Abstract
Shear failure in concrete specimens and beams is one of the most sensitive cases in construction projects. Since the shear strength of specimens is affected by various parameters, it is important to study them to arrive at a predictive relationship or model. This research has used the computational methods of machine learning to achieve a new evaluation of the shear strength of reinforced concrete (RC) beams. In the first step, 85 laboratory samples were examined. These samples had different geometric and non-geometric properties. The effect of each parameter was examined for smart models, then the optimal state of the parameters was determined. By optimizing the number of parameters, basic models were developed. The different structures of these models were investigated for the accurate evaluation of the shear strength of the samples. Finally, in the second step, the particle swarm optimization (PSO) and genetic algorithm (GA) were used to perpetuate and optimize the weights of the base models. The hybrid models were developed and applied with different structures for evaluation of shear strength and the best models were selected from them. The results showed that both models offer better performance than the base model. However, the GA-based model achieved higher accuracy and less error than the PSO-based model. Finally, the performance of these models was used for new data to evaluate their performance. As the main achievement of this research, these methods can be recommended and implemented for other conditions as well.
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References
Adebar P, Mindess S, Pierre DS, Olund B (1997) Shear tests of fiber concrete beams without stirrups. Structural Journal 94(1):68–76, DOI: https://doi.org/10.14359/462
Adhikary BB, Mutsuyoshi H (2006) Prediction of shear strength of steel fiber RC beams using neural networks. Construction and Building Materials 20(9):801–811, DOI: https://doi.org/10.1016/j.conbuildmat.2005.01.047
Armaghani DJ, Koopialipoor M, Bahri M, Hasanipanah M, Tahir MM (2020) A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bulletin of Engineering Geology and the Environment 1–17, DOI: https://doi.org/10.1007/s10064-020-01834-7
Armaghani DJ, Koopialipoor M, Marto A, Yagiz S (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock Mechanics and Geotechnical Engineering, DOI: https://doi.org/10.1016/j.jrmge.2019.01.002
Armaghani DJ, Yagiz S, Mohamad ET, Zhou J (2021) Prediction of TBM performance in fresh through weathered granite using empirical and statistical approaches. Tunnelling and Underground Space Technology 118:104183, DOI: https://doi.org/10.1016/j.tust.2021.104183
Ashour SA, Hasanain GS, Wafa FF (1992) Shear behavior of high-strength fiber reinforced concrete beams. Structural Journal 89(2): 176–184, DOI: https://doi.org/10.14359/2946
Asteris PG, Koopialipoor M, Armaghani DJ, Kotsonis EA, Lourenço PB (2021) Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Computing and Applications 1–33, DOI: https://doi.org/10.1007/s00521-021-06004-8
Beale R, Jackson T (1990) Neural Computing-an introduction. CRC Press, DOI: https://doi.org/10.1201/9781420050431
Cai M, Koopialipoor M, Armaghani DJ, Thai Pham B (2020) Evaluating slope deformation of earth dams due to earthquake shaking using MARS and GMDH techniques. Applied Sciences 10(4):1486, DOI: https://doi.org/10.3390/app10041486
Chahnasir ES, Zandi Y, Shariati M, Dehghani E, Toghroli A, Mohamed ET, Shariati A, Safa M, Wakil K, Khorami M (2018) Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Structures and Systems 22(4):413–424, DOI: https://doi.org/10.12989/sss.2018.22.4.413
