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

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

Received date: March 30, 2025

Accepted date: April 26, 2025

Published date: April 30, 2025

DOI:

https://doi.org/10.14419/ywwvvd04

Keywords:

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

Received date: March 30, 2025

Accepted date: April 26, 2025

Published date: April 30, 2025