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A robust approach to shear strength prediction of reinforced concrete deep beams using ensemble learning with SHAP interpretability

  • Application of soft computing
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Abstract

The behavior of reinforced concrete (RC) deep beams is complex and difficult to predict due to factors such as compressive and shear stress and beam geometry. To address this challenge, researchers have proposed various machine learning models such as Artificial Neural Network, Decision Tree, Support Vector Machine, Adaptive Boosting, Extreme Gradient Boosting, Random Forest, Gradient Boosting, and Voting Regressor. In this study, the authors evaluated the performance of these models in predicting shear strength of RC deep beams by using metrics such as \(R^{2}\), Mean Squared Error, Root Mean Squared Error, Mean Absolute Percentage Error and Mean Absolute Error. Furthermore, the authors optimize the ensemble learning models using customized hyperparameters. The XGBoost model exhibited the highest accuracy with an \(R^{2}\) value of 0.92 and the least model error, with MAE of 29.65 and RMSE of 47.76 and MAPE of 9.79.The authors compared these models with mechanics-driven models from different country codes including the United States, China, Europe, British (CIRIA), Canada and found that ensemble learning models, specifically XGBoost, outperformed mechanics-driven models. The authors used an explainable machine learning (EML) technique called SHapley Additive exPlanations (SHAP) to gain additional insights into the developed XGBoost model. The outcomes of feature selection and SHAP analysis suggest that the grade of concrete and beam geometry predominantly influence the prediction of shear strength in RC deep beams, whereas steel properties exert minimal impact in this regard.

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Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AI:

Artificial intelligence

ML:

Machine learning

RC:

Reinforced concrete

ACI:

American Concrete Institute

DT:

Decision tree

SVM:

Support vector machines

ANN:

Artificial neural networks

MAE:

Mean absolute error

RMSE:

Root mean squared error

MAPE:

Mean absolute percentage error

GBRT:

Gradient boosting regression tree

RF:

Random forest

SHAP:

Shapley additive explanations

SHM:

Structural health monitoring

EML:

Explainable machine learning

ACI:

American Concrete institute

WOR:

Without web reinforcements

WHR:

Horizontal web reinforcement

WVR:

Vertical web reinforcements

WHVR:

Both horizontal and vertical web reinforcement

TENN:

Transfer ensemble neural network

\(l_{0}\) :

Beam span

h :

Height

\(h_{0}\) :

Effective height

b :

Width

a :

Span

\(\rho _{l}\) :

Reinforcement ratio

\(f_{y l}\) :

Reinforcement strength

\(\rho _{\textrm{h}}\) :

Horizontal reinforcement ratio

\(s_{\textrm{h}}\) :

Horizontal reinforcement spacing

\(f_{y \textrm{h}}\) :

Horizontal reinforcement strength

\(\rho _{\textrm{v}}\) :

Vertical reinforcement ratio

\(s_{\textrm{v}}\) :

Vertical reinforcement spacing

\(f_{y \textrm{v}}\) :

Vertical reinforcement strength

\(f_{\textrm{c}}^{\prime }\) :

Concrete strength

\(V_{u}\) :

Shear strength

References

  • Abuodeh OR, Abdalla JA, Hawileh RA (2020a) Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques. Compos Struct 234:111698

  • Abuodeh OR, Abdalla JA, Hawileh RA (2020b) Assessment of compressive strength of ultra-high performance concrete using deep machine learning techniques. Appl Soft Comput 95:106552

  • Aguilar G, Matamoros AB, Parra-Montesinos G, Ramírez JA, Wight JK (2002) Experimental evaluation of design procedures for shear strength of deep reinfoced concrete beams. American Concrete Institute, London

    Google Scholar 

  • Almasabha G, Al-Shboul KF, Shehadeh A, Alshboul O (2023) Machine learning-based models for predicting the shear strength of synthetic fiber reinforced concrete beams without stirrups. In: Structures, vol 52. Elsevier, London, pp 299–311

  • Alobaidi MH, Chebana F, Meguid MA (2018) Robust ensemble learning framework for day-ahead forecasting of household based energy consumption. Appl Energy 212:997–1012

    Google Scholar 

  • Al-Osta M, Isa M, Baluch M, Rahman M (2017) Flexural behavior of reinforced concrete beams strengthened with ultra-high performance fiber reinforced concrete. Constr Build Mater 134:279–296