Chen F, Jin Z, Wang E, Wang L, Jiang Y, Guo P, Gao X, He X (2021a) Relationship model between surface strain of concrete and expansion force of reinforcement rust. Scientific Reports 11 DOI: https://doi.org/10.1038/s41598-021-83376-w
Chen F, Zhong Y, Gao X, Jin Z, Wang E, Zhu F, Shao X, He X (2021b) Non-uniform model of relationship between surface strain and rust expansion force of reinforced concrete. Scientific Reports 11(1):1–9, DOI: https://doi.org/10.1038/s41598-021-88146-2
Chuang PH, Goh ATC, Wu X (1998) Modeling the capacity of pin-ended slender reinforced concrete columns using neural networks. Journal of Structural Engineering 124(7):830–838, DOI: https://doi.org/10.1061/(ASCE)0733-9445(1998)124:7(830)
Cucuzza R, Rosso MM, Marano GC (2021) Optimal preliminary design of variable section beams criterion. SN Applied Sciences 3(8):1–12, DOI: https://doi.org/10.1007/S42452-021-04702-5
Di Trapani F, Malavisi M, Marano GC, Sberna AP, Greco R (2020) Optimal seismic retrofitting of reinforced concrete buildings by steel-jacketing using a genetic algorithm-based framework. Engineering Structures 219:110864, DOI: https://doi.org/10.1016/j.engstruct.2020.110864
Di Trapani F, Sberna AP, Marano GC (2021) A new genetic algorithm-based framework for optimized design of steel-jacketing retrofitting in shear-critical and ductility-critical RC frame structures. Engineering Structures 243:112684, DOI: https://doi.org/10.1016/j.engstruct.2021.112684
Effati M, Rajabi MA, Samadzadegan F, Shabani S (2014) A geospatial based neuro-fuzzy modeling for regional transportation corridors hazardous zones identification. International Journal of Civil Engineering 12
Emami H, Emami S (2021) Application of whale optimization algorithm combined with adaptive neuro-fuzzy inference system for estimating suspended sediment load. Journal of Soft Computing in Civil Engineering 5 (3):https://doi.org/10.22115/SCCE.2021.281972.1300, DOI: https://doi.org/10.22115/SCCE.2021.281972.1300
Erb RJ (1993) Introduction to backpropagation neural network computation. Pharmaceutical Research 10(2):165–170, DOI: https://doi.org/10.1023/A:1018966222807
Feng J, Chen B, Sun W, Wang Y (2021) Microbial induced calcium carbonate precipitation study using Bacillus subtilis with application to self-healing concrete preparation and characterization. Construction and Building Materials 280:122460, DOI: https://doi.org/10.1016/j.conbuildmat.2021.122460
Ghaleini EN, Koopialipoor M, Momenzadeh M, Sarafraz ME, Mohamad ET, Gordan B (2018) A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Engineering with Computers 1–12, DOI: https://doi.org/10.1007/s00366-018-0625-3
Gurney K (1997) An introduction to neural networks. CRC press
Hajela P, Berke L (1991) Neurobiological computational models in structural analysis and design. Computers & Structures 41(4):657–667, DOI: https://doi.org/10.1016/0045-7949(91)90178-O
Hamzehkolaei NS, Alizamir M (2021) Performance evaluation of machine learning algorithms for seismic retrofit cost estimation using structural parameters. Journal of Soft Computing in Civil Engineering 5(3):32–57, DOI: https://doi.org/10.22115/SCCE.2021.284630.1312
Hebb DO (1949) The organization of behavior: A neuropsychological theory. J. Wiley; Chapman & Hall
Holland JH (1992) Genetic algorithms. Scientific American 267(1):66–73
Huang L, Asteris PG, Koopialipoor M, Armaghani DJ, Tahir MM (2019) Invasive weed optimization technique-based ANN to the prediction of rock tensile strength. Applied Sciences 9(24):5372, DOI: https://doi.org/10.3390/app9245372
Huang J, Duan T, Lei Y, Hasanipanah M (2020) Finite element modeling for the antivibration pavement used to improve the slope stability of the open-pit mine. Shock and Vibration 2020, DOI: https://doi.org/10.1155/2020/6650780
