Bhupinder Kumar
Assistant Professor
Bhupinder Kumar is an Assistant Professor in the School of Core Engineering at Shoolini University, Solan. He holds a PhD in Civil Engineering with a specialisation in Transportation Engineering and works in the domain of advanced pavement engineering and high-performance construction materials.
His research interests include sustainable construction materials, plastic waste utilisation in flexible pavements, geopolymer concrete, and the application of artificial intelligence in civil engineering. He focuses on developing reliable prediction models for parameters such as Marshall Stability, Bulk Specific Gravity, and the durability of modified asphalt and concrete, using machine learning and soft computing techniques.
Asst Prof Kumar has published his research in reputed Scopus- and SCIE-indexed journals and regularly presents his work at international conferences. His research approach is strongly application-oriented and interdisciplinary, aimed at developing innovative material technologies and data-driven solutions for modern transportation infrastructure.
His work supports eco-friendly construction practices, resource-efficient pavement systems, and the development of next-generation infrastructure materials aligned with sustainability goals. With a strong belief in innovation that delivers real-world impact, Asst Prof Kumar actively contributes to research development, technical skill enhancement, and knowledge sharing, bridging the gap between advanced research and practical field applications for resilient and future-ready infrastructure.
Publications
1. Kumar, B., Alyaseen, A. and Kumar, N., 2025. Utilizing Conventional and State-of-the-Art Machine Learning Algorithms to Predict Marshall Stability of Modified Asphalt Mixes Incorporating PET, HDPE, and PVC Plastic Waste: Performance Evaluation and Mix Optimization: B. Kumar et al. International Journal of Pavement Research and
Technology, pp.1-32.
2. Kumar, B., Kumar, N., Rustum, R. and Shankar, V., 2025. Comparative Analysis of
Machine Learning Techniques for Predicting Bulk Specific Gravity in Modified Asphalt
Mixtures Incorporating Polyethylene Terephthalate (PET), High-Density Polyethylene
(HDPE), and Polyvinyl Chloride (PVC). Machine Learning and Knowledge
Extraction, 7(2), p.30.
3. Kumar, B. and Kumar, N., 2024. Enhancing asphalt mixture performance through waste plastic modification: a comprehensive analysis of optimal compositions and volumetric
properties. Journal of Structural Integrity and Maintenance, 9(2), p.2376804.
4. Kumar, B., Kumar, N. and Mehta, V., 2024. Evaluation of soft computing techniques for
predicting Marshall Stability of waste plastic-reinforced asphalt concrete. Journal of
Structural Integrity and Maintenance, 9(2), p.2371038.
5. Kumar, B. and Kumar, N., 2024. Forecasting Marshall stability of waste plastic reinforced concrete using SVM, ANN, and tree-based techniques. Multiscale and Multidisciplinary Modeling, Experiments and Design, pp.1-19.
6. Kumar, B., Kumar, N. and Kashyap, V., 2024. Soft computing-based optimization of plastic waste utilization in flexible pavement construction. Multiscale and Multidisciplinary Modeling, Experiments and Design, pp.1-12.
7. Kashyap, V., Poddar, A., Sihag, P. and Kumar, B., 2023. Forecasting compressive strength of jute fiber reinforced concrete using ANFIS, ANN, RF and RT models. Asian Journal of Civil Engineering, pp.1-12.
8. Kumar, B. and Kumar, N., 2023. Assessment of Marshall Stability of asphalt concrete with plastic waste using soft computing techniques. Multiscale and multidisciplinary modeling, experimentsand design, 6(4), pp.733-745.
Book Chapters:
1. Kumar, B. and Kumar, N., 2023, December. Compressive Strength Prediction of Coal Ash-Reinforced Concrete Using Machine Learning. In International Conference on Signal, Machines, Automation, and Algorithm (pp. 197-212). Singapore: Springer Nature Singapore.
2. Kumar, B., Kumar, N., Elbeltagi, A. and Almohammed, F.H., 2022. Evaluation of ANN and tree-based techniques for predicting the compressive strength of granite powder reinforced concrete. In Applications of computational intelligence in concrete technology (pp. 253-267). CRC Press.
3. Kashyap, V., Poddar, A. and Kumar, B., 2024. Exploration of coal ash to determine the strength characteristics of self compacting concrete. In Alternative Cementitious Materials for Self-Compacting Concrete (pp. 35-44). Woodhead Publishing.
Conferences:
1. International Conference SIGMAA–2023, Springer Nature International Conference, held on December 15–16, 2023 – Presented research paper entitled “Utilizing Soft Computing Techniques for Enhancing Concrete Performance through Coal Ash Incorporation.”
2. International Conference on Sustainable Experimentation and Modeling in Civil
Engineering (SEMCE–2023), held on August 10–11, 2023 – Presented a paper entitled
“Soft Computing-Based Optimization of Plastic Waste Utilization in Flexible Pavement
Construction.”
3. International Conference on Recent Trends in Engineering Science & Technology
(RTEST–2024), organized by Government Hydro Engineering College, Bandla, Bilaspur,
Himachal Pradesh, held on January 29–30, 2024 – Presented a research paper entitled
“Application of Machine Learning Algorithms to Assess Concrete Strength with Coal Ash
Integration.”
Contact Details:
E mail id: bhupender@shooliniuniversity.com
LinkedIn profile: https://www.linkedin.com/in/dr-bhupender-kumar-057a50166/