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

• Published in: Composites. Part C, Open access Open access, 2024-07, Vol.14, p.100466, Article 100466
• Main Authors: Ali Talpur, Shabbir, Thansirichaisree, Phromphat, Poovarodom, Nakhorn, Mohamad, Hisham, Zhou, Mingliang, Ejaz, Ali, Hussain, Qudeer, Saingam, Panumas
• Format: Article
• Language: English
• Subjects: Compressive strength; Decision tree; Gradient boosting regressor, machine learning; Natural FRP; Neural network; Random forest
• ISSN: 2666-6820; 2666-6820
• DOI: 10.1016/j.jcomc.2024.100466

Recent 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.