Machine learning approach to predict the strength of concrete confined with sustainable natural FRP composites

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dc.contributor.authorAli Talpur, Shabbir
dc.contributor.authorThansirichaisree, Phromphat
dc.contributor.authorPoovarodom, Nakhorn
dc.contributor.authorMohamad, Hisham
dc.contributor.authorZhou, Mingliang
dc.contributor.authorEjaz, Ali
dc.contributor.authorHussain, Qudeer
dc.contributor.authorSaingam, Panumas
dc.date.accessioned2026-08-06T10:46:36Z
dc.date.available2026-08-06T10:46:36Z
dc.date.issued2024-07-01
dc.description.abstractRecent earthquakes have highlighted the need to strengthen existing structures with substandard designs. NFRPs provide a sustainable, cost-effective alternative for strengthening, but accurately predicting their performance remains a challenge. This study investigates the use of machine learning algorithms for predicting the compressive strength concrete specimens confined with various NFRPs. Four algorithms were employed: decision tree, random forest, neural network, and gradient boosting regressor. A diverse dataset encompassing various geometries, material properties, and confinement configurations was used to train and evaluate the models. Gradient boosting regressor (GBR) achieved the highest performance, with an average R-squared value of 0.94 and low mean absolute error (MAE) and root mean squared error (RMSE) during training and k-fold cross-validation. Neural network and random forest also demonstrated satisfactory performance, with average R-squared values of 0.88 and 0.86, respectively, during cross-validation. These results suggest that machine learning holds promise for predicting the compressive strength of concrete confined with NFRPs. GBR offers the most accurate predictions, making it a valuable tool for engineers seeking to optimize the design and performance of strengthened structures using sustainable materials.
dc.identifier.citationComposites Part C Open Access, 14, 2024
dc.identifier.doi10.1016/j.jcomc.2024.100466
dc.identifier.issn26666820
dc.identifier.other2-s2.0-85193584254
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15769
dc.sourceComposites Part C Open Access
dc.subjectCompressive strength
dc.subjectDecision tree
dc.subjectGradient boosting regressor, machine learning
dc.subjectNatural FRP
dc.subjectNeural network
dc.subjectRandom forest
dc.titleMachine learning approach to predict the strength of concrete confined with sustainable natural FRP composites
dc.typeArticle

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