Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models
The shear behavior of beams cast with steel fiber reinforced concrete and provided with stirrups is a complex phenomenon that depends on various factors. In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm is provided, to exp...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/8/1265 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849712502916513792 |
|---|---|
| author | B. R. Kavya A. S. Shrikanth K. S. Sreekeshava |
| author_facet | B. R. Kavya A. S. Shrikanth K. S. Sreekeshava |
| author_sort | B. R. Kavya |
| collection | DOAJ |
| description | The shear behavior of beams cast with steel fiber reinforced concrete and provided with stirrups is a complex phenomenon that depends on various factors. In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm is provided, to explore the relationship between the material and dimensional characteristics of a concrete beam and its shear strength. A database with diverse material properties associated with the shear strength of a steel fiber reinforced concrete beam was established from numerous reliable published research articles and was utilized for the development and evaluation of the model. The obtained results from the hybrid support vector regression model were then validated through the results of the artificial neural network and convolutional neural network models combined with the particle swarm optimization algorithm. In conclusion, the adopted hybrid support vector regression approach was proven to be a successful engineering technique that can be used in structural and construction engineering problems. |
| format | Article |
| id | doaj-art-6993bd2f7e3b47d3a6cdd8f68cc64e9d |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-6993bd2f7e3b47d3a6cdd8f68cc64e9d2025-08-20T03:14:15ZengMDPI AGBuildings2075-53092025-04-01158126510.3390/buildings15081265Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning ModelsB. R. Kavya0A. S. Shrikanth1K. S. Sreekeshava2Department of Civil Engineering, Adichunchanagiri Institute of Technology, Chikkamagaluru 577102, IndiaDepartment of Mathematics, Adichunchanagiri Institute of Technology, Chikkamagaluru 577102, IndiaDepartment of Civil Engineering, Jyothy Institute of Technology, Visvesvaraya Technological University, Belagavi 590018, IndiaThe shear behavior of beams cast with steel fiber reinforced concrete and provided with stirrups is a complex phenomenon that depends on various factors. In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm is provided, to explore the relationship between the material and dimensional characteristics of a concrete beam and its shear strength. A database with diverse material properties associated with the shear strength of a steel fiber reinforced concrete beam was established from numerous reliable published research articles and was utilized for the development and evaluation of the model. The obtained results from the hybrid support vector regression model were then validated through the results of the artificial neural network and convolutional neural network models combined with the particle swarm optimization algorithm. In conclusion, the adopted hybrid support vector regression approach was proven to be a successful engineering technique that can be used in structural and construction engineering problems.https://www.mdpi.com/2075-5309/15/8/1265shear strengthsteel fiber reinforced concretesupport vector regressionswarm particle optimizationartificial neural networkconvolutional neural network |
| spellingShingle | B. R. Kavya A. S. Shrikanth K. S. Sreekeshava Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models Buildings shear strength steel fiber reinforced concrete support vector regression swarm particle optimization artificial neural network convolutional neural network |
| title | Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models |
| title_full | Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models |
| title_fullStr | Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models |
| title_full_unstemmed | Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models |
| title_short | Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models |
| title_sort | prediction of shear strength of steel fiber reinforced concrete beams with stirrups using hybrid machine learning and deep learning models |
| topic | shear strength steel fiber reinforced concrete support vector regression swarm particle optimization artificial neural network convolutional neural network |
| url | https://www.mdpi.com/2075-5309/15/8/1265 |
| work_keys_str_mv | AT brkavya predictionofshearstrengthofsteelfiberreinforcedconcretebeamswithstirrupsusinghybridmachinelearninganddeeplearningmodels AT asshrikanth predictionofshearstrengthofsteelfiberreinforcedconcretebeamswithstirrupsusinghybridmachinelearninganddeeplearningmodels AT kssreekeshava predictionofshearstrengthofsteelfiberreinforcedconcretebeamswithstirrupsusinghybridmachinelearninganddeeplearningmodels |