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• W4366144494 abstract "Artificial intelligence (AI) in the form of machine learning enables the working framework to get more accurate at interpreting outcomes without being explicitly programmed. Machine learning is described as a collection of techniques and algorithms that are capable of extracting information from data and continually enhancing their capabilities through the process of learning from experience. In Machine learning, Artificial Neural Networks (ANN), Multiple linear regression, support vector machine, and linear regression are widely available. An outline of the benefits of machine learning (ML) in the domain of Civil Engineering is described in this article. The review focused more on the determination of the strength characteristics of concrete through machine learning. Rapid growth in the sophistication of these approaches has had a profound impact on Civil Engineering, particularly in the building and infrastructure industries. In the field of Civil Engineering, Machine Learning plays a significant role in forecasting strength, particularly compressive strength and the physical features of concrete, such as assessment of crack propagation, surface roughness, etc., as opposed to the references. The review shows that machine learning-based strength estimation yields reliable findings in comparison to experimental values. The review explores various machine learning models which are used to estimate concrete properties. The XGBoost produces the best accurate result (85%) with the lowest mean absolute error. The most significant shear strength and compressive strength features, according to an analysis of the relevance of the input parameters, are the ratio of shear span to effective depth, longitudinal reinforcement ratio, concrete strength, and volume percent of fiber. The analysis demonstrated that machine learning may be utilized successfully to determine the compressive and tensile properties of concrete." @default.
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• W4366144494 date "2023-04-01" @default.
• W4366144494 modified "2023-10-06" @default.
• W4366144494 title "Machine learning for strength evaluation of concrete structures – Critical review" @default.
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• W4366144494 doi "https://doi.org/10.1016/j.matpr.2023.04.090" @default.
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