{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T01:10:09Z","timestamp":1773018609442,"version":"3.50.1"},"reference-count":60,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":143,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg\u2013Marquardt (ANN\u2010LM), quasi\u2010Newton method (ANN\u2010QN), conjugate gradient (ANN\u2010CG), and gradient descent (ANN\u2010GD). A database containing 106 results of RC deep beam shear strength tests is collected and used to investigate the performance of the four proposed algorithms. The ANN training phase uses 70% of data, randomly taken from the collected dataset, whereas the remaining 30% of data are used for the algorithms\u2019 evaluation process. The ANN structure consists of an input layer with 9 neurons corresponding to 9 input parameters, a hidden layer of 10 neurons, and an output layer with 1 neuron representing the shear strength of RC deep beams. The performance evaluation of the models is performed using statistical criteria, including the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results show that the ANN\u2010CG model has the best prediction performance with <jats:italic>R<\/jats:italic>\u2009=\u20090.992, RMSE\u2009=\u200914.02, MAE\u2009=\u200914.24, and MAPE\u2009=\u20096.84. The results of this study show that the ANN\u2010CG model can accurately predict the shear strength of RC deep beams, representing a promising and useful alternative design solution for structural engineers.<\/jats:p>","DOI":"10.1155\/2021\/5548988","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T19:50:09Z","timestamp":1621885809000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4266-1707","authenticated-orcid":false,"given":"Thuy-Anh","family":"Nguyen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8038-2381","authenticated-orcid":false,"given":"Hai-Bang","family":"Ly","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4482-715X","authenticated-orcid":false,"given":"Hai-Van Thi","family":"Mai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4157-7717","authenticated-orcid":false,"given":"Van Quan","family":"Tran","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"1","volume-title":"Concrete, Plain and Reinforced","author":"Bureau of Indian Standards","year":"2000"},{"key":"e_1_2_10_2_2","unstructured":"ACI 318-14 ACI 318-14 - Building Code Requirements for Structural Concrete. 2014."},{"key":"e_1_2_10_3_2","volume-title":"Eurocode 2: Design of Concrete Structures: Part 1-1: General Rules and Rules for Buildings","author":"British Standards Institution","year":"2004"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acme.2013.02.007"},{"key":"e_1_2_10_5_2","unstructured":"G. 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