Retaining Wall Field Condition Inspection, Rating Analysis, and Condition Assessment

Butler, Cedrick J.; Gabr, M. A.; Rasdorf, William; Findley, Daniel J.; Chang, Jeffrey C; Hammit, Britton E.

doi:10.1061/(asce)cf.1943-5509.0000785

Cited by 17 publications

(15 citation statements)

References 3 publications

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“…approach for evaluating the condition of ERSs, many of the existing condition rating systems in use today vary from agency to agency. For the purpose of demonstration, this study used the two-part rating system proposed by Butler et al to determine an ERS condition rating and illustrate how this numerical rating can be used in the evaluation of risk (6).…”

Section: Framework For Defining Asset Features To Monitor and Assess Earth-retaining Structuresmentioning

confidence: 99%

“…Butler et al suggested a transition from a rating system that provides only a single overall rating, which could potentially mask or overlook critical distresses within an ERS and its elements (6). In the Butler et al study, the following potential benefits of the use of a two-part rating approach were highlighted:…”

Section: Framework For Defining Asset Features To Monitor and Assess Earth-retaining Structuresmentioning

confidence: 99%

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Framework for Defining Asset Features to Monitor and Assess Earth-Retaining Structures

Rasdorf

Butler

Findley

et al. 2016

Transportation Research Record: Journal of the Transportation R

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Permanent earth-retaining structures (ERSs), including retaining walls, have been systematically managed, inventoried, and assessed by several government organizations, including the City of Cincinnati, Ohio; the Oregon and Pennsylvania Departments of Transportation; and the Central Federal Lands Highway Division of FHWA (for the National Park Service). For a comprehensive ERS asset management program, a careful evaluation of the structural health, criticality, and risk of ERSs is essential for proper management (preservation, rehabilitation, or replacement) of these aging assets. However, many transportation agencies struggle with the evaluation and incorporation of risk in their ERS management programs. This study addressed the development of a framework that would evaluate risk and would define its relationship to ERS condition (likelihood of failure) and ERS criticality (consequence of failure). A methodology for relating risk to routine inspection cycles for ERSs and remedial actions for those in distress is proposed.

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Section: Framework For Defining Asset Features To Monitor and Assess Earth-retaining Structuresmentioning

confidence: 99%

Section: Framework For Defining Asset Features To Monitor and Assess Earth-retaining Structuresmentioning

confidence: 99%

Framework for Defining Asset Features to Monitor and Assess Earth-Retaining Structures

Rasdorf

Butler

Findley

et al. 2016

Transportation Research Record: Journal of the Transportation R

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“…Moreover, ensuring structural safety for geotechnical applications is a critical component for transportation asset management systems [8,9]. The demand for an effective asset management protocol is growing for geotechnical infrastructure, such as retaining walls, for improving operations and enhancing safety at a minimal cost, while enabling conformity assessment results for structures [10][11][12][13].…”

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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“…Asset management is receiving greater attention in geotechnical infrastructure to improve operations, enhance safety, and minimize costs with the inclusion of innovative assessment methods by incorporating a performance-based approach [10][11][12][13]. However, challenges, such as inadequacies of the current methods to effectively assess the condition of retaining walls and identifying the needs for preservation of physical transportation assets, have been acknowledged [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Current methods for retaining wall condition assessment includes mostly qualitative field inspections to evaluate wall elements, which is subjective and could incur defective documentation and result in overlooking critical safety problems [7,11,15]. Various in-contact devices, such as global positioning systems (GPS), tiltmeters, and total station surveying devices, have been used to measure deformation or slope movement of structures, such as retaining walls, but can be expensive, time-consuming, limited by physical or traffic accessibilities, or have poor accuracy in capturing real-time movements [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Application of Photogrammetry for Retaining Wall Assessment

2017

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Abstract:Retaining walls are critical geotechnical assets and their performance needs to be monitored in accordance to transportation asset management principles. Current practices for retaining wall monitoring consist mostly of qualitative approaches that provide limited engineering information or the methods include traditional geodetic surveying, which may provide high accuracy and reliability, but is costly and time-consuming. This study focuses on evaluating failure modes of a 2.43 m × 2.43 m retaining wall model using three-dimensional (3D) photogrammetry as a cost-effective quantitative alternative for retaining wall monitoring. As a remote sensing technique, photogrammetry integrates images collected from a camera and creates a 3D model from the measured data points commonly referred to as a point cloud. The results from this photogrammetric approach were compared to ground control points surveyed with a total station. The analysis indicates that the accuracy of the displacement measurements between the traditional total station survey and photogrammetry were within 1-3 cm. The results are encouraging for the adoption of photogrammetry as a cost-effective monitoring tool for the observation of spatial changes and failure modes for retaining wall condition assessment.

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Retaining Wall Field Condition Inspection, Rating Analysis, and Condition Assessment

Cited by 17 publications

References 3 publications

Framework for Defining Asset Features to Monitor and Assess Earth-Retaining Structures

Framework for Defining Asset Features to Monitor and Assess Earth-Retaining Structures

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

A Novel Application of Photogrammetry for Retaining Wall Assessment

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