UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Choi, Daegyun; Bell, W. H.; Kim, Dong-Hoon; Kim, Jichul

doi:10.3390/s21082650

Cited by 27 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A realtime crack geometric size generation method was developed by Li and Zhao using an encoder-decoder network [35]. Other researchers, such as Choi et al [36] and Liu et al [37], continued to improve the practice of the AI-based crack measurement method by adding angle adaptability and portability. Since the images in the dataset built for this work were taken through bridge inspectors' daily work and, thus, were not specifically taken for crack assessment research using computer vision, the authors elected not to re-develop advanced crack size measurement methods.…”

Section: Design Of the Ai-based Workflowmentioning

confidence: 99%

Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence

Zhang

Wogen

Liu

et al. 2023

Sensors

View full text Add to dashboard Cite

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state’s databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities.

show abstract

Section: Design Of the Ai-based Workflowmentioning

confidence: 99%

Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence

Zhang

Wogen

Liu

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Eschmann et al (2012) mentioned that structure monitoring especially on crack assessment of stone-based or concrete structure can be provided via conventional monitoring. Choi et al (2021) studied the documentation and analysis of historical buildings to detect damages, such as biological changes or moistures on monument surfaces. Fan et al (2018) investigated the building pathology to characterise and define building defects.…”

Section: Introductionmentioning

confidence: 99%

Possibility the use of UAV platform for building’s crack length measurement

Rahman¹,

Tahar

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

This study uses Unmanned Aerial Vehicle (UAV) as a platform to inspect and monitor a building. It focuses on the inspection of defects in buildings, especially cracks. The aim of this study is to investigate cracks on a building by using UAV photogrammetry. The building was used as an object for this study because there are demands from the building maintenance team to assess cracks on the building in an effective way, where it can help to provide reliable crack information. The selected building for this study was an old apartment building with cracks, making it appropriate for this study. There were several steps and procedure used to capture the image of cracks on the building. From the image captured by using UAV, measurement of the cracks can be identified by using a software. There were also different approaches for the output. First is the 3D model of the building and second is the measurement of cracks. The 3D model of the building was created by using Agisoft Photoscan software for the purpose of getting the overview of building dimension. The measurement of cracks was processed by using PhotoModeler software. The accuracy achieved for this study was ±0.8cm. The result showed that UAV photogrammetry can help the current surveying work, especially on building maintenance. Besides, it can capture the crack’s images from certain altitude. The authors used UAV for smart building monitoring assessment, which was less considered in the past.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The kinematics of the STS transitions have been measured in many, typically laboratory-bound, studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. For example, a smartphone acceleration sensor has been used to quantify STS transition and was found to be valid [ 6 , 7 ]. The widely used 5x STS test [ 8 , 9 , 10 ], 10x STS test [ 11 ] and sit-to-walk [ 12 ] movement kinematics of the different phases have also been interpreted successfully with body-fixed gyroscope and/or accelerometer sensors.…”

Section: Introductionmentioning

confidence: 99%

Day-to-Day Variability and Year-to-Year Reproducibility of Accelerometer-Measured Free-Living Sit-to-Stand Transitions Volume and Intensity among Community-Dwelling Older Adults

Löppönen

Karavirta

Portegijs

et al. 2021

Sensors

View full text Add to dashboard Cite

(1) Background: The purpose of this study was to evaluate the day-to-day variability and year-to-year reproducibility of an accelerometer-based algorithm for sit-to-stand (STS) transitions in a free-living environment among community-dwelling older adults. (2) Methods: Free-living thigh-worn accelerometry was recorded for three to seven days in 86 (women n = 55) community-dwelling older adults, on two occasions separated by one year, to evaluate the long-term consistency of free-living behavior. (3) Results: Year-to-year intraclass correlation coefficients (ICC) for the number of STS transitions were 0.79 (95% confidence interval, 0.70–0.86, p < 0.001), for mean angular velocity—0.81 (95% ci, 0.72–0.87, p < 0.001), and maximal angular velocity—0.73 (95% ci, 0.61–0.82, p < 0.001), respectively. Day-to-day ICCs were 0.63–0.72 for number of STS transitions (95% ci, 0.49–0.81, p < 0.001) and for mean angular velocity—0.75–0.80 (95% ci, 0.64–0.87, p < 0.001). Minimum detectable change (MDC) was 20.1 transitions/day for volume, 9.7°/s for mean intensity, and 31.7°/s for maximal intensity. (4) Conclusions: The volume and intensity of STS transitions monitored by a thigh-worn accelerometer and a sit-to-stand transitions algorithm are reproducible from day to day and year to year. The accelerometer can be used to reliably study STS transitions in free-living environments, which could add value to identifying individuals at increased risk for functional disability.

show abstract

UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Cited by 27 publications

References 33 publications

Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence

Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence

Possibility the use of UAV platform for building’s crack length measurement

Day-to-Day Variability and Year-to-Year Reproducibility of Accelerometer-Measured Free-Living Sit-to-Stand Transitions Volume and Intensity among Community-Dwelling Older Adults

Contact Info

Product

Resources

About