Civil Infrastructure Health Monitoring and Management Using Unmanned Aerial Systems

Vidyadharan, Akash; Carter, Tyler; Ceylan, Hali̇l; Bloebaum, Christina; Gopalakrishnan, Kasthurirangan; Kim, Sunghwan

doi:10.1061/9780784480946.019

Cited by 3 publications

(5 citation statements)

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“…In recent years, the ever-increasing use of UAVs has been remarkable (Paszotta et al, 2017). UAVs provide applications including the delivery of goods, disaster management, search and rescue for missing persons, security issues, land surveying, construction, and infrastructure inspection (Keaveney and McGetrick, 2020;Gopalakrishnan et al, 2017). UAV applications are up-and-coming, especially in the civil infrastructure (Lattanzi and Miller, 2017;Fan and Ala Saadeghvaziri, 2019).…”

Section: General Framework Of Uav Approachesmentioning

confidence: 99%

“…In fact, due to their capability to visually approach vulnerable and inaccessible components of infrastructure, UAVs have been efficiently integrated into infrastructure monitoring practices (Duque et al, 2018). Consequently, inspections become extremely easy, more secure, and cost-effective (Vidyadharan et al, 2017). Therefore, nowadays, many engineers are using UAVs to monitor the integrity of various infrastructures (e.g.…”

Section: General Framework Of Uav Approachesmentioning

confidence: 99%

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Unmanned Aerial Vehicles: A Survey on Monitoring Advancements for Port Infrastructure Applications

2023

EPPM-Journal

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Ports play a significant role in the economic and social activities of the areas they serve. However, the adverse effects of the harsh and highly corrosive environments that ports operate in, along with phenomena related to the climate crisis and insufficient maintenance practices, increase port infrastructure's susceptibility to rapid degradation. This degradation can be aesthetic, functional, or structural and often leads to loss of serviceability at either a component (local and national) level or a global level. Moreover, many port structures have reached the limit of their lifetime, thus introducing the concept of extending their lifespan as a financially attractive alternative to constructing new facilities. Therefore, port operators pursue monitoring the structural integrity of the structures through an inspection plan aiming to reduce their maintenance and rehabilitation costs and ensure the safety of both the port infrastructure itself and human lives. Optimising monitoring approaches to enable damage detection and condition assessment can be achieved through the employment of Non-Destructive Testing (NDT) and Remote Sensing (RS) techniques. The current surge of using Unmanned Aerial Vehicles (UAVs) for both RS and NDT monitoring practices has proved promising since UAVs provide improved accessibility, increased inspection speed, and reduced safety hazards. The present paper is focused on investigating and evaluating the recent advances in UAV-driven port infrastructure monitoring. For this purpose, a comprehensive review of UAVs applications combined with NDT Infrared Thermography (IRT) and Ground Penetrating Radar (GPR) or RS Close Range Photogrammetry (CRP) and Light Detection and Ranging (LiDAR) is carried out to assess the potential and the limitations of the UAV-based monitoring approaches. This research provides valuable information on enhancing management strategies by upgrading port monitoring practices.

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Section: General Framework Of Uav Approachesmentioning

confidence: 99%

Section: General Framework Of Uav Approachesmentioning

confidence: 99%

Unmanned Aerial Vehicles: A Survey on Monitoring Advancements for Port Infrastructure Applications

2023

EPPM-Journal

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“…Rotorcraft are the most commonly used sUAS platforms for data collection because of their highly adaptable launch and recovery systems that offer the capability for hovering and collecting data from a variety of positions, and often they are also less expensive and more widely available than the other two types of platforms. Many recent studies have demonstrated the potential of rotorcraft in traffic monitoring, railroad inspection, bridge-defect detection, and pavement inspection ( 6 , 7 , 12 , 14 ).…”

Section: Suas Platforms and Sensorsmentioning

confidence: 99%

“…Among many civil and transportation infrastructure health-monitoring efforts, the use of sUAS has also attracted attention as an alternative for performing cost-effective and efficient pavement inspection ( 12 ). In the United States, 49 Departments of Transportation (DOTs) are presently using sUAS in various applications, and some have employed highly skilled personnel to manage sUAS operations ( 13 ).…”

mentioning

confidence: 99%

Evaluation of Small Uncrewed Aircraft Systems Data in Airfield Pavement Crack Detection and Rating

