Estimation of Construction Site Elevations Using Drone-Based Orthoimagery and Deep Learning

Jiang, Yuhan; Bai, Yong

doi:10.1061/(asce)co.1943-7862.0001869

Cited by 26 publications

(7 citation statements)

References 17 publications

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“…It is important to note that 3-D modeling and UAS are multi-mission technologies that could be integrated with other technologies. For instance, UAS is used as an aerial platform carrying cameras, sensors, and GPS devices (Jiang and Bai, 2020; Greenwood et al. , 2019).…”

Section: Discussionmentioning

confidence: 99%

“…A UAS is an aerial platform and typically synergized with other technology such as sensors. The synergized UAS is used as a remote sensing tool to collect inspection data of various construction elements such as bridges and earthwork activities (Jiang and Bai, 2020;Yao et al, 2019;Chen et al, 2019;Greenwood et al, 2019;Zhou et al, 2018). The position detection systems include GPS and GIS.…”

Section: E-inspection Implementationmentioning

confidence: 99%

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Content analysis of e-inspection implementation for highway infrastructure construction projects

Mohamed

Tran

2022

ECAM

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PurposeThe use of digital inspection or e-inspection of transportation projects has been proven as an efficient method over the last decade. A wide range of studies were dedicated to developing and applying e-inspection techniques and technologies. However, there is a lack of a comprehensive systematic review and content analysis of using e-inspection in highway construction and maintenance projects. The main objectives of this study were to explore the current trend and identify relevant inspection technologies and their applications for highway construction projects.Design/methodology/approachA systematic review of 172 articles from 16 high-ranked academic journals in construction engineering and management published during 2000–2021 was conducted. This process resulted in 67 relevant articles included in the detailed content analysis. The analysis involved synthesizing six main construction elements and work types, nine typical inspection activities, and 23 technologies.FindingsThe result of the analysis showed that among the six construction elements and work types, bridge and hot mix asphalt (HMA) recorded the largest share of e-inspection research. For the nine inspection activities, progress monitoring of construction operations was the highest focused area of e-inspection research. The most common e-inspection technologies are geospatial tools, 3D modeling, and unmanned aircraft systems (UASs). Camera-based inspection has existed for decades, however, has limited research development. The critical success factors in implementing e-inspection in highway projects are sharing data among different technologies, inspector training, and reducing the cost of technology purchase.Originality/valueThis study is one of the first attempts to conduct a content analysis of the e-inspection implementation for highway projects. The findings of this study expose knowledge gaps in contemporary research related to implementation barriers such as cost of purchase and operation of e-inspection technologies and transferring data between technologies.

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Section: Discussionmentioning

confidence: 99%

Section: E-inspection Implementationmentioning

confidence: 99%

Content analysis of e-inspection implementation for highway infrastructure construction projects

Mohamed

Tran

2022

ECAM

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“…CNN is used in image recognition by inserting the values from the image pixels to identify (Jiang & Bai, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…CNNs, with their capabilities related to the partial connections, sharing weights and pooling layers, can automatically capture the grid-like topology of images under fewer computations and then generate promising detection results (Cha et al , 2017). CNN is used in image recognition by inserting the values from the image pixels to identify features (Jiang and Bai, 2020).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

Elghaish

Matarneh

Talebi

et al. 2021

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Purpose The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection. Design/methodology/approach A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations. Findings The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results. Practical implications The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. Originality/value A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

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“…The distributed optical fiber sensor can continuously measure the strain of cables for more than 20 km, and hence it is recently applied as a SMART monitoring technique for various infrastructures [17][18][19]. In order to monitor displacement in wide sections at large-scale excavation construction sites, photogrammetry techniques are being introduced [20][21][22]. Research on applying thermal infrared cameras has been conducted to find local damage, such as cracks in structures [23,24]; 3D cameras have been also applied to evaluate the global movement of structures' environmental area [25].…”

Section: Introductionmentioning

confidence: 99%

Displacement Estimation Error in Laser Scanning Monitoring of Retaining Structures Considering Roughness

Seo

Zhao

Chen

2021

Sensors

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Point clouds were obtained after laser scanning of the concrete panel, SMW, and sheet pile which is most widely used in retaining structures. The surface condition of the point cloud affects the displacement calculation, and hence both local roughness and global curvature of each point cloud were analyzed using the different sizes of the kernel. The curvature of the three retaining structures was also analyzed by the azimuth angle. In this paper, artificial displacements are generated for the point clouds of 100%, 80%, 60%, 40%, and 20% of the retaining structures, and displacement and analysis errors were calculated using the C2C, C2M, and M3C2 methods. C2C method is affected by the resolution of the point cloud, and the C2M method underestimates the displacement by the location of the points in the curvature of the retaining structures. M3C2 method had the lowest error, and the optimized M3C2 parameters for analyzing the displacement were presented.

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Estimation of Construction Site Elevations Using Drone-Based Orthoimagery and Deep Learning

Cited by 26 publications

References 17 publications

Content analysis of e-inspection implementation for highway infrastructure construction projects

Content analysis of e-inspection implementation for highway infrastructure construction projects

Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

Displacement Estimation Error in Laser Scanning Monitoring of Retaining Structures Considering Roughness

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