A Machine Learning Approach to Predict Relative Residual Strengths of Recycled Aggregate Concrete after Exposure to High Temperatures

Document Type

Article

Publication Date

3-1-2024

Abstract

In recent years, there has been a heightened focus among researchers and policymakers on assessing the environmental impact and sustainability of human activities. In this context, the reutilization of construction materials, particularly recycled aggregate concrete, has emerged as an environmentally friendly choice in construction projects, gaining significant traction. This study addresses the critical need to investigate the mechanical properties of recycled aggregate concrete under diverse extreme scenarios. Conducting an extensive literature review, key findings were synthesized on the relative residual strength of recycled aggregate concrete following exposure to high temperatures. Leveraging these insights, innovative hybrid machine learning models were developed, offering practical equations and model trees for predicting the relative residual compressive strength, flexural strength, elasticity modulus, and splitting tensile strength of recycled aggregate concrete post high temperature exposure. Uncertainty analysis was performed on each model to assess the reliability, while sensitivity analysis was performed to find out the significance of each input variable for each predictive model. This paper presents interpretable models achieving high levels of performance, with (Formula presented.) values of 0.91, 0.94, 0.9, and 0.96 for predicting the relative residual compressive strength, flexural strength, modulus of elasticity, and splitting tensile strength of RCA concrete exposed to high temperatures, respectively. The unique contribution of the paper lies in the provision of easily applicable equations and model trees, enhancing accessibility for practitioners seeking to estimate mechanical properties of recycled aggregate concrete. Notably, our hybrid machine learning models stand out for their user-friendly nature compared with conventional ML algorithms, without compromising on accuracy. This paper not only advances our understanding of sustainable construction practices but also equips industry professionals with efficient tools for practical implementation.

Identifier

85187493683 (Scopus)

Publication Title

Sustainability (Switzerland)

External Full Text Location

https://doi.org/10.3390/su16051891

e-ISSN

20711050

Issue

5

Volume

16

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