The construction industry is facing challenges from the hazardous nature of Ordinary Portland Cement (OPC) production as one of the main contributors to global warming and CO₂ emission. Given its increasing demand, the need to replace OPC with sustainable alternatives for green concrete production is essential. The promising results of using Rice Husk Ash (RHA) in concrete mixtures as a supplementary cementitious material (SCM) have attracted great attention. However, conventional laboratory procedures for the analysis of concrete properties like Compressive Strength (CS) are laborious and expensive. Therefore, developing a reliable and accurate model for its prediction can save time and reduce operational costs. In this study, the Artificial Neural Network (ANN) technique was hybridized with a robust optimization technique called Particle Swarm Optimization algorithm with Two Differential mutations (PSOTD) to predict the CS of RHA concrete. To compare the results of the PSOTD over classical optimization techniques, three other algorithms, including the best reported conventional Back-Propagation algorithm (i.e., Levenberg-Marquardt (LM)), Differential Evolution (DE), and Particle Swarm Optimization (PSO), were employed. For the training dataset, the coefficients of determination obtained for the ANN-LM, ANN-DE, ANN-PSO, and ANN-PSOTD were 0.9797, 0.9389, 0.9462, and 0.9803, while for the testing dataset, the values were 0.9682, 0.9198, 0.9223, and 0.9697, respectively. Overall, the error measures show the superiority of the ANN-PSOTD in predicting the CS of RHA concretes. To further enhance the prediction accuracy and reliability, the hybrid model of ANN-LM and ANN-PSOTD (i.e., ANN-PSOTD-LM) was also proposed. However, there is a trade-off between the computational time and the accuracy of models when the ANN-PSOTD-LM technique is chosen.

_{作为全球变暖和 CO 2}排放的主要贡献者之一，普通硅酸盐水泥 (OPC) 生产的危险性使建筑行业面临挑战。鉴于其日益增长的需求，用可持续的替代品替代 OPC 来生产绿色混凝土至关重要。在混凝土混合物中使用稻壳灰 (RHA) 作为辅助胶凝材料 (SCM) 的良好结果引起了人们的极大关注。然而，用于分析混凝土特性（如抗压强度）的传统实验室程序(CS) 既费力又昂贵。因此，为其预测开发可靠且准确的模型可以节省时间并降低运营成本。在这项研究中，人工神经网络 (ANN) 技术与称为粒子群优化具有两个差分突变 (PSOTD) 的算法来预测 RHA 混凝土的 CS。为了比较 PSOTD 与经典优化技术的结果，其他三种算法，包括报道最好的传统反向传播算法（即 Levenberg-Marquardt (LM)）、差分进化 (DE) 和粒子群优化 (PSO)，被雇用。对于训练数据集，ANN-LM、ANN-DE、ANN-PSO 和 ANN-PSOTD 获得的确定系数分别为 0.9797、0.9389、0.9462 和 0.9803，而对于测试数据集，值为 0.9682、0.9198 ，分别为 0.9223 和 0.9697。总体而言，误差测量显示了 ANN-PSOTD 在预测 RHA 混凝土的 CS 方面的优越性。为了进一步提高预测的准确性和可靠性，ANN-LM 和 ANN-PSOTD 的混合模型（即，还提出了 ANN-PSOTD-LM)。However, there is a trade-off between the computational time and the accuracy of models when the ANN-PSOTD-LM technique is chosen.