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 Articles 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/. Title A name given to the resource Assessment of ML techniques and suitability to predict the compressive strength of high-performance concrete (HPC) Subject The topic of the resource Extended deep neural network (EDNN); High-performance concrete (HPC); Multi-objective jellyfish optimization (MOJO); Strength characteristic Description An account of the resource Using industrial soil waste or secondary materials for making cement and concrete has encouraged the construction industry because it uses fewer natural resources. High-performance concrete (HPC) is recognized for its exceptional strength and sturdiness compared to conventional concrete. Accurate prediction of the compressive concentration of HPC is vital for optimizing the concrete mix design and ensuring structural integrity. Machine learning (ML) techniques have shown promise in predicting concrete properties, including compressive strength. This research focuses on various ML techniques for their suitability in predicting the compressive dilution of HPC. In this research, the Extended Deep Neural Network (EDNN) technique is used to analyze the strengths, limitations, and performance of different ML algorithms and identify the most effective methods for this specific prediction task. However, there is a problem with accuracy. Therefore, our research approach is the EDNN-centred strength characteristics prediction of HPC. In the suggested approach, data is initially acquired. Afterward, the data is pre-processed through normalization and removing missing data. Thus, the data are fed into the EDNN algorithm, which forecasts the strength characteristics of the particular mixed input designs. With the Multi-Objective Jellyfish Optimization (MOJO) technique, the value of weight is initialized in the EDNN. The activation function is the Gaussian radial function. In the experimental analysis, the implementation of the suggested EDNN is evaluated to the performance of the prevailing algorithms. When compared to current research methodologies, the proposed method performs better in this regard. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Creator An entity primarily responsible for making the resource Gupta M.; Upreti K.; Yadav S.; Verma M.; Mageswari M.; Tiwari A. Source A related resource from which the described resource is derived Asian Journal of Civil Engineering, Vol-25, No. 8, pp. 5741-5752. Publisher An entity responsible for making the resource available Springer Nature Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42107-024-01142-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42107-024-01142-5</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201300311&amp;doi=10.1007%2Fs42107-024-01142-5&amp;partnerID=40&amp;md5=c2ac730760753c66d72ce056fadd22fe" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201300311&amp;doi=10.1007%2fs42107-024-01142-5&amp;partnerID=40&amp;md5=c2ac730760753c66d72ce056fadd22fe</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15630854 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gupta M., Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, India; Upreti K., CHRIST University, Delhi NCR Campus, Ghaziabad, India; Yadav S., Jaypee Business School, JIIT, Sector-62, Noida, India; Verma M., GLA University, Mathura, India; Mageswari M., Panimalar Engineering College, Chennai, India; Tiwari A., CHRIST University, Delhi NCR Campus, Ghaziabad, India