Ali Tariq, Onyelowe Kennedy C, Mahmood Muhammad Sarmad, Qureshi Muhammad Zeeshan, Kahla Nabil Ben, Rezzoug Aïssa, Deifalla Ahmed
Department of Civil Engineering, Swedish College of Engineering and Technology, Wah, 47080, Pakistan.
Department of Civil Engineering, School of Engineering and Applied Sciences, Kampala International University, Kampala, Uganda.
Sci Rep. 2025 Feb 10;15(1):4997. doi: 10.1038/s41598-025-89263-y.
The increasing demand for sustainable construction materials has led to the incorporation of Palm Oil Fuel Ash (POFA) into concrete to reduce cement consumption and lower CO₂ emissions. However, predicting the compressive strength (CS) of POFA-based concrete remains challenging due to the variability of input factors. This study addresses this issue by applying advanced machine learning models to forecast the CS of POFA-incorporated concrete. A dataset of 407 samples was collected, including six input parameters: cement content, POFA dosage, water-to-binder ratio, aggregate ratio, superplasticizer content, and curing age. The dataset was divided into 70% for training and 30% for testing. The models evaluated include Hybrid XGB-LGBM, ANN, Bagging, LSSVM, GEP, XGB and LGBM. The performance of these models was assessed using key metrics, the coefficient of determination (R2), root mean square error (RMSE), normalized root means square error (NRMSE), mean absolute error (MAE) and Willmott index (d). The Hybrid XGB-LGBM model achieved the maximum R2 of 0.976 and the lowest RMSE, demonstrating superior accuracy, followed by the ANN model with an R2 of 0.968. SHAP analysis further validated the models by identifying the most impactful input factors, with the water-to-binder ratio emerging as the most influential. These predictive models offer the construction industry a reliable framework for evaluating POFA concrete, reducing the need for extensive experimental testing, and promoting the development of more eco-friendly, cost-effective building materials.
对可持续建筑材料的需求不断增加，促使人们将棕榈油燃料灰（POFA）掺入混凝土中，以减少水泥用量并降低二氧化碳排放量。然而，由于输入因素的变异性，预测基于POFA的混凝土的抗压强度（CS）仍然具有挑战性。本研究通过应用先进的机器学习模型来预测掺入POFA的混凝土的CS来解决这一问题。收集了一个包含407个样本的数据集，包括六个输入参数：水泥含量、POFA用量、水胶比、骨料比、高效减水剂含量和养护龄期。数据集分为70%用于训练，30%用于测试。评估的模型包括混合XGB-LGBM、人工神经网络（ANN）、装袋法、最小二乘支持向量机（LSSVM）、基因表达式编程（GEP）、XGB和LGBM。使用关键指标评估这些模型的性能，包括决定系数（R2）、均方根误差（RMSE）、归一化均方根误差（NRMSE）、平均绝对误差（MAE）和威尔莫特指数（d）。混合XGB-LGBM模型实现了最高的R2为0.976和最低的RMSE，显示出卓越的准确性，其次是R2为0.968的ANN模型。SHAP分析通过识别最具影响力的输入因素进一步验证了这些模型，其中水胶比成为最具影响力的因素。这些预测模型为建筑行业提供了一个可靠的框架，用于评估POFA混凝土，减少了广泛实验测试的需求，并促进了更环保、更具成本效益的建筑材料的开发。
