Ahmad Waqas, Ahmad Ayaz, Ostrowski Krzysztof Adam, Aslam Fahid, Joyklad Panuwat, Zajdel Paulina
Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland.
Materials (Basel). 2021 Oct 2;14(19):5762. doi: 10.3390/ma14195762.
The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures.
用于确定混凝土力学性能的浇筑和测试试件是一项耗时的工作。本研究采用监督式机器学习技术、装袋法、自适应增强算法、基因表达式编程和决策树来估计含有辅助胶凝材料（粉煤灰和高炉矿渣）的混凝土的抗压强度。使用决定系数（R）、平均绝对误差、均方误差和均方根误差对模型的性能进行比较和评估。使用k折交叉验证方法进一步验证模型的性能。与其他采用的方法相比，装袋模型在预测结果方面更有效，R值为0.92。还进行了敏感性分析，以确定运行模型所使用的每个参数的贡献水平。使用机器学习（ML）技术预测混凝土的力学性能将有利于土木工程领域，因为它将节省时间、精力和资源。所提出的技术对于预测含有辅助胶凝材料（SCM）的混凝土的强度性能是有效的，并为混凝土构件和结构的智能设计铺平了道路。