    Google Scholar 

  • Al-Ta’an S, Al-Feel J (1990) Evaluation of shear strength of fibre-reinforced concrete beams. Cement Concr Compos 12:87–94

    Google Scholar 

  • Aristodemou L, Tietze F (2018) The state-of-the-art on intellectual property analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Inf 55:37–51

    Google Scholar 

  • Armaghani DJ, Hatzigeorgiou GD, Karamani C, Skentou A, Zoumpoulaki I, Asteris PG (2019) Soft computing-based techniques for concrete beams shear strength. Proc Struct Integr 17:924–933

    Google Scholar 

  • Arup O (1977) Partners, the design of deep beams in reinforced concrete. CIRIA Guide 144

  • Asteris PG, Armaghani DJ, Hatzigeorgiou GD, Karayannis CG, Pilakoutas K (2019) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Comput Concr Int J 24:469–488

    Google Scholar 

  • Baduge SK, Thilakarathna S, Perera JS, Arashpour M, Sharafi P, Teodosio B, Shringi A, Mendis P (2022) Artificial intelligence and smart vision for building and construction 4.0: machine and deep learning methods and applications. Autom Constr 141:104440

    Google Scholar 

  • Bakouregui AS, Mohamed HM, Yahia A, Benmokrane B (2021) Explainable extreme gradient boosting tree-based prediction of load-carrying capacity of FRP-RC columns. Eng Struct 245:112836

    Google Scholar 

  • Basilio SA, Goliatt L (2022) Gradient boosting hybridized with exponential natural evolution strategies for estimating the strength of geopolymer self-compacting concrete. Knowl Based Eng Sci 3:1–16

    Google Scholar 

  • Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogrammet Remote Sens 114:24–31

    Google Scholar 

  • Bethlehem D (2004) The European union. In: National implementation of United Nations sanctions, Brill Nijhoff, pp 123–165

  • Biswas R, Li E, Zhang N, Kumar S, Rai B, Zhou J (2022) Development of hybrid models using metaheuristic optimization techniques to predict the carbonation depth of fly ash concrete. Constr Build Mater 346:128483

    Google Scholar 

  • Castelli M, Vanneschi L, Silva S (2013) Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. Expert Syst Appl 40:6856–6862

    Google Scholar 

  • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Develop 7:1247–1250

    Google Scholar 

  • Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, Chen K, et al (2015) Xgboost: extreme gradient boosting, R package version 0.4-2 1, pp 1–4

  • Chen R, Ma Y, Chen N, Lee D, Wang W (2019) Cephalometric landmark detection by attentive feature pyramid fusion and regression-voting. In: International conference on medical image computing and computer-assisted intervention. Springer, London, pp 873–881

  • Cho S-H, Kim Y-I (2003) Effects of steel fibers on short beams loaded in shear. Struct J 100:765–774

    Google Scholar 

  • Chou J-S, Pham T-P-T, Nguyen T-K, Pham A-D, Ngo N-T (2020a) Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models. Soft Comput 24:3393–3411

  • Chou J-S, Pham T-P-T, Nguyen T-K, Pham A-D, Ngo N-T (2020b) Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models. Soft Comput 24:3393–3411

  • Clark AP (1951) Diagonal tension in reinforced concrete beams. J Proc 48:145–156

    Google Scholar 

  • Committee A (2008) Building code requirements for structural concrete (ACI 318-08) and commentary. American Concrete Institute

  • Concha N, Aratan JR, Derigay EM, Martin JM, Taneo RE (2023) A hybrid neuro-swarm model for shear strength of steel fiber reinforced concrete deep beams. J Build Eng 2023:107340

    Google Scholar 

  • Dai H, Cao Z (2017) A wavelet support vector machine-based neural network metamodel for structural reliability assessment. Comput Aided Civ Infrastruct Eng 32:344–357

    Google Scholar 

  • Darwin D, Dolan CW, Nilson AH (2016) Design of concrete structures, vol 2. McGraw-Hill Education, Frontiers of Computer

    Google Scholar 

  • De Myttenaere A, Golden B, Le Grand B, Rossi F (2015) Using the mean absolute percentage error for regression models. In: Proceedings, Presses universitaires de Louvain, p 113

