Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

• Published in: Materials Vol. 14; no. 19; p. 5762
• Main Authors: Ahmad, Waqas, Ahmad, Ayaz, Ostrowski, Krzysztof Adam, Aslam, Fahid, Joyklad, Panuwat, Zajdel, Paulina
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 02.10.2021; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bagging; Blast furnace practice; Blast furnace slags; Cement hydration; Civil engineering; Compressive strength; Concrete; Decision trees; Errors; Fly ash; Gene expression; Laboratories; Machine learning; Mean square errors; Mechanical properties; Neural networks; Sensitivity analysis; Software
• ISSN: 1996-1944; 1996-1944
• 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 (R2), 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 R2 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.