Title:
Machine Learning for Shear Strength of Reinforced-Concrete Beams
Author(s):
Rodrigo Castillo, Pinar Okumus, Negar Elhami Khorasani, and Varun Chandola
Publication:
Structural Journal
Volume:
119
Issue:
5
Appears on pages(s):
83-94
Keywords:
DOI:
10.14359/51734662
Date:
9/1/2022
Abstract:
This study uses machine learning (ML) to characterize the shear strength of reinforced concrete (RC) beams and one-way slabs. A database of 1436 RC shear tests is used to train three ML algorithms (ordinary linear regression, support vector regression, and Gaussian process regression). The database is divided into two training subsets per ACI 318-19 minimum shear reinforcement requirements. Each training sample consists of up to 10 predictive features (geometry, materials, reinforcement detail, load location) and the corresponding target feature (shear strength). The most accurate algorithm, the Gaussian process regression, has 17% and 11% smaller mean percent error in shear strength predictions than ACI 318-19 guidelines for the subsets with shear reinforcement areas less than and larger than the minimum ACI 318-19 requirement, respectively. This algorithm also provides predictive confidence with a standard deviations less than 5.2 kN (1.18 kip) for 99.7% of the predictions.
Related References:
ACI Committee 318, 2019, “Building Code Requirements for Structural Concrete (ACI 318-19) and Commentary (ACI 318R-19),” American Concrete Institute, Farmington Hills, MI, 624 pp.
Angelakos, D.; Bentz, E. C.; and Collins, M. P., 2001, “Effect of Concrete Strength and Minimum Stirrups on Shear Strength of Large Members,” ACI Structural Journal, V. 98, No. 3, May-June, pp. 290-300.
Armaghani, D. J.; Hatzigeorgiou, G. D.; Karamani, C.; Skentou, A.; Zoumpoulaki, I.; and Asteris, P. G., 2019, “Soft Computing-Based Techniques for Concrete Beams Shear Strength,” Procedia Structural Integrity, V. 17, pp. 924-933. doi: 10.1016/j.prostr.2019.08.123
Barrett, J. P., 1974, “The Coefficient of Determination—Some Limitations,” The American Statistician, V. 28, No. 1, pp. 19-20.
Bažant, Z. P.; Yu, Q.; Gerstle, W.; Hanson, J.; and Ju, J. W., 2007, “Justification of ACI 446 Proposal for Updating ACI Code Provisions for Shear Design of Reinforced Concrete Beams,” ACI Structural Journal, V. 104, No. 5, Sept.-Oct., pp. 601-610.
Bentz, E. C., 2000, “Sectional Analysis of Reinforced Concrete Members,” PhD thesis, University of Toronto, Toronto, ON, Canada, 316 pp.
Bresler, B., and Scordelis, A. C., 1963, “Shear Strength of Reinforced Concrete Beams,” ACI Journal Proceedings, V. 60, No. 1, Jan., pp. 51-74.
Collins, M. P.; Bentz, E. C.; and Sherwood, E. G., 2008, “Where is Shear Reinforcement Required? Review of Research Results and Design Procedures,” ACI Structural Journal, V. 105, No. 5, Sept.-Oct., pp. 590-600.
Cortes, C., and Vapnik, V., 1995, “Support-Vector Networks,” Machine Learning, V. 20, No. 3, Sept., pp. 273-297. doi: 10.1007/BF00994018
Davoudi, R.; Miller, G. R.; and Kutz, J. N., 2018, “Structural Load Estimation Using Machine Vision and Surface Crack Patterns for Shear-Critical RC Beams and Slabs,” Journal of Computing in Civil Engineering, ASCE, V. 32, No. 4, July, p. 04018024. doi: 10.1061/(ASCE)CP.1943-5487.0000766
Desai, V. S., and Bharati, R., 1998, “A Comparison of Linear Regression and Neural Network Methods for Predicting Excess Returns on Large Stocks,” Annals of Operations Research, V. 78, No. 78, Jan., pp. 127-163. doi: 10.1023/A:1018993831870
Drucker, H.; Burges, C. J. C.; Kaufman, L.; Smola, A.; and Vapnik, V., 1997, “Support Vector Regression Machines,” Advances in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan, and T. Petsche, eds., The MIT Press, Cambridge, MA, pp. 155-161.
Hocking, R. R., 1983, “Developments in Linear Regression Methodology: 1959-l982,” Technometrics, V. 25, No. 3, Aug., pp. 219-230.
Kim, K. S.; Lee, D. H.; Hwang, J.-H.; and Kuchma, D. A., 2012, “Shear Behavior Model for Steel Fiber-Reinforced Concrete Members without Transverse Reinforcements,” Composites Part B: Engineering, V. 43, No. 5, July, pp. 2324-2334.