  • Di Bucchianico A (2008) Coefficient of determination (R2). Encyclop Stat Qual Reliab 1:1

    Google Scholar 

  • Dietterich TG (2000) Ensemble methods in machine learning. In: Multiple classifier systems: first international workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 proceedings 1. Springer, London, pp 1–15

  • Dimiduk DM, Holm EA, Niezgoda SR (2018) Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integr Mater Manuf Innov 7:157–172

    Google Scholar 

  • Dong X, Yu Z, Cao W, Shi Y, Ma Q (2020) A survey on ensemble learning. Front Comput Sci 14:241–258

    Google Scholar 

  • Dubourg V, Sudret B, Bourinet J-M (2011) Reliability-based design optimization using kriging surrogates and subset simulation. Struct Multidiscip Optim 44:673–690

    Google Scholar 

  • Dubourg V, Sudret B, Deheeger F (2013) Metamodel-based importance sampling for structural reliability analysis. Probab Eng Mech 33:47–57

    Google Scholar 

  • Esteghamati MZ, Flint MM (2021) Developing data-driven surrogate models for holistic performance-based assessment of mid-rise RC frame buildings at early design. Eng Struct 245:112971

    Google Scholar 

  • Farrar CR, Worden K (2012) Structural health monitoring: a machine learning perspective. Wiley, London

    Google Scholar 

  • Fathipour-Azar H (2022) Stacking ensemble machine learning-based shear strength model for rock discontinuity. Geotech Geol Eng 40:3091–3106

  • Feng D-C, Wang W-J, Mangalathu S, Hu G, Wu T (2021) Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements. Eng Struct 235:111979

    Google Scholar 

  • Freund Y, Schapire RE et al (1996) Experiments with a new boosting algorithm. In: ICML, vol 96. Citeseer, pp 148–156

  • Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969

    MathSciNet  Google Scholar 

  • Fu B, Feng D-C (2021) A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams. J Build Eng 36:102118

    Google Scholar 

  • Fujino Y, Siringoringo DM, Ikeda Y, Nagayama T, Mizutani T (2019) Research and implementations of structural monitoring for bridges and buildings in japan. Engineering 5:1093–1119

    Google Scholar 

  • Gordan M, Sabbagh-Yazdi S-R, Ismail Z, Ghaedi K, Carroll P, McCrum D, Samali B (2022) State-of-the-art review on advancements of data mining in structural health monitoring. Measurement 193:110939

    Google Scholar 

  • Gundersen OE, Kjensmo S (2018) State of the art: reproducibility in artificial intelligence. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

  • Hackeling G (2017) Mastering machine learning with scikit-learn. Packt Publishing Ltd, London

    Google Scholar 

  • Hamel LH (2011) Knowledge discovery with support vector machines. Wiley, London

    Google Scholar 

  • Hancock J, Khoshgoftaar TM (2020) Performance of catboost and XGBoost in medicare fraud detection. In: 2020 19th IEEE international conference on machine learning and applications (ICMLA). IEEE, London, pp 572–579

  • Hao H, Bi K, Chen W, Pham TM, Li J (2023) Towards next generation design of sustainable, durable, multi-hazard resistant, resilient, and smart civil engineering structures. Eng Struct 277:115477

    Google Scholar 

  • Hatami M, Franz B, Paneru S, Flood I (2022) Using deep learning artificial intelligence to improve foresight method in the optimization of planning and scheduling of construction processes. In: Computing in civil engineering 2021, pp 1171–1178

  • He Z, Li W, Salehi H, Zhang H, Zhou H, Jiao P (2022) Integrated structural health monitoring in bridge engineering. Autom Constr 136:104168

    Google Scholar 

  • 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 28:1119–1130

    Google Scholar 

  • Ismail KS, Guadagnini M, Pilakoutas K (2018) Strut-and-tie modeling of reinforced concrete deep beams. J Struct Eng 2018:144

    Google Scholar 

  • Jalayer F, Ebrahimian H, Miano A, Manfredi G, Sezen H (2017) Analytical fragility assessment using unscaled ground motion records. Earthq Eng Struct Dyn 46:2639–2663

