Rapid Analysis of CFRP-Reinforced Concrete Structures Using Artificial Neural Networks
Nasim Shakouri Mahmoudabadi, Charles V. Camp
Abstract
This study examines the vital role of artificial neural networks (ANNs) in predicting and optimizing carbon fiber-reinforced polymer (CFRP) reinforced concrete structures. ANNs excel in modeling complex non-linear relationships, making them ideal for analyzing composite materials' intricate behavior. The review covers ANNs' fundamental principles, CFRP materials, reinforcement techniques, and the challenges of coupling multiple design parameters. It investigates ANNs' potential to model these complexities through multivariate input parameters and ensemble techniques, highlighting current ANN-based practices like response surface models. The study suggests overcoming limitations with advanced feature engineering, hybrid modeling, and embracing emerging technologies. It emphasizes the transformative potential of integrating ANNs and CFRP in designing more efficient, durable, and sustainable concrete structures.