This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregate concrete. The proposed algorithm integrates machine learning with a new version of the firefly algorithm called chaotic based firefly algorithm (CFA) to evolve a rational and efficient predictive model. The CFA optimizer is augmented with chaotic maps and Lévy flight to improve the firefly performance in forecasting the chloride penetrability of strengthened recycled aggregate concrete (RAC). A comprehensive and credible database of distinctive chloride migration coefficient results is used to establish the developed algorithm. A dataset composite of nine effective parameters, including concrete components and fundamental characteristics of recycled aggregate (RA), is used as input to predict the migration coefficient of strengthened RAC as output. k-fold cross validation algorithm is utilized to validate the hybrid algorithm. Three numerical benchmark analyses are applied to prove the superiority and applicability of the CFA algorithm in predicting chloride penetrability. Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost tested functions and demonstrates powerful prediction. In addition, the proposed strategy can be an active tool to recognize the contradictions in the experimental results and can be especially beneficial for assessing the chloride resistance of RAC.

本研究提出了一种新的系统算法来优化钢筋再生骨料混凝土的耐久性。所提出的算法将机器学习与新版本的萤火虫算法相结合，称为基于混沌的萤火虫算法 (CFA)，以演化出一个合理且高效的预测模型。CFA 优化器增加了混沌映射和 Lévy 飞行，以提高萤火虫在预测强化再生骨料混凝土 (RAC) 的氯离子渗透性方面的性能。一个全面和可信的独特氯迁移系数结果数据库用于建立所开发的算法。九个有效参数的数据集组合，包括混凝土成分和再生骨料 (RA) 的基本特征，k折交叉验证算法用于验证混合算法。应用三个数值基准分析来证明 CFA 算法在预测氯离子渗透性方面的优越性和适用性。结果表明，所开发的 CFA 方法在几乎测试的功能上明显优于萤火虫算法，并展示了强大的预测能力。此外，所提出的策略可以成为识别实验结果中矛盾的积极工具，并且对于评估 RAC 的耐氯性特别有益。