    Google Scholar 

  • Jiang Y, Tong G, Yin H, Xiong N (2019) A pedestrian detection method based on genetic algorithm for optimize XGBoost training parameters. IEEE Access 7:118310–118321

    Google Scholar 

  • Jin L, Wang T, Jiang X-A, Du X (2019) Size effect in shear failure of RC beams with stirrups: simulation and formulation. Eng Struct 199:109573

    Google Scholar 

  • Kersaudy P, Sudret B, Varsier N, Picon O, Wiart J (2015) A new surrogate modeling technique combining kriging and polynomial chaos expansions-application to uncertainty analysis in computational dosimetry. J Comput Phys 286:103–117

    MathSciNet  Google Scholar 

  • Khuntia M, Stojadinovic B, Goel SC (1999) Shear strength of normal and high-strength fiber reinforced concrete beams without stirrups. Struct J 96:282–289

    Google Scholar 

  • Kivrak M, Guldogan E, Colak C (2021) Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods. Comput Methods Programs Biomed 201:105951

    Google Scholar 

  • Kong F-K, Robins PJ, Cole DF (1970) Web reinforcement effects on deep beams. J Proc 67:1010–1018

    Google Scholar 

  • König G, Molnar C, Bischl B, Grosse-Wentrup M (2021) Relative feature importance. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, London, pp 9318–9325

  • Kwak Y-K, Eberhard MO, Kim W-S, Kim J (2002) Shear strength of steel fiber-reinforced concrete beams without stirrups. ACI Struct J 99:530–538

    Google Scholar 

  • Lagaros ND, Fragiadakis M (2007) Fragility assessment of steel frames using neural networks. Earthq Spect 23:735–752

    Google Scholar 

  • Lagaros ND, Tsompanakis Y, Psarropoulos PN, Georgopoulos EC (2009) Computationally efficient seismic fragility analysis of geostructures. Comput Struct 87:1195–1203

    Google Scholar 

  • Leonhardt F, Walter R (1966) Deep beams. In: Bulletin 178, Deutcher Ausschuss fur Stahlbeton, Berlin

  • Liu T, Wang Z, Long Z, Zeng J, Wang J, Zhang J (2022) Direct shear strength prediction for precast concrete joints using the machine learning method. J Bridge Eng 27:04022026

    Google Scholar 

  • Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017:30

    Google Scholar 

  • Ly H-B, Le T-T, Vu H-LT, Tran VQ, Le LM, Pham BT (2020) Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability 12:2709

    Google Scholar 

  • Ma B, Terayama K, Matsumoto S, Isaka Y, Sasakura Y, Iwata H, Araki M, Okuno Y (2021) Structure-based de novo molecular generator combined with artificial intelligence and docking simulations. J Chem Inf Model 61:3304–3313

    Google Scholar 

  • Mahmoudi S, Chouinard L (2016) Seismic fragility assessment of highway bridges using support vector machines. Bull Earthq Eng 14:1571–1587

    Google Scholar 

  • Maimon OZ, Rokach L (2014) Data mining with decision trees: theory and applications, vol 81. World Scientific, London

    Google Scholar 

  • 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

    Google Scholar 

  • Mangalathu S, Heo G, Jeon J-S (2018a) Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes. Eng Struct 162:166–176

  • Mangalathu S, Jeon J-S, DesRoches R (2018b) Critical uncertainty parameters influencing seismic performance of bridges using lasso regression. Earthq Eng Struct Dyn 47:784–801

  • Maragos P (1989) Morphological correlation and mean absolute error criteria. In: International conference on acoustics, speech, and signal processing. IEEE, London, pp 1568–1571

  • Marie HS, Abu El-hassan K, Almetwally EM, El-Mandouh MA (2022) Joint shear strength prediction of beam-column connections using machine learning via experimental results. Case Stud Constr Mater 17:e01463

    Google Scholar 

  • Mihaylov BI, Bentz EC, Collins MP (2010) Behavior of large deep beams subjected to monotonic and reversed cyclic shear. ACI Struct J 2010:107

    Google Scholar 

  • 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 5:29

    Google Scholar 

  • Naranjo-Pérez J, Infantes M, Jiménez-Alonso JF, Sáez A (2020) A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures. Eng Struct 225:111327

    Google Scholar 

  • Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7:21

    Google Scholar 

  • Oh J-K, Shin S-W (2001) Shear strength of reinforced high-strength concrete deep beams. Struct J 98:164–173

