APPLICATION OF MACHINE LEARNING IN CONCRETE TECHNOLOGY: A STATE-OF-THE-ART REVIEW

Machine learning (ML) has revolutionized various industries, including civil engineering, byproviding tools to predict and analyze scenarios previously challenging for human experts. This paperextensively reviews the state-of-the-art ML applications in concrete technology, covering design,compression strength analysis, and fault detection. ML techniques offer promising solutions for optimizingconcrete design, predicting compression strength, and detecting faults early on. Various ML algorithms,including Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent NeuralNetworks (RNN), Long Short-Term Memory (LSTM), and Autoencoders, are explored for their efficacyin concrete-related tasks. Furthermore, the paper discusses the integration of ML with evolutionaryalgorithms and metaheuristic techniques to improve prediction accuracy. Despite notable progress, thereremains ample room for enhancing ML techniques in comprehending concrete properties and behaviors.The review highlights the importance of ongoing research efforts in advancing ML applications in concretetechnology to optimize construction processes and infrastructure durability, ultimately contributing to saferand more resilient built environments.

کلیدواژه ها:

machine learning ، deep learning ، concrete ، civil engineering ، construction ، compression strength ، concrete design ، fault detection.