Huang J, Leandri P, Cuciniello G, Losa M (2021a) Mix design and laboratory characterisation of rubberised mixture used as damping layer in pavements. International Journal of Pavement Engineering 1–15, DOI: https://doi.org/10.1080/10298436.2020.1869975
Huang J, Losa M, Leandri P, Kumar SQ Zhang J, Sun Y (2021b) Potential anti-vibration pavements with damping layer: Finite element (FE) modeling, validation, and parametrical studies. Construction and Building Materials 281:122550, DOI: https://doi.org/10.1016/j.conbuildmat.2021.122550
Huang J, Shiva Kumar G, Ren J, Zhang J, Sun Y (2021c) Accurately predicting dynamic modulus of asphalt mixtures in low-temperature regions using hybrid artificial intelligence model. Construction and Building Materials 297:123655, DOI: https://doi.org/10.1016/j.conbuildmat.2021.123655
Huang J, Sun Y (2020) Viscoelastic analysis of the damping asphalt mixtures (DAMs) made with a high content of asphalt rubber (AR). Advances in Civil Engineering 2020:1–12, DOI: https://doi.org/10.1155/2020/8826926
Huang J, Sun Y, Zhang J (2021d) Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm. Engineering with Computers, DOI: https://doi.org/10.1007/s00366-021-01305-x
Huang J, Zhang J, Ren J, Chen H (2021e) Anti-rutting performance of the damping asphalt mixtures (DAMs) made with a high content of asphalt rubber (AR). Construction and Building Materials 271:121878, DOI: https://doi.org/10.1016/j.conbuildmat.2020.121878
Huang J, Zhang Y, Sun Y, Ren J, Zhao Z, Zhang J (2021f) Evaluation of pore size distribution and permeability reduction behavior in pervious concrete. Construction and Building Materials 290:123228, DOI: https://doi.org/10.1016/j.conbuildmat.2021.123228
Jahed Armaghani D, Shoib RSNSBR, Faizi K, Rashid ASA (2017) Developing a hybrid PSO-ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Computing and Applications 28(2), DOI: https://doi.org/10.1007/s00521-015-2072-z
Jiang Y, Yuan K, Deng C, Tian T (2020) Fatigue performance of cement-stabilized crushed gravel produced using vertical vibration compaction method. Journal of Materials in Civil Engineering 32(11):04020318, DOI: https://doi.org/10.1061/(ASCE)MT.1943-5533.0003401
Kaveh A, Ghaffarian R (2015) Shape optimization of arch dams with frequency constraints by enhanced charged system search algorithm and neural network. International Journal of Civil Engineering 13(1): 102–111, DOI: https://doi.org/10.22068/IJCE.13.1.102
Kaveh A, Maniat M (2014) Damage detection in skeletal structures based on charged system search optimization using incomplete modal data. International Journal of Civil Engineering 12(2):193–200
Kennedy J (2011) Particle swarm optimization. Encyclopedia of machine learning, Springer, 760–766
Khuntia M, Stojadinovic B, Goel SC (1999) Shear strength of normal and high-strength fiber reinforced concrete beams without stirrups. Structural Journal 96(2):282–289, DOI: https://doi.org/10.14359/620
Koopialipoor M, Armaghani DJ, Haghighi M, Ghaleini EN (2017) A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels. Bulletin of Engineering Geology and the Environment 78:981–990, DOI: https://doi.org/10.1007/s10064-017-1116-2
Koopialipoor M, Armaghani DJ, Hedayat A, Marto A, Gordan B (2018a) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Computing 23:5913–5929, DOI: https://doi.org/10.1007/s00500-018-3253-3
Koopialipoor M, Fahimifar A, Ghaleini EN, Momenzadeh M, Armaghani DJ (2019a) Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Engineering with Computers 36:345–357, DOI: https://doi.org/10.1007/s00366-019-00701-8
Koopialipoor M, Fallah A, Armaghani DJ, Azizi A, Mohamad ET (2018b) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Engineering with Computers 35:243–256, DOI: https://doi.org/10.1007/s00366-018-0596-4