    Google Scholar 

  • Olalusi OB, Awoyera PO (2021) Shear capacity prediction of slender reinforced concrete structures with steel fibers using machine learning. Eng Struct 227:111470

    Google Scholar 

  • Pak H, Leach S, Yoon SH, Paal SG (2023) A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups. Comput Aided Civ Infrastruct Eng 2023:1

    Google Scholar 

  • Palsara C, Kumar V, Pal J, Naresh M (2023) Structural health monitoring of ASCE benchmark building using machine learning algorithms. Asian J Civ Eng 2023:1–14

    Google Scholar 

  • Pan Y, Zhang L (2021) Roles of artificial intelligence in construction engineering and management: a critical review and future trends. Autom Constr 122:103517

    Google Scholar 

  • Prayogo D, Cheng M-Y, Wu Y-W, Tran D-H (2020) Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Eng Comput 36:1135–1153

    Google Scholar 

  • Quintero-Febres CG, Parra-Montesinos G, Wight JK (2006) Strength of struts in deep concrete members designed using strut-and-tie method. ACI Mater J 103:577

    Google Scholar 

  • Rahman J, Ahmed KS, Khan NI, Islam K, Mangalathu S (2021a) Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach. Eng Struct 233:111743

  • Rahman J, Ahmed KS, Khan NI, Islam K, Mangalathu S (2021b) Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach. Eng Struct 233:111743

  • Ramakrishnan V, Ananthanarayana Y (1968) Ultimate strength of deep beams in shear. J Proc 65:87–98

    Google Scholar 

  • Rathakrishnan V, Beddu SBt, Ahmed AN (2022) Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms. Sci Rep 12:9539

    Google Scholar 

  • Renaud O, Victoria-Feser M-P (2010) A robust coefficient of determination for regression. J Stat Plan Inference 140:1852–1862

    MathSciNet  Google Scholar 

  • Sacks R, Bloch T, Katz M, Yosef R (2019) Automating design review with artificial intelligence and Bim: state of the art and research framework. In: ASCE international conference on computing in civil engineering 2019. American Society of Civil Engineers Reston, VA, pp 353–360

  • Salehi H, Burgueño R (2018) Emerging artificial intelligence methods in structural engineering. Eng Struct 171:170–189

    Google Scholar 

  • Salman B, Kadhum MM (2022) Predicting of load carrying capacity of reactive powder concrete and normal strength concrete column specimens using artificial neural network. Knowl Based Eng Sci 3:45–53

    Google Scholar 

  • Sandeep MS, Tiprak K, Kaewunruen S, Pheinsusom P, Pansuk W (2023) Shear strength prediction of reinforced concrete beams using machine learning. In: Structures, vol 47. Elsevier, London, pp 1196–1211

  • Sarmadi H, Yuen K-V (2022) Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling. Mech Syst Signal Process 173:109049

    Google Scholar 

  • Selvaraj S, Sivaraman S (2019) Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification. Neural Comput Appl 31:1365–1373

    Google Scholar 

  • Seni G, Elder JF (2010) Ensemble methods in data mining: improving accuracy through combining predictions. Synth Lect Data Min Knowl Disc 2:1–126

    Google Scholar 

  • Shaoxi G (1982) The shear strength capability of reinforced concrete deep beam under symmetric concentrated loads. J Zhengzhou Technol Inst 1:52–68

    Google Scholar 

  • Sharma A (1986) Shear strength of steel fiber reinforced concrete beams. J Proc 83:624–628

    Google Scholar 

  • Smith K, Vantsiotis A (1982) Shear strength of deep beams. J Proc 9:201–213

    Google Scholar 

  • Standard GC (2002) 50010-2010, code for design of concrete structures

  • Sun H, Burton HV, Huang H (2021a) Machine learning applications for building structural design and performance assessment: state-of-the-art review. J Build Eng 33:101816

  • Sun H, Burton HV, Huang H (2021b) Machine learning applications for building structural design and performance assessment: state-of-the-art review. J Build Eng 33:101816

  • Tan K-H, Kong F-K, Teng S, Guan L (1995) High-strength concrete deep beams with effective span and shear span variations. Struct J 92:395–405

