In the present research, the information on compressive strength of self-compacting concrete (SCC) containing rice husk ash (RHA) and calcium carbide waste (CCW) as an admixture cured for 28 days was provided. The research applied feedforward propagation neural network (FFNN), emotional neural network (EANN), and conventional linear regression (LR) in the prediction of compressive in which FFNN, EANN, and LR models were trained on the experimental data obtained from addition of 0%–10% RHA and 0%–20% CCW in the SCC mixtures. The results revealed that inclusion of CCW reduces the workability of SCC mixtures and increases in compressive strength at 28 days were observed for SCC mixture containing 10% RHA and 0% CCW against the reference mixtures. The results also indicated that all the AI models (FFNN, EANN, and LR) performed very well with R²-values higher than 0.8951 in both the testing and training stages. The results showed that EANN-M3, FFNN-M3, and LR-M3 combination has the highest performance evaluation criteria of R² = 0.9733 and 0.9610, R² = 0.9440 and 0.9454 and R² = 0.9117 and 0.9205 in both training and testing stages, respectively. It indicates the proposed models' high accuracy in predicting the compressive strength σ of self-compacting concrete with rice husk ash as cement replacement and calcium carbide waste as supplementary materials. The result also suggested that other models, like emerging algorithms, hybrid models, and optimization methods, could enhance the models’ performance.

在本研究中，提供了含有稻壳灰 (RHA) 和电石废料 (CCW) 作为外加剂固化 28 天的自密实混凝土 (SCC) 的抗压强度信息。该研究将前馈传播神经网络 (FFNN)、情感神经网络 (EANN) 和常规线性回归 (LR) 应用于压缩预测中，其中 FFNN、EANN 和 LR 模型是在添加 0 获得的实验数据上训练的SCC 混合物中的 %–10% RHA 和 0%–20% CCW。结果表明，加入 CCW 会降低 SCC 混合物的可加工性，并且在 28 天时观察到含有 10% RHA 和 0% CCW 的 SCC 混合物的抗压强度相对于参考混合物增加。结果还表明，所有 AI 模型（FFNN、EANN 和 LR）在R ^{2 -}在测试和训练阶段均高于 0.8951。结果表明，EANN-M3、FFNN-M3和LR-M3组合 在训练和测试阶段的性能评价标准最高，分别为R ²  = 0.9733和0.9610，R ²  = 0.9440和0.9454以及R ² = 0.9117和0.9205 ， 分别。这表明所提出的模型在预测以稻壳灰为水泥替代物和电石废料为补充材料的自密实混凝土的抗压强度σ方面具有较高的准确性。结果还表明，其他模型，如新兴算法、混合模型和优化方法，可以提高模型的性能。