Modeling carbonation depth of recycled aggregate concrete using novel automatic regression technique

Abstract

Waste from concrete demolition is a sustainability concern that can be mitigated when used as recycled aggregate in concrete instead of virgin natural aggregates. However, the durability of recycled aggregate concrete (RAC), including concrete carbonation, needs to be investigated before the widespread applications of RAs in construction. Developing artificial intelligence-based predictive models for estimating the carbonation depth of RAC using the available data can reduce the need for experimental studies to generate reliable models for the service life assessment of concrete structures. In this study, artificial bee colony expression programming (ABCEP), as a novel branch of automatic regression technique, was used to predict the carbonation depth of RAC from a large dataset consisting of 655 data samples. Several ABCEP architectures were developed, different analyses were conducted, and a comparison study between the best ABCEP model and previous models published in the literature was conducted. The findings show that the best structure of the ABCEP model could estimate the carbonation depth of RAC with a reasonable root mean square error of 3.33 mm. The exposure time was the most influential parameter affecting the carbonation depth of RAC. Furthermore, the ABCEP model could outperform the previous models, despite the larger unknown dataset used to test its performance.