Prashant Kumar
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Research Interests
Prashant Kumar has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Aman Kumar
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
5 shared publications - Harish Chandra Arora
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
4 shared publications - Moncef L. Nehdi
College of Engineering, University of Guelph, Guelph, Canada
2 shared publications - Nishant Raj Kapoor
Central Building Research Institute
2 shared publications - Alireza Bahrami
Department of Building Engineering, Energy Systems and Sustainability Science, Faculty of Engineering and Sustainable Development, University of Gävle, 801 76 Gävle, Sweden
1 shared publication Aman Kumar
1 shared publication
Latest Publications
- Open Access
- 3 February 2026
- Published by Elsevier in Progress in Engineering Science
- Vol. 3 (1)
Data-driven structural performance assessment of FRP-confined concrete under environmental exposure
- 3 February 2026
- Published by Elsevier in Progress in Engineering Science
- Vol. 3 (1)
Open AccessData-driven structural performance assessment of FRP-confined concrete under environmental exposure
- Open Access
- 17 August 2024
- Published by Elsevier in Construction and Building Materials
- Vol. 445
Automated explainable ensemble learning prediction of FRP-bar-to-concrete bond strength and failure pattern under diverse exposure scenarios
- 17 August 2024
- Published by Elsevier in Construction and Building Materials
- Vol. 445
Open AccessAutomated explainable ensemble learning prediction of FRP-bar-to-concrete bond strength and failure pattern under diverse exposure scenarios
- Open Access
- 22 June 2024
- Published by Elsevier in KSCE Journal of Civil Engineering
- Vol. 28 (9) , 3835-3844
Prediction of Axial Capacity of RC Columns Reinforced with Ferro-cement Jacketing: A Data-driven Machine Learning Strategy
- 22 June 2024
- Published by Elsevier in KSCE Journal of Civil Engineering
- Vol. 28 (9) , 3835-3844
Open AccessPrediction of Axial Capacity of RC Columns Reinforced with Ferro-cement Jacketing: A Data-driven Machine Learning Strategy
- Open Access
- 22 December 2023
- Published by Elsevier in Developments in the Built Environment
- Vol. 17, 100311
Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions
- 22 December 2023
- Published by Elsevier in Developments in the Built Environment
- Vol. 17, 100311
Open AccessMachine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions
- Open Access
Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders
The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.
Open AccessDevelopment of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders
The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.