavizadegan SH, Moodi F (2012) A fuzzy classification system for evaluating the health condition of marine concrete structures. J Adv Concrete Technol 10(3):95–109 Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 28(8):2005–2016 Zhang YN, Liu Y (2013) Durability evaluation of concrete structure based on fuzzy extension AHP. Appl Mech Mater 454:179–182. https://doi.org/10.4028/www.scientific.net/AMM.454.179 Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Shi F, Le D-N (2017) Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm. Struct Eng Mech 63(4):429–438 Gharehbaghi S, Yazdani H, Khatibinia M (2019) Estimating inelastic seismic response of reinforced concrete frame structures using a wavelet support vector machine and an artificial neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04075-2 Li H, Hao SL, Zhang DF (2011) Shear bearing capacity forecast of reinforced concrete frame abnormal node based on the BP neural network theory. Adv Mater Res 368–373:66–71. https://doi.org/10.4028/www.scientific.net/AMR.368-373.66 Thinley K, Hao H (2017) Seismic performance of reinforced concrete frame buildings in Bhutan based on fuzzy probability analysis. Soil Dyn Earthq Eng 92:604–620. https://doi.org/10.1016/j.soildyn.2016.11.004 American Institute of Steel Construction, Specification for structural steel buildings (ANSI/AISC 360) (2010) Wei H, Du Y, Wang HJ (2012) Seismic behavior of concrete filled circular steel tubular columns based on artificial neural network. Adv Mater Res 502:189–192. https://doi.org/10.4028/www.scientific.net/AMR.502.189 Saadoon AS, Nasser KZ, Mohamed IQ (2012) A neural network model to predict ultimate strength of rectangular concrete filled steel tube beam-columns. Eng Technol J 30(19):3328–3340 Gao HG, Cheng H, Cui XF (2013) Calculation of load-carrying capacity of square concrete filled tube columns based on neural network. Appl Mech Mater 351–352:713–716. https://doi.org/10.4028/www.scientific.net/AMM.351-352.713 Ahmadi M, Naderpour H, Kheyroddin A (2019) A proposed model for axial strength estimation of non-compact and slender square CFT columns. Iran J Sci Technol Trans Civ Eng 43:131–147 Basarir H, Elchalakani M, Karrech A (2019) The prediction of ultimate pure bending moment of concrete-filled steel tubes by adaptive neuro-fuzzy inference system (ANFIS). Neural Comput Appl 31(2):1239–1252 Lee N-K, Souri H, Lee H-K (2014) Neural network application overview in prediction of properties of cement-based mortar and concrete. In: The 2013 world congress on advances in civil, environmental, & materials research (ACEM14), 2014. The 2013 World Congress on Advances in Civil, Environmental, & Materials Deng Y-q, Wang P-m (2010) Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks. J Zhejiang Univ Sci A 11(3):212–222 Tran T-H, Hoang N-D (2016) Predicting colonization growth of algae on mortar surface with artificial neural network. J Comput Civ Eng 30(6):04016030 Naderpour H, Mirrashid M (2017) Compressive strength of mortars admixed with wollastonite and microsilica. Mater Sci Forum 890:415–418. https://doi.org/10.4028/www.scientific.net/MSF.890.415 Naderpour H, Mirrashid M (2018) A computational model for compressive strength of mortars admixed with mineral materials. Comput Eng Phys Model 1(4):16–25. https://doi.org/10.22115/CEPM.2018.136069.1031 Naderpour H, Mirrashid M (2018) An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals. J Build Eng 19:205–215. https://doi.org/10.1016/j.jobe.2018.05.012 Tran T-H, Hoang N-D (2019) Predicting algal appearance on mortar surface with ensembles of adaptive neuro fuzzy models: a comparative study of ensemble strategies. Int J Mach Learn Cybern 10: 1687-1704 Kang S-M, Choi K-K (2013) Shear strength model for fibre reinforced polymer shear reinforced concrete beams. Proc Inst Civ Eng Struct Build 166(3):139–152. https://doi.org/10.1680/stbu.11.00032 Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 32(5):361–378 Dung CV, Anh LD (2019) Autonomous concrete