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Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams

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Abstract

This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weighting” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simultaneously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root-mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process.

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References

  1. 1.

    Tan KH, Weng LW, Teng S (1997) A strut-and-tie model for deep beams subjected to combined top-and-bottom loading. Struct Eng J 75(13):215–225

  2. 2.

    Park J-W, Kuchma D (2007) Strut-and-Tie model analysis for strength prediction of deep beams. ACI Struct J 104(6):657–666

  3. 3.

    Tang C, Tan K (2004) Interactive mechanical model for shear strength of deep beams. J Struct Eng 130(10):1534–1544

  4. 4.

    Pal M, Deswal S (2011) Support vector regression based shear strength modelling of deep beams. Comput Struct 89(13–14):1430–1439

  5. 5.

    ACI-318 ACIC (2011) 318-11: Building Code Requirements for Structural Concrete and Commentary. American Concrete Institute

  6. 6.

    CIRIA-Guide2 CIRaIA (1977) CIRIA Guide 2: The Design of Deep Beams in Reinforced Concrete. CIRIA, Ove Arup and Partners

  7. 7.

    CSA CSA (1994) Design of concrete structures: structures (design)—a national standard of Canada. CAN-A23.3-94. Toronto

  8. 8.

    Cheng M-Y, Prayogo D, Wu Y-W (2014) Novel genetic algorithm-based evolutionary support vector machine for optimizing high-performance concrete mixture. J Comput Civil Eng 28(4):06014003

  9. 9.

    Cheng M-Y, Firdausi PM, Prayogo D (2014) High-performance concrete compressive strength prediction using genetic weighted pyramid operation tree (GWPOT). Eng Appl Artif Intell 29:104–113

  10. 10.

    Tien Bui D, Nhu V-H, Hoang N-D (2018) Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and multi-layer perceptron neural network. Adv Eng Inform 38:593–604

  11. 11.

    Hoang N-D, Bui DT (2018) Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bull Eng Geol Env 77(1):191–204

  12. 12.

    Shaik S, Krishna KSR, Abbas M, Ahmed M, Mavaluru D (2018) Applying several soft computing techniques for prediction of bearing capacity of driven piles. Eng Comput. https://doi.org/10.1007/s00366-018-0674-7

  13. 13.

    Moosazadeh S, Namazi E, Aghababaei H, Marto A, Mohamad H, Hajihassani M (2019) Prediction of building damage induced by tunnelling through an optimized artificial neural network. Eng Comput 35:579–591

  14. 14.

    Sharma LK, Singh TN (2018) Regression-based models for the prediction of unconfined compressive strength of artificially structured soil. Eng Comput 34(1):175–186

  15. 15.

    Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

  16. 16.

    Suykens JAK, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Least squares support vector machines. World Scientific Publishing Company, Singapore

  17. 17.

    Tien Bui D, Tuan TA, Hoang N-D, Thanh NQ, Nguyen DB, Van Liem N, Pradhan B (2017) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14(2):447–458

  18. 18.

    Chou J-S, Pham A-D (2013) Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Constr Build Mater 49:554–563

  19. 19.

    Hancock T, Put R, Coomans D, Vander Heyden Y, Everingham Y (2005) A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies. Chemom Intell Lab Syst 76(2):185–196

  20. 20.

    Acar E, Rais-Rohani M (2009) Ensemble of metamodels with optimized weight factors. Struct Multidiscip Optim 37(3):279–294

  21. 21.

    Wang Y-R, Yu C-Y, Chan H-H (2012) Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. Int J Project Manage 30(4):470–478

  22. 22.

    Chou J-S, Lin C (2013) Predicting disputes in public-private partnership projects: classification and ensemble models. J Comput Civil Eng 27(1):51–60

  23. 23.

    Chou J-S, Yang K-H, Lin J-Y (2016) Peak shear strength of discrete fiber-reinforced soils computed by machine learning and metaensemble methods. J Comput Civil Eng 30(6):04016036

  24. 24.

    Prayogo D, Cheng MY, Widjaja J, Ongkowijoyo H, Prayogo H (2017) Prediction of concrete compressive strength from early age test result using an advanced metaheuristic-based machine learning technique. In: ISARC 2017—proceedings of the 34th international symposium on automation and robotics in construction, 2017. pp 856–863

  25. 25.

    Cheng M-Y, Prayogo D, Wu Y-W (2018) Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3426-0

  26. 26.

    Aljarah I, Al-Zoubi AM, Faris H, Hassonah MA, Mirjalili S, Saadeh H (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive Comput 10(3):478–495

  27. 27.

    Faris H, Hassonah MA, Al-Zoubi AM, Mirjalili S, Aljarah I (2018) A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Comput Appl 30(8):2355–2369

  28. 28.

    Hoang N-D, Pham A-D (2016) Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: a multinational data analysis. Expert Syst Appl 46:60–68

  29. 29.

    Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

  30. 30.

    Tejani GG, Savsani VJ, Patel VK (2016) Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization. J Comput Des Eng 3(3):226–249

  31. 31.

    Tejani GG, Savsani VJ, Bureerat S, Patel VK (2018) Topology and size optimization of trusses with static and dynamic bounds by modified symbiotic organisms search. J Comput Civil Eng 32(2):04017085

  32. 32.

    Tejani GG, Savsani VJ, Patel VK, Mirjalili S (2018) Truss optimization with natural frequency bounds using improved symbiotic organisms search. Knowl Based Syst 143:162–178

  33. 33.

