Measuring the in situ deformation of retaining walls by the digital image correlation method

Tung, Shih‐Heng; Weng, Meng-Chia; Shih, Ming‐Hsiang

doi:10.1016/j.enggeo.2013.09.008

Cited by 20 publications

(9 citation statements)

References 21 publications

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“…These accuracy values are comparable with other researchers' documenting accuracies, within an average range of 3.3-20.9 mm, when deploying traditional TLS techniques for infrastructure systems [42,43]. Digital Image Correlation (DIC) was implemented for measuring in situ deformation of the retaining wall, obtaining an accuracy of 3 mm, which further advocated for development of 3D model inclusion [44].…”

Section: Resultssupporting

confidence: 75%

“…Digital Image Correlation (DIC) was implemented for measuring in situ deformation of the retaining 151 wall, obtaining an accuracy of 3 mm, which further advocated for development of 3D model inclusion 152 [44]. Several photogrammetric, unmanned aerial applications indicated 0.03-0.25 m range accuracy 157 as a color range with equal value contours, between the two software's outputs for scenario L, the…”

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confidence: 99%

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Evaluation of Photogrammetry and Inclusion of Control Points: Significance for Infrastructure Monitoring

Oats

Escobar-Wolf

Oommen

2019

Data

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Structure from Motion (SfM)/Photogrammetry is a powerful mapping tool in extracting three-dimensional (3D) models from photographs. This method has been applied to a range of applications, including monitoring of infrastructure systems. This technique could potentially become a substitute, or at least a complement, for costlier approaches such as laser scanning for infrastructure monitoring. This study expands on previous investigations, which utilize photogrammetry point cloud data to measure failure mode behavior of a retaining wall model, emphasizing further robust spatial testing. In this study, a comparison of two commonly used photogrammetry software packages was implemented to assess the computing performance of the method and the significance of control points in this approach. The impact of control point selection, as part of the photogrammetric modeling processes, was also evaluated. Comparisons between the two software tools reveal similar performances in capturing quantitative changes of a retaining wall structure. Results also demonstrate that increasing the number of control points above a certain number does not, necessarily, increase 3D modeling accuracies, but, in some cases, their spatial distribution can be more critical. Furthermore, errors in model reproducibility, when compared with total station measurements, were found to be spatially correlated with the arrangement of control points.The software uses the control points to best align all images in the dataset. The photogrammetry 105 point cloud models consist of numerous sets of points in 3D, on the order of several million. This 106 magnitude of points results in point densities of tens-to hundreds-of-thousands of points per m2 107 [9]. Figure 2b provides an illustration of the point cloud model, including the flagged referenced 108 control points within the testing environment. Control points on the moving wall panels were 109 compared between scenarios, but also with total station measurements of those same points. The 110 displacements were calculated by comparing the positions of the control points along the surfaces, 111 for different scenarios. The relative space coordinate locations of the control points were determined 112 in each of these scenarios, which were compared to the ground truth total station measurements. 113A focused evaluation, considering different amounts of control points, was also performed for 114 each of the five failure mode scenarios, specifically using the Photoscan software's 3D model. Three 115 sets of data, i.e., point cloud models, were created by aligning the collected images: (1) without any 116 of the reference control points, (2) with two of the stationary control points and (3) with all four 117 stationary control points. This strategy was implemented to consider the impact of control points for 118 the model creation, varying from zero to four control points. For all three datasets, the locations of 119 the wall panel control points, and two stationary points, were captured in the point cloud model...

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Section: Resultssupporting

confidence: 75%

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confidence: 99%

Evaluation of Photogrammetry and Inclusion of Control Points: Significance for Infrastructure Monitoring

Oats

Escobar-Wolf

Oommen

2019

Data

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“…The investigated areas are located in the Laonong river basin, Ailiou river basin and Lanang river basin, Taiwan [11][12]. Geological analysis of the investigated areas indicated that they are located on the Chaochou Formation, which consists mainly of slate that is occasionally intercalated with metasandstone.…”

Section: Deformation Characteristics Of Slate Slopesmentioning

confidence: 99%

“…ACCEPTED MANUSCRIPT 12 This section presents an investigation of the influence of material deterioration on the deformation of a consequent slope. Because resin weakens after it is soaked in water, resin was employed to simulate the intensity of degradation between rock layers after rainfall.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Gravitational deformation mechanisms of slate slopes revealed by model tests and discrete element analysis

Weng

et al. 2015

Engineering Geology

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“…On that account, unsurprisingly, imaging systems provide cheap alternatives for full‐field analysis. Tung, Weng, and Shih (2013) use a standard camera with a fixed setup to repeatedly capture images of a retaining wall and to perform digital image correlation. This method is sensitive to in‐plane deformations only and comprises the challenge of setup stability for pure imaging systems (Ehrhart, 2017).…”

Section: Related Workmentioning

confidence: 99%

Processing of mobile laser scanning data for large‐scale deformation monitoring of anchored retaining structures along highways

Kalenjuk

Lienhart

Rebhan

2021

Computer aided Civil Eng

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In times of steadily increasing traffic loads and extreme weather phenomena, the safe maintenance of infrastructure poses a difficult challenge to operators, especially when a vast number of aged structures exists and fundamental data is missing. This paper addresses the demand for cost-efficient deformation monitoring of anchored retaining structures along public roads. The principal idea is to process laser scans of a motor-vehicle-based mobile mapping system with a high degree of automation. Starting with scene interpretation, our processing pipeline extracts the retaining wall from the rest of the point cloud, segments the anchored elements, and computes their deformations. This method requires, however, correcting for positioning errors to obtain accurate results. We exploit the high data redundancy of road patches and line markings for alignment. Due to the high degree of automation, computations scale to large numbers of point clouds and run in a repeatable manner. Even when traveling along highways with up to 100 km/h, we achieve repeatable accuracies for tilting and lateral displacements that compare to traditional, labor-intense surveying methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Measuring the in situ deformation of retaining walls by the digital image correlation method

Cited by 20 publications

References 21 publications

Evaluation of Photogrammetry and Inclusion of Control Points: Significance for Infrastructure Monitoring

Evaluation of Photogrammetry and Inclusion of Control Points: Significance for Infrastructure Monitoring

Gravitational deformation mechanisms of slate slopes revealed by model tests and discrete element analysis

Processing of mobile laser scanning data for large‐scale deformation monitoring of anchored retaining structures along highways

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