Koopialipoor M, Ghaleini EN, Tootoonchi H, Jahed Armaghani D, Haghighi M, Hedayat A (2019b) Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN. Environmental Earth Sciences 78(5):165, DOI: https://doi.org/10.1007/s12665-019-8163-x
Koopialipoor M, Murlidhar BR, Hedayat A, Armaghani DJ, Gordan B, Mohamad ET (2019c) The use of new intelligent techniques in designing retaining walls. Engineering with Computers 36:283–294, DOI: https://doi.org/10.1007/s00366-018-00700-1
Koopialipoor M, Nikouei SS, Marto A, Fahimifar A, Armaghani DJ, Mohamad ET (2018c) Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bulletin of Engineering Geology and the Environment 78:3799–3813, DOI: https://doi.org/10.1007/s10064-018-1349-8
Koopialipoor M, Noorbakhsh A, Noroozi Ghaleini E, Jahed Armaghani D, Yagiz S (2019d) A new approach for estimation of rock brittleness based on non-destructive tests. Nondestructive Testing and Evaluation 34:354–375, DOI: https://doi.org/10.1080/10589759.2019.1623214
Koopialipoor M, Tootoonchi H, Jahed Armaghani D, Tonnizam Mohamad E, Hedayat A (2019e) Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bulletin of Engineering Geology and the Environment 78:6347–6360, DOI: https://doi.org/10.1007/s10064-019-01538-7
Lee S-C (2003) Prediction of concrete strength using artificial neural networks. Engineering Structures 25(7):849–857, DOI: https://doi.org/10.1016/S0141-0296(03)00004-X
Li VC, Ward R, Hamza AM (1992) Steel and synthetic fibers as shear reinforcement
Liao X, Khandelwal M, Yang H, Koopialipoor M, Murlidhar BR (2019) Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques. Engineering with Computers 36:499–510, DOI: https://doi.org/10.1007/s00366-019-00711-6
Lim TY, Paramasivam P, Lee SL (1987) Shear and moment capacity of reinforced steel-fibre-concrete beams. Magazine of Concrete Research 39(140):148–160, DOI: https://doi.org/10.1680/MACR.1987.39.140.148
Lu S, Koopialipoor M, Asteris PG, Bahri M, Armaghani DJ (2020) A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs. Materials 13(17):3902, DOI: https://doi.org/10.3390/ma13173902
Luo Y, Zheng H, Zhang H, Liu Y (2021) Fatigue reliability evaluation of aging prestressed concrete bridge accounting for stochastic traffic loading and resistance degradation. Advances in Structural Engineering 24(13):3021–3029, DOI: https://doi.org/10.1177/13694332211017995
Mahdiyar A, Jahed Armaghani D, Koopialipoor M, Hedayat A, Abdullah A, Yahya K (2020) Practical risk assessment of ground vibrations resulting from blasting, using gene expression programming and monte carlo simulation techniques. Applied Sciences 10(2):472, DOI: https://doi.org/10.3390/app10020472
Mohamad ET, Armaghani DJ, Momeni E, Yazdavar AH, Ebrahimi M (2016) Rock strength estimation: A PSO-based BP approach. Neural Computing and Applications 30:1635–1646, DOI: https://doi.org/10.1007/s00521-016-2728-3
Mohamad ET, Koopialipoor M, Murlidhar BR, Rashiddel A, Hedayat A, Armaghani DJ (2019) A new hybrid method for predicting ripping production in different weathering zones through in-situ tests. Measurement 147, DOI: https://doi.org/10.1016/j.measurement.2019.07.054
Mou B, Bai Y (2018) Experimental investigation on shear behavior of steel beam-to-CFST column connections with irregular panel zone. Engineering Structures 168:487–504, DOI: https://doi.org/10.1016/j.engstruct.2018.04.029
Naderpour H, Kheyroddin A, Amiri GG (2010) Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Composite Structures 92(12):2817–2829, DOI: https://doi.org/10.1016/j.compstruct.2010.04.008
Narayan R, Darwish IYS (1988) Fiber concrete deep beams in shear. Structural Journal 85(2):141–149, DOI: https://doi.org/10.14359/2698
