Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upreti, Kamal; Pandey, Adesh Kumar; Kushwah, Virendra Singh; Kshirsagar, Pravin R.; Sharma, Kamal Kant; Singh, Jagendra; Parashar, Jyoti; Jain, Rituraj Title A name given to the resource Predictive modeling of mechanical behavior in waste ceramic concrete using machine learning techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.124-135 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14419/ywwvvd04" target="_blank" rel="noreferrer noopener">https://doi.org/10.14419/ywwvvd04</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005015895?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005015895?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 &amp; Engineering, VIT Bhopal University, Highway, Kothrikalan, Madhya Pradesh, Sehore, India; Kshirsagar P.R., Department of Electronics &amp; Telecommunication Engineering, J D College of Engineering &amp;; Management, Maharashtra, Nagpur, India; Sharma K.K., Department of Information Technology, KIET Group of Institutions, Ghaziabad, India; Singh J., School of Computer Science Engineering &amp; 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 Description An account of the resource 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. Subject The topic of the resource Artificial Neural Network; Construction Industry; Environmental Sustainability; LightGBM; Machine Learning; Waste Ceramic Concrete Publisher An entity responsible for making the resource available Science Publishing Corporation Inc. Relation A related resource ISSN: 22275053; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online