Sourav

Mahedi

Ceylan

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Current practice for airport Pavement Management Program (PMP) inspection relies on visual surveys and manual interpretation of reports and sketches prepared by inspectors in the field to quantify pavement conditions using the Pavement Condition Index method set forth in ASTM D5340. In recent years, several attempts have been made, both by the industry and by airport operators, to use small Uncrewed (Unpersonned/Unmanned) Aircraft Systems (sUAS), or “drones,” to conduct various types of imaging and inspection of airport pavements. As part of a comprehensive study on the use of such sUAS to evaluate airfield pavement conditions, the objectives of this study were to assess the performance of various sUAS platforms and sensors in detecting and rating a subset of crack-based pavement distresses and to evaluate the use of a combination of different sUAS datasets to complement current methods used to support airport PMP. Two airports in Michigan were selected for sUAS data collection, and five sUAS platforms equipped with eight different sensors were flown at these airports at different altitudes to collect red, green, and blue (RGB) optical and thermal data at different resolutions. RGB orthophotos, digital elevation models, and thermal images were visually analyzed to study their usefulness in detecting and rating longitudinal and transverse cracks in flexible/asphalt pavements and longitudinal, transverse, and diagonal cracks, corner breaks, and durability cracks in rigid/concrete pavements. This study demonstrated the capability of using sUAS data in detecting and rating multiple crack-related distresses in both flexible and rigid airfield pavement systems.

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“…I nspec t i ng civ i l a nd t ra nspor tat ion infrastructure manually is an expensive and relatively dangerous task. The use of UAV for infrastructure inspection purposes can make this task significantly easier, safer and cost effective (Vidyadharan et al, 2017). Many times a lift or a crane is used to allow manual access to inspect the 'hard to reach' locations where damages may have occurred.…”

Section: Introductionmentioning

confidence: 99%

Crack Damage Detection in Unmanned Aerial Vehicle Images of Civil Infrastructure Using Pre-Trained Deep Learning Model

2018

IJTTE

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Abstract:The field of computer vision based civil infrastructure defect detection is constantly evolving with steady advances being made in sensing technologies, hardware, and image processing techniques. Although a number of image processing techniques have been proposed over the years with varying degrees of success, real-world situations (e.g., lack of background illumination, shadow changes) pose a significant challenge to the wide adoption of such techniques, especially for routine analysis. With the emerging application of Unmanned Aerial Vehicles (UAVs) or drones for civil infrastructure inspection and condition monitoring, an added level of complexity is introduced in vision-based crack detection as the process of acquiring images using UAVs is not yet standardized resulting in images of widely varying sizes, resolutions, blurriness, etc. In recent years, Deep Learning, a generalized form of Deep Neural Network (DNN) algorithms that can learn very complex mappings between inputs and outputs directly from the data, has achieved huge success in diverse fields such as automatic speech recognition, image recognition, Natural Language Processing (NLP), drug and materials discovery, etc. However, the large number of hidden neurons and layers used in DNNs result in computationally-intensive matrix and vector computations involving millions of parameters, requiring the use of high-performance computing systems. Also, it is practically impossible to get labeled "big data" samples in many domains to be able to train an entire DNN from scratch. In such situations, the use of a pre-trained deep learning model and fine-tuning it to the novel task at hand with smaller datasets, has shown to be successful across domains. In this paper, we propose the use of pre-trained deep learning models with transfer learning for crack damage detection in UAV images of civil infrastructure. The robustness of the proposed approach is tested on a small set of real-world, complex UAV-sourced infrastructure images not used during training and validation. The results show that the proposed method can rapidly and easily achieve up to 90% accuracy in finding cracks in realistic situations without any augmentation and preprocessing.

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Civil Infrastructure Health Monitoring and Management Using Unmanned Aerial Systems

Cited by 3 publications

References 5 publications

Unmanned Aerial Vehicles: A Survey on Monitoring Advancements for Port Infrastructure Applications

Unmanned Aerial Vehicles: A Survey on Monitoring Advancements for Port Infrastructure Applications

Evaluation of Small Uncrewed Aircraft Systems Data in Airfield Pavement Crack Detection and Rating

Crack Damage Detection in Unmanned Aerial Vehicle Images of Civil Infrastructure Using Pre-Trained Deep Learning Model

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