Kong, P. Y. L., and Rangan, B. V., 1998, “Shear Strength of High-Performance Concrete Beams,” ACI Structural Journal, V. 95, No. 6, Nov.-Dec., pp. 677-688.
Koo, S.; Shin, D.; and Kim, C., 2021, “Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups,” Materials (Basel), V. 14, No. 13, July, Article No. 3471. doi: 10.3390/ma14133471
Kuss, O., 2002, “Global Goodness‐of‐Fit Tests in Logistic Regression with Sparse Data,” Statistics in Medicine, V. 21, No. 24, Dec., pp. 3789-3801. doi: 10.1002/sim.1421
Mangalathu, S.; Jang, H.; Hwang, S.-H.; and Jeon, J.-S., 2020, “Data-Driven Machine-Learning-Based Seismic Failure Mode Identification of Reinforced Concrete Shear Walls,” Engineering Structures, V. 208, Apr., Article No. 110331. doi: 10.1016/j.engstruct.2020.110331
Moehle, J. P., 2019, “Key Changes in the 2019 Edition of the ACI Building Code (ACI 318-19),” Concrete International, V. 41, No. 8, Aug., pp. 21-27.
Murphy, K. P., 2012, Machine Learning: A Probabilistic Perspective, The MIT Press, Cambridge, MA, Aug., 1104 pp.
Podgorniak-Stanik, B. A., 1998, “The Influence of Concrete Strength, Distribution of Longitudinal Reinforcement, Amount of Transverse Reinforcement and Member Size on Shear Strength of Reinforced Concrete Members,” master’s thesis, University of Toronto, Toronto, ON, Canada, 369 pp.
Pourahmadi, M., 2007, “Cholesky Decompositions and Estimation of a Covariance Matrix: Orthogonality of Variance–Correlation Parameters,” Biometrika, V. 94, No. 4, Dec., pp. 1006-1013. doi: 10.1093/biomet/asm073
Rodríguez, J. D.; Pérez, A.; and Lozano, J. A., 2010, “Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, V. 32, No. 3, Mar., pp. 569-575. doi: 10.1109/TPAMI.2009.187
Sanad, A., and Saka, M. P., 2001, “Prediction of Ultimate Shear Strength of Reinforced-Concrete Deep Beams Using Neural Networks,” Journal of Structural Engineering, ASCE, V. 127, No. 7, July, pp. 818-828. doi: 10.1061/(ASCE)0733-9445(2001)127:7(818)
Solhmirzaei, R.; Salehi, H.; Kodur, V.; and Naser, M. Z., 2020, “Machine Learning Framework for Predicting Failure Mode and Shear Capacity of Ultra High Performance Concrete Beams,” Engineering Structures, V. 224, Dec., Article No. 111221. doi: 10.1016/j.engstruct.2020.111221
Tompos, E. J., and Frosch, R. J., 2002, “Influence of Beam Size, Longitudinal Reinforcement, and Stirrup Effectiveness on Concrete Shear Strength,” ACI Structural Journal, V. 99, No. 5, Sept.-Oct., pp. 559-567.
Tureyen, A. K., and Frosch, R. J., 2003, “Concrete Shear Strength: Another Perspective,” ACI Structural Journal, V. 100, No. 5, Sept.-Oct., pp. 609-615.
Vapnik, V.; Golowich, S. E.; and Smola, A., 1997, “Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing,” Advances in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan, and T. Petsche, eds., The MIT Press, Cambridge, MA, pp. 281-287.
Velleman, P. F., and Hoaglin, D. C., 1981, Applications, Basics, and Computing of Exploratory Data Analysis, Duxbury Press, Boston, MA, 380 pp.
Williams, C. K. I., and Rasmussen, C. E., 1996, “Gaussian Processes for Regression,” Advances in Neural Information Processing Systems 8, D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, eds., The MIT Press, Cambridge, MA, pp. 514-520.
Zaborac, J.; Athanasiou, A.; Salamone, S.; Bayrak, O.; and Hrynyk, T., 2019, “Evaluation of Structural Cracking in Concrete: Final Report,” Report No. FHWA/TX-19/0-6919-1, Center for Transportation Research, The University of Texas at Austin, Austin, TX, 171 pp.
Zhang, D., 2017, “A Coefficient of Determination for Generalized Linear Models,” The American Statistician, V. 71, No. 4, Dec., pp. 310-316. doi: 10.1080/00031305.2016.1256839
Zhang, J.; Sun, Y.; Li, G.; Wang, Y.; Sun, J.; and Li, J., 2022, “Machine-Learning-Assisted Shear Strength Prediction of Reinforced Concrete Beams with and without Stirrups,” Engineering with Computers, V. 38, No. 2, Apr., pp. 1293-1307.