Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning - Kennedy C. Onyelowe; Viroon Kamchoom; Ahmed M. Ebid; Shadi Hanandeh; Susana Monserrat Zurita Polo; Vilma Fernanda Noboa Silva; Rodney Orlando Santilln Murillo; Rolando Fabin Zabala Vizuete; Paul Awoyera; Siva Avudaiappan (My University, 2025)
The traditional evaluation of compressive strength through repeated experimental works can be resource-intensive, time-consuming, and environmentally taxing. Leveraging advanced machine learning (ML) offers a faster, cheaper, and more sustainable alternative for evaluating and optimizing concrete properties, particularly for materials incorporating industrial wastes and steel fibers. In this research work, a total of 166 records were collected and partitioned into training set (130 records = 80%) and validation set (36 records = 20%) in line with the requirements of data partitioning and sorting for optimal model performance. These data entries represented ten (10) components of the steel fiber reinforced concrete such as C, W, FAg, CAg, PL, SF, FA, Vf, FbL, and FbD, which were applied as the input variables in the model and Cs, which was the target. Advanced machine learning techniques were applied to model the compressive strength (Cs) of the steel fiber reinforced concrete such as "Semi-supervised classifier (Kstar)", "M5 classifier (M5Rules), "Elastic net classifier (ElasticNet), "Correlated Nystrom Views (XNV)", and "Decision Table (DT)". All models were created using 2024 "Weka Data Mining" software version 3.8.6. Also, accuracies of developed models were evaluated by comparing sum of squared error (SSE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), Error (%), Accuracy (%) and coefficient of determination (R2), correlation coefficient (R), willmott index (WI), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE) and symmetric mean absolute percentage error (SMAPE) between predicted and calculated values of the output. At the end, machine learning has been found to be a transformative approach that enhances the efficiency, cost-effectiveness, and sustainability of evaluating compressive strength in industrial wastes-based concrete reinforced with steel fiber. Among the models reviewed, Kstar and DT emerge as the most practical for achieving precise and sustainable results. Their adoption can significantly reduce environmental impacts and promote the sustainable use of industrial by-products in construction. The sensitivity of the input variables on the compressive strength of industrial wastes-based concrete reinforced with steel fiber produced 36% from C, 71% from W, 70% from FAg, 60% from CAg, 34% from PL, 5% from SF, 33% from FA, 67% from Vf, 5% from FbL, and 61% from 61%. Fiber Volume Fraction (Vf) (67%) high sensitivity suggests that steel fiber content greatly impacts crack resistance and tensile strength. Steel Fiber Orientation (61%) indicates the importance of fiber alignment in distributing stresses and enhancing structural integrity.
Keywords: Innovative concrete reinforcement materials