    Google Scholar 

  • Tapeh ATG, Naser M (2023a) Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices. Arch Comput Methods Eng 30:115–159

  • Tapeh ATG, Naser M (2023b) Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices. Arch Comput Methods Eng 30:115–159

  • Tiwari A, Chugh A, Sharma A (2022) Ensemble framework for cardiovascular disease prediction. Comput Biol Med 2022:105624

    Google Scholar 

  • Tiwari A, Chugh A, Sharma A (2023) Chapter 7—Uses of artificial intelligence with human–omputer interaction in psychology. In: Innovations in artificial intelligence and human–computer interaction in the digital era, intelligent data-centric systems. Academic Press, London, pp 173–205

  • Truong-Hong L, Lindenbergh R (2022) Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point clouds. Autom Constr 135:104127

    Google Scholar 

  • Vamdewalle MI, Mortelmans F (1994) Shear capacity of steel fiber high-strength concrete beams. Spec Publ 149:227–242

    Google Scholar 

  • Wakjira TG, Al-Hamrani A, Ebead U, Alnahhal W (2022) Shear capacity prediction of FRP-RC beams using single and ensenble explainable machine learning models. Compos Struct 287:115381

  • Wakjira TG, Al-Hamrani A, Ebead U, Alnahhal W (2022b) Shear capacity prediction of FRP-RC beams using single and ensenble explainable machine learning models. Compos Struct 287:115381

  • Wakjira TG, Ebead U, Alam MS (2022c) Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites. Case Stud Constr Mater 16:e01008

  • Wang Z, Pedroni N, Zentner I, Zio E (2018) Seismic fragility analysis with artificial neural networks: application to nuclear power plant equipment. Eng Struct 162:213–225

    Google Scholar 

  • Wu Q, Burges CJ, Svore KM, Gao J (2010) Adapting boosting for information retrieval measures. Inf Retrieval 13:254–270

    Google Scholar 

  • Wu D, Guo P, Wang P (2020) Malware detection based on cascading XGBoost and cost sensitive. In: 2020 international conference on computer communication and network security (CCNS). IEEE, LOndon, pp 201–205

  • Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu C-W et al (2021) Artificial intelligence: a powerful paradigm for scientific research. Innovation 2:1

    Google Scholar 

  • Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S, Nehdi ML (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125

    Google Scholar 

  • Yegnanarayana B (2009) Artificial neural networks. PHI Learning Pvt. Ltd., London

    Google Scholar 

  • Zhang C, Lu Y (2021) Study on artificial intelligence: the state of the art and future prospects. J Ind Inf Integr 23:100224

    Google Scholar 

  • Zhang J, Sun Y, Li G, Wang Y, Sun J, Li J (2020a) Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups. Eng Comput 2020:1–15

  • Zhang J, Sun Y, Li G, Wang Y, Sun J, Li J (2020b) Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups. Eng Comput 2020:1–15

  • Zhang G, Ali ZH, Aldlemy MS, Mussa MH, Salih SQ, Hameed MM, Al-Khafaji ZS, Yaseen ZM (2020c) Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model. Eng Comput 2020:1–14

  • Zhang W, Liu X, Huang Y, Tong M-N (2022) Reliability-based analysis of the flexural strength of concrete beams reinforced with hybrid BFRP and steel rebars. Arch Civ Mech Eng 22:1–20

    Google Scholar 

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Acknowledgements

The authors acknowledge the invaluable contributions of the reviewers to the manuscript, as their feedback and comments played a crucial role in improving the content of the paper.

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  1. Department of Computer Science and Engineering and Information Technology, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, 173234, India

    Achyut Tiwari

  2. Department of Civil Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, 173234, India

    Ashok Kumar Gupta & Tanmay Gupta

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AT: conceptualisation, methodology, software, validation, investigation, writing the original draft. AKG: conceptualisation, methodology, formal analysis, supervision, review and editing. TG: comparison with mechanics-driven models of CIRIA, the US, Canada, China and European Union, review and editing.

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Tiwari, A., Gupta, A.K. & Gupta, T. A robust approach to shear strength prediction of reinforced concrete deep beams using ensemble learning with SHAP interpretability. Soft Comput 28, 6343–6365 (2024). https://doi.org/10.1007/s00500-023-09495-w

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