AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques
Internet of Things (IoT) and Artificial Intelligence (AI) technologies are currently replacing the traditional methods of handling buildings, infrastructure, and facilities design, control, and maintenance due to their precision and ease of use. This paper proposes a novel automated algorithm for th...
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2023
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| Online Access: | http://dx.doi.org/10.1016/j.asej.2023.102520 https://www.sciencedirect.com/science/article/pii/S2090447923004094 http://hdl.handle.net/10576/50032 |
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| _version_ | 1857415085245333504 |
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| author | Khalid, Naji |
| author2 | Gowid, Samer Ghani, Saud |
| author2_role | author author |
| author_facet | Khalid, Naji Gowid, Samer Ghani, Saud |
| author_role | author |
| dc.creator.none.fl_str_mv | Khalid, Naji Gowid, Samer Ghani, Saud |
| dc.date.none.fl_str_mv | 2023-12-03T09:31:47Z 2023-10-18 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.asej.2023.102520 Naji, K., Gowid, S., & Ghani, S. (2023). AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques. Ain Shams Engineering Journal, 14(11), 102520. 2090-4479 https://www.sciencedirect.com/science/article/pii/S2090447923004094 http://hdl.handle.net/10576/50032 11 14 2090-4495 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Ain Shams University |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Concrete column Automated maintenance Deep learning Condition monitoring Facility management |
| dc.title.none.fl_str_mv | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Internet of Things (IoT) and Artificial Intelligence (AI) technologies are currently replacing the traditional methods of handling buildings, infrastructure, and facilities design, control, and maintenance due to their precision and ease of use. This paper proposes a novel automated algorithm for the health monitoring of concrete column base cover degradation based on IoT and the state-of-the-art deep learning framework, Convolutional Neural Network (CNN). This technique is developed for instance detection and localization of the major types of column defects. Three deep machine learning training models; namely, Resnet-50, Googlenet, and Visual Geometry Group (VGG19), with 7 different network configurations and inputs were studied and compared for their classification performance and certainty. Despite that, a few articles consider the certainty of the CNN classification results, this work investigates the certainty and employs the classification error score as a new performance measure. The results of this study demonstrated the effectiveness of the proposed defect detection and localization algorithm as it managed to read all barcodes, localize defective columns, and binary classify the condition of the concrete covers against their surrounding objects. They also showed that the VGG19 network outperformed the other addressed network models and configurations. The VGG19 network yielded a health condition classification accuracy of 100% with an RMSE of 0.33% and a maximum classification error score of 0.87 %. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_67424e42fdbaaee580bd7675be10532a |
| identifier_str_mv | Naji, K., Gowid, S., & Ghani, S. (2023). AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques. Ain Shams Engineering Journal, 14(11), 102520. 2090-4479 11 14 2090-4495 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/50032 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | Ain Shams University |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| spelling | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniquesKhalid, NajiGowid, SamerGhani, SaudConcrete columnAutomated maintenanceDeep learningCondition monitoringFacility managementInternet of Things (IoT) and Artificial Intelligence (AI) technologies are currently replacing the traditional methods of handling buildings, infrastructure, and facilities design, control, and maintenance due to their precision and ease of use. This paper proposes a novel automated algorithm for the health monitoring of concrete column base cover degradation based on IoT and the state-of-the-art deep learning framework, Convolutional Neural Network (CNN). This technique is developed for instance detection and localization of the major types of column defects. Three deep machine learning training models; namely, Resnet-50, Googlenet, and Visual Geometry Group (VGG19), with 7 different network configurations and inputs were studied and compared for their classification performance and certainty. Despite that, a few articles consider the certainty of the CNN classification results, this work investigates the certainty and employs the classification error score as a new performance measure. The results of this study demonstrated the effectiveness of the proposed defect detection and localization algorithm as it managed to read all barcodes, localize defective columns, and binary classify the condition of the concrete covers against their surrounding objects. They also showed that the VGG19 network outperformed the other addressed network models and configurations. The VGG19 network yielded a health condition classification accuracy of 100% with an RMSE of 0.33% and a maximum classification error score of 0.87 %.Ain Shams University2023-12-03T09:31:47Z2023-10-18Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.asej.2023.102520Naji, K., Gowid, S., & Ghani, S. (2023). AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques. Ain Shams Engineering Journal, 14(11), 102520.2090-4479https://www.sciencedirect.com/science/article/pii/S2090447923004094http://hdl.handle.net/10576/5003211142090-4495enhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/500322024-07-23T15:52:10Z |
| spellingShingle | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques Khalid, Naji Concrete column Automated maintenance Deep learning Condition monitoring Facility management |
| status_str | publishedVersion |
| title | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques |
| title_full | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques |
| title_fullStr | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques |
| title_full_unstemmed | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques |
| title_short | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques |
| title_sort | AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques |
| topic | Concrete column Automated maintenance Deep learning Condition monitoring Facility management |
| url | http://dx.doi.org/10.1016/j.asej.2023.102520 https://www.sciencedirect.com/science/article/pii/S2090447923004094 http://hdl.handle.net/10576/50032 |