Data-driven based estimation of waste-derived ceramic concrete from experimental results with its environmental assessment

Abstract

The significant requirement for natural resources, specifically as ingredients of cement, is accelerating due to the considerable growth of the construction sector. Further, cement production adversely affects climate change due to the generation of bulk CO2 emissions. At the same time, a significant quantum of ceramic waste is generated either in the ceramic production process or due to the demolition of ceramic products each year. The unavailability of an adequate way to dispose of this ceramic waste negatively impacts the environment and landfills. Numerous researchers have explored the potential of utilizing this ceramic waste powder as a partial cement replacement to reduce the allied issues. Hence, in the current study, the supervised machine learning (ML) algorithms, i.e., Decision Tree (DT), AdaBoost (AdB), Bagging (Bg), Random Forest (RF), Gradient Boosting (GB) and XGBoost (XGB) are employed for predicting the Compressive Strength (CS) of ceramic waste powder concrete (CWPC). The performance of models is also assessed by using the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash Sutcliffe efficiency (NSE). The k-fold cross-validation technique is applied afterwards to validate the model's performance. For predicting the CS of CWPC, the RF algorithm is the most effective among the employed algorithms, with a higher R2 value of 0.97 and significantly lesser RMSE and MAE values of 1.40 and 1.13, respectively. SHAP analysis shows that the curing days feature has the highest influence on the CS of CWPC. As per quantitative Environmental Impact Assessment (EIA), the concrete with 10% CWP content can have 6.78%, 8.68%, 7.18%, and 7.19% reduced impacts on natural resources, climate change, ecosystem quality, and human health, respectively. Moreover, the effects on non-renewable energy resources, depletion of the ozone layer, and global warming can also primarily be reduced by a maximum of 7%, 6%, and 9%, respectively. The application of ML techniques for estimating the CS of CWPC would benefit the field of civil engineering in terms of conserving resources, effort, and time.