ItemArticle

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

Title
Predictive modeling of mechanical behavior in waste ceramic concrete using machine learning techniques
Creator
Upreti, Kamal; Pandey, Adesh Kumar; Kushwah, Virendra Singh; Kshirsagar, Pravin R.; Sharma, Kamal Kant; Singh, Jagendra; Parashar, Jyoti; Jain, Rituraj
Description
This study identifies the critical demand for a certain approach that aims to predict and ascertain the mechanical behavior of concrete 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 (R = 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 R 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. Kamal Upreti et al.
Source
International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.124-135
Date
01-01-2025
Publisher
Science Publishing Corporation Inc.
Subject
Artificial Neural Network; Construction Industry; Environmental Sustainability; LightGBM; Machine Learning; Waste Ceramic Concrete
Coverage
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Pandey A.K., Department of Information Technology, KIET Group of Institutions, Ghaziabad, India; Kushwah V.S., School of Computing Science & Engineering, VIT Bhopal University, Highway, Kothrikalan, Madhya Pradesh, Sehore, India; Kshirsagar P.R., Department of Electronics & Telecommunication Engineering, J D College of Engineering &; Management, Maharashtra, Nagpur, India; Sharma K.K., Department of Information Technology, KIET Group of Institutions, Ghaziabad, India; Singh J., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Parashar J., Bharati Vidyapeeths Institute of Computer Applications and Management, New Delhi, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 22275053;
Format
online
Language
English
Type
Article

Citation

Upreti, Kamal; Pandey, Adesh Kumar; Kushwah, Virendra Singh; Kshirsagar, Pravin R.; Sharma, Kamal Kant; Singh, Jagendra; Parashar, Jyoti; Jain, Rituraj, “Predictive modeling of mechanical behavior in waste ceramic concrete using machine learning techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23255.

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