Narayanan R, Darwish IYS (1987) Use of steel fibers as shear reinforcement. Structural Journal 84(3):216–227, DOI: https://doi.org/10.14359/2654
Ni H-G, Wang J-Z (2000) Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research 30(8):1245–1250, DOI: https://doi.org/10.1016/S0008-8846(00)00345-8
Oh BH, Lim DH, Yoo SW, Kim ES (1998) Shear behaviour and shear analysis of reinforced concrete beams containing steel fibres. Magazine of Concrete Research 50(4):283–291, DOI: https://doi.org/10.1680/MACR.1998.50.4.283
Oreta AWC, Kawashima K (2003) Neural network modeling of confined compressive strength and strain of circular concrete columns. Journal of Structural Engineering 129(4):554–561, DOI: https://doi.org/10.1061/(ASCE)0733-9445(2003)129:4(554)
Parsajoo M, Armaghani DJ, Mohammed AS, Khari M, Jahandari S (2021) Tensile strength prediction of rock material using non-destructive tests: A comparative intelligent study. Transportation Geotechnics 31:100652, DOI: https://doi.org/10.1016/J.TRGEO.2021.100652
Perera R, Barchín M, Arteaga A, De Diego A (2010) Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks. Composites Part B: Engineering 41(4): 287–298, DOI: https://doi.org/10.1016/j.compositesb.2010.03.003
Rabunal JR, Puertas J (2006) “Hybrid system with artificial neural networks and evolutionary computation in civil engineering” Artificial Neural Networks in real-life applications, IGI Global 166–187, DOI: https://doi.org/10.4018/978-1-59140-902-1.CH008
Ren J, Xu Y, Zhao Z, Chen J, Cheng Y, Huang J, Yang C, Wang J (2022b) Fatigue prediction of semi-flexible composite mixture based on damage evolution. Construction and Building Materials 318:126004, DOI: https://doi.org/10.1016/j.conbuildmat.2021.126004
Ren J, Xue B, Zhang L, Liu W, Li D, Xu Y (2020) Characterization and prediction of rutting resistance of rock asphalt mixture under the coupling effect of water and high-temperature. Construction and Building Materials 254:119316, DOI: https://doi.org/10.1016/j.conbuildmat.2020.119316
Ren J, Yin C (2022) Investigating mechanical characteristics of aggregate structure for road materials. International Journal of Pavement Engineering 23(2):372–386, DOI: https://doi.org/10.1080/10298436.2020.1748189
Ren J, Zhang L, Zhao H, Zhao Z, Wang S (2022a) Determination of the fatigue equation for the cement-stabilized cold recycled mixtures with road construction waste materials based on data-driven. International Journal of Fatigue 158:106765, DOI: https://doi.org/10.1016/j.ijfatigue.2022.106765
Rosso MM, Cucuzza R, Di Trapani F, Marano GC (2021) Nonpenalty machine learning constraint handling using PSO-SVM for structural optimization. Advances in Civil Engineering, DOI: https://doi.org/10.1155/2021/6617750
Sanad A, Saka MP (2001) Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks. Journal of Structural Engineering 127(7):818–828, DOI: https://doi.org/10.1061/(ASCE)0733-9445(2001)127:7(818)
Simpson PK (1990) Artificial neural systems. Pergamon
Sun L, Koopialipoor M, Armaghani DJ, Tarinejad R, Tahir MM (2019) Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples. Engineering with Computers 37: 1133–1145, DOI: https://doi.org/10.1007/s00366-019-00875-1
Swamy RN, Ali SAR (1982) Punching shear behavior of reinforced slab-column connections made with steel fiber concrete. Journal Proceedings 79:392–406
Swamy RN, Jones R, Chiam ATP (1993) Influence of steel fibers on the shear resistance of lightweight concrete I-beams. Structural Journal 90(1):103–114, DOI: https://doi.org/10.14359/4201
Tan KH, Murugappan K, Paramasivam P (1993) Shear behavior of steel fiber reinforced concrete beams. Structural Journal 90(1):3–11, DOI: https://doi.org/10.14359/9646
