Structural Performance of EB-FRP-Strengthened RC T-Beams Subjected to Combined Torsion and Shear Using ANN

Abstract

This research study applied Artificial Neural Networks (ANNs) to predict and evaluate the structural responses of externally bonded FRP (EB-FRP)-strengthened RC T-beams under combined torsion and shear. Previous studies proved that, compared to reinforced concrete (RC) rectangular beams, RC T-beams performance in shear is significantly higher in structural analysis and design. The structural response of RC beams experiences a critical change while torsion moments are applied in load conditions. Fiber Reinforced Polymer (FRP) is used to retrofit the structural elements due to changing structural design codes and loadings, especially in earthquake-prone countries. We applied Finite Element Method (FEM) software, ABAQUS, to provide a precise numerical database of a set of experimentally tested FRP-retrofitted RC T-beams in previous research works. ANN predicted structural analysis results and Mean Square Error (MSE) and Multiple Determination Coefficients proved the accuracy of this study. The MSE values that were less than 0.0009 and values greater than 0.9960 showed that the ANN precisely fits the data. The consistency between analyzed experimental and numerical results demonstrated the accurate implication of ANN, MSE, and in predicting the structural responses of EB-FRP- strengthened RC T-beams.

Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network

“…By leveraging the capacity of ANNs to discern complex patterns, the objective is to enhance the efficiency, durability, and overall structural performance of rehabilitated RC beams [20]. This intersection of neural networks and structural rehabilitation represents a promising avenue for advancing stateof-the-art civil engineering practice, and this innovative approach holds promise for optimizing rehabilitation strategies and strengthening the resilience of reinforced concrete beams under varying transient conditions [21].…”

“…However, although ANNs have found extensive application in practical software and electrical technology, their utilization in structural engineering is still restricted to research-related issues only. Nevertheless, there are also user-friendly software options, such as Propagator Software and Predict Software, which require only a basic understanding of ANNs [21]. These tools have been effectively used by researchers in the field of structural engineering applications.…”

Torsional Capacity Prediction of Reinforced Concrete Beams Using Machine Learning Techniques Based on Ensembles of Trees

“…In parallel, during the last decades, a huge amount of data has been accumulated in the field of civil engineering due to the advance in experimental programs, monitoring, data acquisition, and processing. For these reasons, ML-based models have been successfully used in complex civil engineering problems, including geotechnical, materials and structural [27][28][29][30][31][32][33][34][35][36][37][38][39], and some of them already focus on the torsional capacity of RC beams, including externally strengthened and combined RC beams [40][41][42][43][44][45][46][47][48]. The referred studies on the torsional capacity of RC beams are still very limited and they generally apply different ML techniques.…”

“…The referred studies on the torsional capacity of RC beams are still very limited and they generally apply different ML techniques. This is due to the fact that several ML techniques have been proposed in the literature, which are able to be used to solve problems in the field of civil engineering, including for structural concrete [46]. Despite the successes achieved with the application of ML approaches in several previous studies, some key challenges still exist which prevent these models from being widely used to solve engineering problems.…”

“…Perhaps the main key challenge is related with the selection process of the most appropriate and effective ML models to solve a particular civil engineering problem. For instance, some studies indicated that the best performances were reached with extreme gradient boosting (XGBoost) for problems involving the shear strength of RC beams [49,50], whereas others have mentioned other ML models to be more performant for the same types of problems [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55], namely Artificial Neural Networks (ANN). This includes the analysis of RC beams under torsion, where the shear effect is predominant [40,41,[43][44][45][46]48].…”