Development of a Cognitive Digital Twin for Pavement Infrastructure Health Monitoring
A road network is the key foundation of any nation’s critical infrastructure. Pavements represent one of the longest-living structures, having a post-construction life of 20–40 years. Currently, most attempts at maintaining and repairing these structures are performed in a reactive and traditional f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/7/9/113 |
| _version_ | 1797486897407197184 |
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| author | Cristobal Sierra Shuva Paul Akhlaqur Rahman Ambarish Kulkarni |
| author_facet | Cristobal Sierra Shuva Paul Akhlaqur Rahman Ambarish Kulkarni |
| author_sort | Cristobal Sierra |
| collection | DOAJ |
| description | A road network is the key foundation of any nation’s critical infrastructure. Pavements represent one of the longest-living structures, having a post-construction life of 20–40 years. Currently, most attempts at maintaining and repairing these structures are performed in a reactive and traditional fashion. Recent advances in technology and research have proposed the implementation of costly measures and time-intensive techniques. This research presents a novel automated approach to develop a cognitive twin of a pavement structure by implementing advanced modelling and machine learning techniques from unmanned aerial vehicle (e.g., drone) acquired data. The research established how the twin is initially developed and subsequently capable of detecting current damage on the pavement structure. The proposed method is also compared to the traditional approach of evaluating pavement condition as well as the more advanced method of employing a specialized diagnosis vehicle. This study demonstrated an efficiency enhancement of maintaining pavement infrastructure. |
| first_indexed | 2024-03-09T23:39:53Z |
| format | Article |
| id | doaj.art-6091ec7c3125432681a06456e572dcbb |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-09T23:39:53Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-6091ec7c3125432681a06456e572dcbb2023-11-23T16:53:42ZengMDPI AGInfrastructures2412-38112022-08-017911310.3390/infrastructures7090113Development of a Cognitive Digital Twin for Pavement Infrastructure Health MonitoringCristobal Sierra0Shuva Paul1Akhlaqur Rahman2Ambarish Kulkarni3Faculty of Science, Engineering and Technology, Swinburne University of Technology, H38 John Street, Hawthorn, VIC 3122, AustraliaSchool of Electrical & Computer Engineering (ECE), Georgia Institute of Technology, Atlanta, GA 30332-0250, USASchool of Industrial Automation Engineering, Engineering Institute of Technology, Melbourne, VIC 3000, AustraliaFaculty of Science, Engineering and Technology, Swinburne University of Technology, H38 John Street, Hawthorn, VIC 3122, AustraliaA road network is the key foundation of any nation’s critical infrastructure. Pavements represent one of the longest-living structures, having a post-construction life of 20–40 years. Currently, most attempts at maintaining and repairing these structures are performed in a reactive and traditional fashion. Recent advances in technology and research have proposed the implementation of costly measures and time-intensive techniques. This research presents a novel automated approach to develop a cognitive twin of a pavement structure by implementing advanced modelling and machine learning techniques from unmanned aerial vehicle (e.g., drone) acquired data. The research established how the twin is initially developed and subsequently capable of detecting current damage on the pavement structure. The proposed method is also compared to the traditional approach of evaluating pavement condition as well as the more advanced method of employing a specialized diagnosis vehicle. This study demonstrated an efficiency enhancement of maintaining pavement infrastructure.https://www.mdpi.com/2412-3811/7/9/113pavementreality modelcognitive twinUAVinfrastructuremaintenance |
| spellingShingle | Cristobal Sierra Shuva Paul Akhlaqur Rahman Ambarish Kulkarni Development of a Cognitive Digital Twin for Pavement Infrastructure Health Monitoring Infrastructures pavement reality model cognitive twin UAV infrastructure maintenance |
| title | Development of a Cognitive Digital Twin for Pavement Infrastructure Health Monitoring |
| title_full | Development of a Cognitive Digital Twin for Pavement Infrastructure Health Monitoring |
| title_fullStr | Development of a Cognitive Digital Twin for Pavement Infrastructure Health Monitoring |
| title_full_unstemmed | Development of a Cognitive Digital Twin for Pavement Infrastructure Health Monitoring |
| title_short | Development of a Cognitive Digital Twin for Pavement Infrastructure Health Monitoring |
| title_sort | development of a cognitive digital twin for pavement infrastructure health monitoring |
| topic | pavement reality model cognitive twin UAV infrastructure maintenance |
| url | https://www.mdpi.com/2412-3811/7/9/113 |
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