Tang D, Gordan B, Koopialipoor M, Jahed Armaghani D, Tarinejad R, Thai Pham B, Huynh V Van (2020) Seepage analysis in short embankments using developing a metaheuristic method based on governing equations. Applied Sciences 10(5):1761, DOI: https://doi.org/10.3390/app10051761
Toghroli A, Mohammadhassani M, Suhatril M, Shariati M, Ibrahim Z (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel and Composite Structures 17(5):623–639, DOI: https://doi.org/10.12989/scs.2014.17.5.623
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. Journal of Intelligent Manufacturing 29(8):1793–1801, DOI: https://doi.org/10.1007/s10845-016-1217-y
Xu C, Gordan B, Koopialipoor M, Armaghani DJ, Tahir MM, Zhang X (2019) Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access 7:94692–94700, DOI: https://doi.org/10.1109/ACCESS.2019.2927632
Xu D, Liu Q, Qin Y, Chen B (2020) Analytical approach for crack identification of glass fiber reinforced polymer-sea sand concrete composite structures based on strain dissipations. Structural Health Monitoring 1475921720974290, DOI: https://doi.org/10.1177/1475921720974290
Xu J, Wu Z, Chen H, Shao L, Zhou X, Wang S (2021) Study on strength behavior of basalt fiber-reinforced loess by digital image technology (DIT) and scanning electron microscope (SEM). Arabian Journal for Science and Engineering 46:11319–11338, DOI: https://doi.org/10.1007/s13369-021-05787-1
Yang H, Koopialipoor M, Armaghani DJ, Gordan B, Khorami M, Tahir MM (2019) Intelligent design of retaining wall structures under dynamic conditions. Steel and Composite Structures 31(6):629–640, DOI: https://doi.org/10.12989/scs.2019.31.6.629
Yun C-B, Yi J-H, Bahng EY (2001) Joint damage assessment of framed structures using a neural networks technique. Engineering Structures 23(5):425–435, DOI: https://doi.org/10.1016/S0141-0296(00)00067-5
Zhang W, Tang Z, Yang Y, Wei J, Stanislav P (2021) Mixed-mode debonding behavior between CFRP plates and concrete under fatigue loading. Journal of Structural Engineering 147(5):4021055, DOI: https://doi.org/10.1061/(ASCE)ST.1943-541X.0003032
Zhao Y, Noorbakhsh A, Koopialipoor M, Azizi A, Tahir MM (2019) A new methodology for optimization and prediction of rate of penetration during drilling operations. Engineering with Computers 36:587–595, DOI: https://doi.org/10.1007/s00366-019-00715-2
Zhou J, Guo H, Koopialipoor M, Armaghani DJ, Tahir MM (2020a) Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm. Engineering with Computers 1–16, DOI: https://doi.org/10.1007/s00366-019-00908-9
Zhou J, Koopialipoor M, Li E, Armaghani DJ (2020b) Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system. Bulletin of Engineering Geology and the Environment 79:4265–4279, DOI: https://doi.org/10.1007/s10064-020-01788-w
Zhou J, Koopialipoor M, Murlidhar BR, Fatemi SA, Tahir MM, Jahed Armaghani D, Li C (2019) Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance. Natural Resources Research 29:625–639, DOI: https://doi.org/10.1007/s11053-019-09519-z
Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Engineering Geology 96(3):141–158, DOI: https://doi.org/10.1016/j.enggeo.2007.10.009
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This research was funded by the National Natural Science Foundation of China (Grant No. 52108426, 51808326) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20210513).
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Huang, J., Zhou, M., Zhang, J. et al. The Use of GA and PSO in Evaluating the Shear Strength of Steel Fiber Reinforced Concrete Beams. KSCE J Civ Eng 26, 3918–3931 (2022). https://doi.org/10.1007/s12205-022-0961-0
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DOI: https://doi.org/10.1007/s12205-022-0961-0