Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model
This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty t...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/5589688 |
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| author | A. Shamila Ebenezer S. Deepa Kanmani V. Sheela K. Ramalakshmi V. Chandran M. G. Sumithra B. Elakkiya Bharani Murugesan |
| author_facet | A. Shamila Ebenezer S. Deepa Kanmani V. Sheela K. Ramalakshmi V. Chandran M. G. Sumithra B. Elakkiya Bharani Murugesan |
| author_sort | A. Shamila Ebenezer |
| collection | DOAJ |
| description | This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time-consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision-based techniques for automatic damage detection have been developed. This paper’s civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%. |
| format | Article |
| id | doaj-art-70436c52c9e64f17a21049983e6048c6 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-70436c52c9e64f17a21049983e6048c62025-02-03T01:24:48ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/55896885589688Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning ModelA. Shamila Ebenezer0S. Deepa Kanmani1V. Sheela2K. Ramalakshmi3V. Chandran4M. G. Sumithra5B. Elakkiya6Bharani Murugesan7Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, IndiaDepartment of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, IndiaDepartment of Civil Engineering, Sri Ranganathar Institute of Engineering and Technology, Coimbatore, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Alliance School of Engineering and Design, Alliance University, Bangalore, Karnataka, IndiaDepartment of Electronics and Communication Engineering, Dr. N. G. P. Institute of Technology, Kalapatti, Coimbatore 641048, IndiaDepartment of Electronics and Communication Engineering, Dr. N. G. P. Institute of Technology, Kalapatti, Coimbatore 641048, IndiaDepartment of Electronics and Communication Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu 600062, IndiaSchool of Textile Leather and Fashion Technology Kombolcha 208, Kombolcha Institute of Technology, Wollo University, South Wollo, EthiopiaThis article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time-consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision-based techniques for automatic damage detection have been developed. This paper’s civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.http://dx.doi.org/10.1155/2021/5589688 |
| spellingShingle | A. Shamila Ebenezer S. Deepa Kanmani V. Sheela K. Ramalakshmi V. Chandran M. G. Sumithra B. Elakkiya Bharani Murugesan Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model Advances in Civil Engineering |
| title | Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model |
| title_full | Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model |
| title_fullStr | Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model |
| title_full_unstemmed | Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model |
| title_short | Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model |
| title_sort | identification of civil infrastructure damage using ensemble transfer learning model |
| url | http://dx.doi.org/10.1155/2021/5589688 |
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