This paper develops a novel artificial intelligence (AI)‐based approach, called the metaheuristics‐optimized ensemble system (MOES), to assist civil engineers significantly in achieving accurate estimations of the mechanical strength of reinforced concrete (RC) materials. MOES integrates the advantages of hybrid and ensemble models by combining a metaheuristic optimization algorithm and efficient AI models. The metaheuristic algorithm finds the optimal hyperparameters of individual AI techniques and simultaneously adjusts their weights to yield the best optimized‐weight‐ensemble model. Particularly, the developed MOES was established by integrating the forensic‐based investigation optimization algorithm, the radial basis function neural network, and the least squares support vector regression. Four case studies of predicting structural mechanics of RC beams were performed to evaluate the performance of MOES and compare it to those of other single AI models, conventional ensemble models, hybrid models, and empirical methods. The analytical results of cross‐validation reveal that MOES was the most reliable approach, achieving the best values of all performance evaluation indexes. The automated predictive analytics revealed the robustness, efficiency, and stability of MOES. Thus, the proposed approach is a highly promising tool for predicting the structural mechanics of RC beams. The success of MOES in estimating the mechanical strength of RC beams has redefined the way of optimizing an ensemble AI model, which is the primary contribution of this research to the relevant body of knowledge.

中文翻译：

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基于元启发式优化的集成系统，用于预测钢筋混凝土材料的机械强度

本文开发了一种新颖的基于人工智能（AI）的方法，称为元启发式优化集成系统（MOES），以帮助土木工程师极大地准确估计钢筋混凝土（RC）材料的机械强度。MOES通过结合元启发式优化算法和高效的AI模型，整合了混合模型和集成模型的优点。元启发式算法找到单个AI技术的最佳超参数，并同时调整其权重以产生最佳的优化权重集成模型。特别是，通过集成基于法医的调查优化算法，径向基函数神经网络和最小二乘支持向量回归，建立了开发的MOES。进行了四个预测RC梁结构力学的案例研究，以评估MOES的性能，并将其与其他单个AI模型，传统集成模型，混合模型和经验方法的性能进行比较。交叉验证的分析结果表明，MOES是最可靠的方法，在所有性能评估指标中均达到了最佳值。自动化的预测分析揭示了MOES的鲁棒性，效率和稳定性。因此，所提出的方法是用于预测RC梁结构力学的极有前途的工具。MOES在估算RC梁机械强度方面的成功重新定义了优化集成AI模型的方法，这是本研究对相关知识体系的主要贡献。