Sourav Kumar
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Sourav Kumar has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Aman Kumar
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
1 shared publication - Denise-Penelope N. Kontoni
Department of Civil Engineering, School of Engineering, University of the Peloponnese, GR-26334 Patras, Greece
1 shared publication - Harish Chandra Arora
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
1 shared publication - Krishna Kumar
Department of ECSE, The University of Auckland, Auckland 1010, New Zealand
1 shared publication - Madhu Sharma
Civil Engineering Department, Government Hydro Engineering College, Bilaspur, India
1 shared publication - Nishant Raj Kapoor
Central Building Research Institute
1 shared publication
Latest Publications
- Open Access
Axial Capacity of FRP-Reinforced Concrete Columns: Computational Intelligence-Based Prognosis for Sustainable Structures
Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated using analytical, as well as machine learning, models. A total of fourteen popular analytical models and one proposed machine learning-based model were used to estimate the ALCC of the concrete columns. The proposed machine learning model is based on an artificial neural network (ANN) method. The performance of the ANN, as well as the analytical models, are assessed using six different performance indices. The R-value of the developed ANN model is 0.9758, followed by an NS value of 0.9513. It has been found that the mean absolute percentage error of the best-fitted analytical model is 328.71% higher than the ANN model, and the root-mean-square error value of the best-fitted analytical model is 211.97% higher than the ANN model. The evaluated data demonstrate that the proposed ANN model performs better than the other analytical models. The developed model is quick and easy-to-use to estimate the axial capacity of the FRP-reinforced concrete columns.
Open AccessAxial Capacity of FRP-Reinforced Concrete Columns: Computational Intelligence-Based Prognosis for Sustainable Structures
Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated using analytical, as well as machine learning, models. A total of fourteen popular analytical models and one proposed machine learning-based model were used to estimate the ALCC of the concrete columns. The proposed machine learning model is based on an artificial neural network (ANN) method. The performance of the ANN, as well as the analytical models, are assessed using six different performance indices. The R-value of the developed ANN model is 0.9758, followed by an NS value of 0.9513. It has been found that the mean absolute percentage error of the best-fitted analytical model is 328.71% higher than the ANN model, and the root-mean-square error value of the best-fitted analytical model is 211.97% higher than the ANN model. The evaluated data demonstrate that the proposed ANN model performs better than the other analytical models. The developed model is quick and easy-to-use to estimate the axial capacity of the FRP-reinforced concrete columns.