A new intelligent sunflower optimization based explainable artificial intelligence approach for early-age concrete compressive strength classification and mixture design of RAC

Abstract

This study aims to develop a new artificial intelligence model that can produce explainable rules to predict the mix design and early-age concrete compressive strength classes of recycled aggregate concrete (RAC). Unlike other black-box machine learning methods and rule-based algorithms, the study relies on a metaheuristic mechanism for explainability. This metaheuristic mechanism is not used for a traditional parameter optimization, but to automatically extract interpretable and interpretable rules from the experimental data. In the study, 30 series of RACs are produced, and the samples' 1- and 3-day early-age concrete compressive strength values are determined. Concretes produced using these strength values are classified. The labels defined for each concrete class are Class A (C 8/10), Class B (C 12/15), Class C (C 16/20), and Class D (C 20/25). The proposed intelligent classification model that consists of rule set automatically produces interpretable and comprehensible rules from data to determine the early-age concrete compressive strength class and RAC mix amounts. In addition, the proposed method eliminates the black-box disadvantages of classical machine learning methods with its explainability and interpretability feature. The sunflower optimization algorithm is adapted as the metaheuristic mechanism and a special fitness function and representative solution form are developed for automatic extraction of high-quality comprehensible rules by simultaneously optimizing many different metrics. This paper is the first interpretable and comprehensible artificial intelligence model attempt used for early-age compressive strength classification and mixture design of recycled aggregate concrete by balancing and optimizing both the accuracy and explainability. Proposed explainable intelligent classification model is tested against both well-known state-of-the-art machine learning algorithms and standard rule-based methods on the produced real data. Promising results in terms of accuracy, precision, recall are obtained along with the explainability feature.