Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder

• Published in: Case Studies in Construction Materials Vol. 19; p. e02557
• Main Authors: Mahmood, Muhammad Sarmad, Elahi, Ayub, Zaid, Osama, Alashker, Yasser, Șerbănoiu, Adrian A., Grădinaru, Cătălina M., Ullah, Kiffayat, Ali, Tariq
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023; Elsevier
• Subjects: Compressive strength; Deep learning; Machine learning; Marble powder; Rice husk ash; Self compacting concrete; ANN; LM; Marble powder; OPC; DL; RHA; KNN; Compressive strength; SVM; C-A-S-H; SCGB; SVR; Self compacting concrete; DT; BR; Deep learning; ADAM; BPNN; GB; SP; CA; MSE; ML; R2; MP; AI; NRMSE; MAPE; RMSE; CS; MAE; Rice husk ash; SGD; RMSprop; SCC; XGB; GEP; RF; Machine learning; FA; TS; ADB
• ISSN: 2214-5095; 2214-5095
• DOI: 10.1016/j.cscm.2023.e02557

Focusing on sustainable development, the demand for alternative materials in concrete, especially for Self-Compacting Concrete (SCC), has risen due to excessive cement usage and resulting CO2 emissions. As Compressive Strength (CS) is dominant among concrete properties, this research concentrates on developing SCC by incorporating Rice Husk Ash (RHA) and Marble Powder (MP) as cement and filler replacements, respectively, while applying Machine Learning (ML) and Deep Learning (DL) techniques to forecast the CS of RHA/MP-based SCC. The research further evaluates material characteristics, with a strong emphasis on ML and DL for CS prediction. Concrete samples with various mixed ratios were cast and examined after 91 days to collect data for model application. In the experimental technique, 133 samples were gathered, and CS was predicted using seven input factors (cement, RHA, MP, superplasticizer, coarse aggregate, fine aggregate, and water) in an 80:20 ratio. Various ML algorithms, including linear regression, ridge regression, lasso regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and boosting methods such as gradient boost (GB), XG boost (XGB), and adaptive boosting (ADB) are employed, along with the DL technique of backpropagation neural network (BPNN) with different optimizer algorithms (Adam, SGD, RMSprop) to predict CS in RHA/MP-based SCC. The predicted data is further validated using evaluation parameters such as R-squared (R2), mean squared error (MSE), normalized root mean squared error (NRMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Comparatively, ML ensemble algorithms and BPNN using Adam and RMSprop optimizers demonstrate high accuracy in predicting CS outcomes, indicated by their high coefficient correlation R2 values and low error values.