Predictive modeling of mechanical behavior in waste ceramic concrete ‎using machine learning techniques

  • Authors

    • Kamal Upreti Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR ‎Ghaziabad, Uttar Pradesh, India
    • Adesh Kumar Pandey Department of Information Technology, KIET Group of Institutions, Ghaziabad, India
    • Virendra Singh Kushwah School of Computing Science & Engineering, VIT Bhopal University, Highway, Kothrikalan, ‎Sehore, Madhya Pradesh, India
    • Pravin R. Kshirsagar Department of Electronics & Telecommunication Engineering, J D College of Engineering ‎& Management, Nagpur, Maharashtra, India
    • Kamal Kant Sharma Department of Information Technology, KIET Group of Institutions, Ghaziabad, India
    • Jagendra Singh School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India
    • Jyoti Parashar Bharati Vidyapeeth’s Institute of Computer Applications and Management, New Delhi,
    • Rituraj Jain Department of Information Technology, Marwadi University, Rajkot, Gujarat, India
    https://doi.org/10.14419/ywwvvd04

    Received date: March 30, 2025

    Accepted date: April 26, 2025

    Published date: April 30, 2025

  • Machine Learning; Waste Ceramic Concrete; Artificial Neural Network; LightGBM; Construction ‎Industry; Environmental Sustainability

  • Abstract

    This study identifies the critical demand for a certain approach that aims to predict and ascertain ‎the mechanical behavior of con-crete admixed with waste ceramic, a method to overcome and ‎mitigate the related environmental challenges as it pertains to the construction field. Concrete ‎modification with ceramic wastes has received significant attention due to its potential ‎improvement in sustainability. The developed predictive models on waste ceramic concrete ‎‎(WCC) involved the use of advanced machine learning techniques such as Artificial Neural ‎Network (ANN) and Light Gradient Boosting Machine (LightGBM). Experimental datasets ‎were formulated based on 5% and 20% variability of ceramic waste percentages as input ‎variables for training and testing data for validation of the proposed model. In each case, iterative ‎training improved model performance, with the ANN showing moderate predictability (R² = ‎‎0.70 and 0.67) and LightGBM demonstrating stronger accuracy. Predictive values ranged ‎between 1.02 MPa and 0.12 MPa for compressive and splitting tensile strengths and had R² ‎values of 0.70 and 0.67 for the ANN model, respectively. The established findings will lead to ‎a dependable framework for assessing and improving the performance of ceramic waste-modified concrete. In this regard, these findings have reinforced the potential of machine ‎learning in developing sustainable construction practices. This paper is of value to ‎engineers and decision-makers within the construction industry, providing an informed choice ‎towards environmental sustainability and better risk management‎.

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  • How to Cite

    Upreti, K., Pandey, A. K. ., Kushwah, V. S. ., Kshirsagar, P. R. ., Sharma, K. K. ., Singh, J. ., Parashar, J. ., & Jain, R. . (2025). Predictive modeling of mechanical behavior in waste ceramic concrete ‎using machine learning techniques. International Journal of Basic and Applied Sciences, 14(1), 124-135. https://doi.org/10.14419/ywwvvd04