Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review
Source abstract
Abstract
Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Recycled Aggregate Concrete Performance · Innovative concrete reinforcement materials · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Department of Mining Engineering, Assiut University, Assiut 71511, Egypt.
References (100+)
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