An innovative method for remote measurement of minimum vertical underclearance in routine bridge inspection

Riveiro, Belén; Jáuregui, David V.; Arias, Pedro; Armesto, Julia; Jiang, Ruinian

doi:10.1016/j.autcon.2012.04.008

Cited by 51 publications

(24 citation statements)

References 24 publications

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“…Along with modeling of existing bridges, Chen et al (2012) used TLS together and simple algorithms to retrieve damage information from geometric point cloud data. Terrestrial laser scanners have also been used to measure bridge clearance (Liu et al 2012;Riveiro et al, 2013;Riveiro et al 2012;Watson et al, 2012), bridge displacement during load testing (Liu et al, 2010), and to perform structural health assessments (Hess et al 2018). Vaghefi et al (2012) assessed 12 remote sensing technologies and their potential to detect various common problems in US bridges, concluding that 3D optical technologies have considerable potential for documenting surface-related defects.…”

Section: Introductionmentioning

confidence: 99%

3D reconstruction of existing concrete bridges using optical methods

Popescu

Täljsten

Blanksvärd

et al. 2019

Structure and Infrastructure Engineering

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Routine bridge inspections usually consist of visual observations. These inspections are time-consuming and subjective. There is a need to identify new inspection techniques for infrastructure that reduce traffic disturbance, and improve the efficiency and reliability of the acquired data. This study compared the performance of three different imaging technologies for the three-dimensional (3D) geometric modeling of existing structures: terrestrial laser scanning, close-range photogrammetry, and infrared scanning. Each technology was used to assess six existing concrete railway bridges. The technologies were compared in terms of geometric deviations, visualization capabilities, the level of the inspector's experience, and degree of automation. The results suggest that all methods investigated can be used to create 3D models, however, with different level of completeness. Measurements such as span length, deck widths, etc. can be extracted with good accuracy. Although promising, a full off-site inspection is currently not feasible as some areas of the bridges were difficult to capture mainly due to restricted access and narrow spaces. Measurements based on terrestrial laser scanning were closer to the reality compared to photogrammetry and infrared scanning. The study indicates the no special training is needed for photogrammetry and infrared scanning to generate a 3D geometric model.

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Section: Introductionmentioning

confidence: 99%

3D reconstruction of existing concrete bridges using optical methods

Popescu

Täljsten

Blanksvärd

et al. 2019

Structure and Infrastructure Engineering

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“…Kim and Kim (2011) proposed a digital image correlation technique to measure multi-point displacement response for civil infrastructures. Recently, an image-based technique was used to measure minimum vertical under-clearance for bridges during routine inspection (Riveiro et al ., 2012).…”

Section: Vision-based Measurement Techniques For Civil Engineering Apmentioning

confidence: 99%

Vision-based sensing for assessing and monitoring civil infrastructures

Chang

2014

Sensor Technologies for Civil Infrastructures

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“…Furthermore, photogrammetry enables both a qualitative and a quantitative evaluation approach for monitoring changes or performances, and would be advantageous for an asset management system, easily data-logging its condition over time [9]. With researchers becoming more interested in using photogrammetry, comparative experimental studies between photogrammetry and Terrestrial Laser Scanning (TLS), for 3D reconstruction of civil and geotechnical infrastructure systems, have been documented [4][5][6][14][15][16][17][18][19][20][21][22][23]. While TLS has been implemented in areas of geotechnical engineering, because of improvements in scanning speed and mobility as well as capabilities for building management [24][25][26][27], few efforts emphasize photogrammetric techniques [9].…”

Section: Introductionmentioning

confidence: 99%

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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An innovative method for remote measurement of minimum vertical underclearance in routine bridge inspection

Cited by 51 publications

References 24 publications

3D reconstruction of existing concrete bridges using optical methods

3D reconstruction of existing concrete bridges using optical methods

Vision-based sensing for assessing and monitoring civil infrastructures

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

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