crack detection using deep fully convolutional neural network. Autom Constr 99:52–58. https://doi.org/10.1016/j.autcon.2018.11.028 Li S, Zhao X (2019) Image-based concrete crack detection using convolutional neural network and exhaustive search technique. Adv Civ Eng 2019:1–12. https://doi.org/10.1155/2019/6520620 Liang D, Zhou X-F, Wang S, Liu C-J (2019) Research on concrete cracks recognition based on dual convolutional neural network. KSCE J Civ Eng. https://doi.org/10.1007/s12205-019-2030-x Ince R (2010) Artificial neural network-based analysis of effective crack model in concrete fracture. Fatigue Fract Eng Mater Struct. https://doi.org/10.1111/j.1460-2695.2010.01469.x Wang TC, Peng QM, Liu WL, Feng LF study on shrinkage of concrete based on BP neural network. In: Advanced materials research, 2011. Trans Tech Publications, pp 3249–3257 Bal L, Buyle-Bodin F (2013) Artificial neural network for predicting drying shrinkage of concrete. Constr Build Mater 38:248–254 Ji T, Zhuang YZ, Chen BC, Huang ZB (2011) Behavioral prediction of reactive powder concrete based on artificial neural network. In: Advanced materials research, 2011. Trans Tech Publications, pp 1030–1033 Hodhod O, Ahmed H (2013) Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete. HBRC J 9(1):15–21 Ghafoori N, Najimi M, Sobhani J, Aqel MA (2013) Predicting rapid chloride permeability of self-consolidating concrete: a comparative study on statistical and neural network models. Constr Build Mater 44:381–390. https://doi.org/10.1016/j.conbuildmat.2013.03.039 Bal L, Buyle-Bodin F (2014) Artificial neural network for predicting creep of concrete. Neural Comput Appl 25(6):1359–1367 De Jesus R, Collado J, Go J, Rosanto M, Tan J (2017) Modelling of carbonation of reinforced concrete structures in intramuros, manila using artificial neural network. Int J Geomate 13(35):87–92 Garoosiha H, Ahmadi J, Bayat H, Formisano A (2019) The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network. Cogent Eng. https://doi.org/10.1080/23311916.2019.1609179 Noh Y, Koo D, Kang Y-M, Park D, Lee D (2017) Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering. In: 2017 International conference on applied system innovation (ICASI), 2017. IEEE, pp 877–880 Venkiteela G, Gregori A, Sun Z, Shah SP (2010) Artificial neural network modeling of early-age dynamic young’s modulus of normal concrete. ACI Mater J 107(3):282 Ashrafi HR, Jalal M, Garmsiri K (2010) Prediction of load–displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network. Expert Syst Appl 37(12):7663–7668. https://doi.org/10.1016/j.eswa.2010.04.076 Naderpour H, Kheyroddin A, Amiri GG (2010) Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Compos Struct 92(12):2817–2829 Boukhatem B, Ghrici M, Kenai S, Tagnit-Hamou A (2011) Prediction of efficiency factor of ground-granulated blast-furnace slag of concrete using artificial neural network. ACI Mater J 108(1):55–63 Nazari A, Riahi S (2011) Prediction split tensile strength and water permeability of high strength concrete containing TiO2 nanoparticles by artificial neural network and genetic programming. Compos B Eng 42(3):473–488. https://doi.org/10.1016/j.compositesb.2010.12.004 Ghorpade VG, Rao HS, Beulah M (2012) Development of genetic algorithm based neural network model for predicting workability of high performance concrete. Int J Res Rev Appl Math Comput Sci 2:40–50 Jalal M, Ramezanianpour AA, Pouladkhan AR, Tedro P (2013) Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders. Neural Comput Appl 23(2):455–470 Shah AA, Alsayed SH, Abbas H, Al-Salloum YA (2012) Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network. Constr Build Mater 29:42–50. https://doi.org/10.1016/j.conbuildmat.2011.10.038 Altun F, Dirikgil T (2013) The prediction of prismatic beam behaviours with polypropylene fiber addition under high temperature effect through ANN, ANFIS and fuzzy genetic models. Compos B Eng 52:362–371. https://doi.org/10.1016/j.compositesb.2013.04.015 Hodhod O, Ahmed H (2014) Modeling the corrosion initiation time of slag concrete using the artificial neural network. HBRC J 10(3):231–234 Cheng J, Li Q (2012) Artificial neural network-based response surface methods for reliability analysis of pre-stressed concrete bridges. Struct Infrastruct Eng 8(2):171–184 Yan Y, Ren Q, Xia N, Shen L, Gu J (2015) Artificial neural network approach to predict the fracture parameters of the size effect model for concrete. Fatigue Fract Eng Mater Struct 38(11):1347–1358 Mansouri I, Ozbakkaloglu T, Kisi O, Xie T (2016) Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques. Mater Struct 49(10):4319–4334 Mansouri I, Gholampour A, Kisi O, Ozbakkaloglu T (2018) Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques. Neural Comput Appl 29(3):873–888 Nikbin MI, Rahimi RS, Allahyari H (2017) A new empirical formula for prediction of fracture energy of concrete based on the artificial neural network. Eng Fract Mech 186:466–482. https://doi.org/10.1016/j.engfracmech.2017.11.010 Kellouche Y, Boukhatem B, Ghrici M, Tagnit-Hamou A (2019) Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network. Neural Comput Appl 31(2):969–988 Nguyen T, Kashani A, Ngo T, Bordas S (2019) Deep neural network with high-order neuron for the prediction of foamed concrete strength. Comput Aided Civil and Infrastruct Eng 34(4):316–332 Xu Y, Jin R (2018) Measurement of reinforcement corrosion in concrete adopting ultrasonic tests and artificial neural network. Constr Build Mater 177:125–133. https://doi.org/10.1016/j.conbuildmat.2018.05.124 Chung L, Hur M-W, Park T (2018) Performance evaluation of CFRP reinforced concrete members utilizing fuzzy technique. Int J Concrete Struct Mater 12(1):78 Güler K, Demir F, Pakdamar F (2012) Stress–strain modelling of high strength concrete by fuzzy logic approach. Constr Build Mater 37:680–684. https://doi.org/10.1016/j.conbuildmat.2012.07.069 Li RT (2014) Application of fuzzy pattern recognition in spalling risk evaluation of concrete structures at high temperature. Adv Mater Res 919–921:451–454. https://doi.org/10.4028/www.scientific.net/AMR.919-921.451 Dilmaç H, Demir F (2013) Stress–strain modeling of high-strength concrete by the adaptive network-based fuzzy inference system (ANFIS) approach. Neural Comput Appl 23(1):385–390 Naderpour H, Mirrashid M (2020) Estimating the compressive strength of eco-friendly concrete incorporating recycled coarse aggregate using neuro-fuzzy approach. J Clean Prod 265:121886. https://doi.org/10.1016/j.jclepro.2020.121886 Kwon S-J, Song H-W (2010) Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling. Cem Concr Res 40(1):119–127. https://doi.org/10.1016/j.cemconres.2009.08.022 Lee J-H, Lee J-J, Cho B-S (2012) Effective prediction of thermal conductivity of concrete using neural network method. Int J Concrete Struct Mater 6(3):177–186. https://doi.org/10.1007/s40069-012-0016-x Wang B, Man T, Jin H (2015) Prediction of expansion behavior of self-stressing concrete by artificial neural networks and fuzzy inference systems. Constr Build Mater 84:184–191. https://doi.org/10.1016/j.conbuildmat.2015.03.059 Abdalla JA, Hawileh R, Al-Tamimi A (2011) Prediction of FRP-concrete ultimate bond strength using. Artif Neural Netw. https://doi.org/10.1109/icmsao.2011.5775518 Makni M, Daoud A, Karray MA, Lorrain M (2014) Artificial neural network for the prediction of the steel–concrete bond behaviour. Eur J Environ Civ Eng 18(8):862–881. https://doi.org/10.1080/19648189.2014.909745 Hoang N-D, Tran X-L, Nguyen H (2020) Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model. Neural Comput Appl 32:7289–7309 Toghroli A, Suhatril M, Ibrahim Z, Safa M, Shariati M, Shamshirband S (2018) Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J Intell Manuf 29(8):1793–1801 Yan F, Lin Z (2016) New strategy for anchorage reliability assessment of GFRP bars to concrete using hybrid artificial neural network with genetic algorithm. Compos B Eng 92:420–433. https://doi.org/10.1016/j.compositesb.2016.02.008