    Kumar S, Tejani GG, Mirjalili S (2018) Modified symbiotic organisms search for structural optimization. Eng Comput. https://doi.org/10.1007/s00366-018-0662-y

  34. 34.

    Tejani GG, Pholdee N, Bureerat S, Prayogo D (2018) Multiobjective adaptive symbiotic organisms search for truss optimization problems. Knowl Based Syst 161:398–414

  35. 35.

    Tejani GG, Pholdee N, Bureerat S, Prayogo D, Gandomi AH (2019) Structural optimization using multi-objective modified adaptive symbiotic organisms search. Expert Syst Appl 125:425–441

  36. 36.

    Prayogo D, Susanto YTT (2018) Optimizing the prediction accuracy of friction capacity of driven piles in cohesive soil using a novel self-tuning least squares support vector machine. Adv Civil Eng 2018:9

  37. 37.

    Prayogo D, Cheng M-Y, Wong FT, Tjandra D, Tran D-H (2018) Optimization model for construction project resource leveling using a novel modified symbiotic organisms search. Asian J Civil Eng 19(5):625–638

  38. 38.

    Cheng M-Y, Prayogo D Modeling the permanent deformation behavior of asphalt mixtures using a novel hybrid computational intelligence. In: ISARC 2016—33rd international symposium on automation and robotics in construction, Auburn, USA, 2016. International association for automation and robotics in construction, pp 1009–1015

  39. 39.

    Cheng M-Y, Prayogo D, Wu Y-W (2018) A self-tuning least squares support vector machine for estimating the pavement rutting behavior of asphalt mixtures. Soft Comput. https://doi.org/10.1007/s00500-018-3400-x

  40. 40.

    Ezugwu AE, Prayogo D (2019) Symbiotic organisms search algorithm: theory, recent advances and applications. Expert Syst Appl 119:184–209

  41. 41.

    Cheng M-Y, Wibowo DK, Prayogo D, Roy AFV (2015) Predicting productivity loss caused by change orders using the evolutionary fuzzy support vector machine inference model. J Civil Eng Manag 21(7):881–892

  42. 42.

    Bishop CM (2006) Pattern recognition and machine learning (Information Science and Statistics). Springer, New York

  43. 43.

    Chou J-S, Chiu C-K, Farfoura M, Al-Taharwa I (2011) Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. J Comput Civil Eng 25(3):242–253

  44. 44.

    Hoang N-D, Tien Bui D, Liao K-W (2016) Groutability estimation of grouting processes with cement grouts using differential flower pollination optimized support vector machine. Appl Soft Comput 45:173–186

  45. 45.

    Clark AP (1951) Diagonal tension in reinforced concrete beams. ACI J 48(10):145–156

  46. 46.

    Kong FK, Robins PJ, Cole DF (1970) Web reinforcement effects on deep beams. ACI J Proc 67(12):1010–1018

  47. 47.

    Smith KN, Vantsiotis AS (1982) Shear strength of deep beams. ACI J Proc 79(3):201–213

  48. 48.

    Anderson NS, Ramirez JA (1989) Detailling of stirrup reinforcement. ACI Struct J 86(5):507–515

  49. 49.

    Tan K-H, Kong F-K, Teng S, Guan L (1995) High-strength concrete deep beams with effective span and shear span variations. ACI Struct J 92(4):395–405

  50. 50.

    Oh J-K, Shin S-W (2001) Shear strength of reinforced high-strength concrete deep beams. ACI Struct J 98(2):164–173

  51. 51.

    Aguilar G, Matamoros AB, Parra-Montesinos GJ, Ramirez JA, Wight JK (2002) Experimental evaluation of design procedures for shear strength of deep reinforced concrete beams. ACI Struct J 99(4):539–548

  52. 52.

    Quintero-Febres CG, Parra-Montesinos G, Wight JK (2006) Strength of struts in deep concrete members designed using Strut-and-Tie method. ACI Struct J 103(4):577–586

  53. 53.

    Gandomi AH, Alavi AH, Shadmehri DM, Sahab MG (2013) An empirical model for shear capacity of RC deep beams using genetic-simulated annealing. Arch Civil Mech Eng 13(3):354–369

  54. 54.

    Chang C-C, Lin C-J, Technology (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst 2(3):27

  55. 55.

    De Brabanter K, Karsmakers P, Ojeda F, Alzate C, De Brabanter J, Pelckmans K, De Moor B, Vandewalle J, Suykens JA (2010) LS-SVMlab toolbox user’s guide: version 1.7. Katholieke Universiteit Leuven

  56. 56.

    Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei. http://www.csie.ntu.edu.tw/cjlin/libsvm/

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Acknowledgments

The authors gratefully acknowledge that the present research is supported by The Ministry of Research, Technology, and Higher Education of the Republic of Indonesia (No: 123.58/D2.3/KP/2018) under the “World Class Professor” (WCP) Research Grant Scheme.

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Correspondence to Doddy Prayogo.

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Appendix 1

Appendix 1

See Tables 7 and 8.

Table 7 Training data set
Table 8 Testing data set

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Prayogo, D., Cheng, M., Wu, Y. et al. Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Engineering with Computers (2019) doi:10.1007/s00366-019-00753-w

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Keywords

  • Shear strength
  • RC deep beams
  • Ensemble model
  • Symbiotic organisms search
  • Support vector machine