Application of a Photogrammetric System for Monitoring Civil Engineering Structures

Han, Jung-Geun; Hong, Ki-Kwon; Kim, Sanghun

doi:10.5772/34906

Cited by 8 publications

(9 citation statements)

References 8 publications

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“…Unlike terrestrial laser scanning, which is an active technique, photogrammetry is an image-based passive method, which provides high resolution and accurate datasets, being at the same time a low-cost solution characterized by high level of automation. These features made close-range photogrammetry very flexible and thus, widely used measuring method successfully applied in a variety of fields including civil engineering [64,65], forestry [66,67], geology [68,69], archaeology [70,71], and cultural heritage documentation [72,73].…”

Section: The Structure-from-motion/close-range Photogrammetrymentioning

confidence: 99%

The Quality Assessment of Different Geolocalisation Methods for a Sensor System to Monitor Structural Health of Monumental Objects

Markiewicz

Łapiński

Kot

et al. 2020

Sensors

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Cultural heritage objects are affected by a wide range of factors causing their deterioration and decay over time such as ground deformations, changes in hydrographic conditions, vibrations or excess of moisture, which can cause scratches and cracks formation in the case of historic buildings. The electromagnetic spectroscopy has been widely used for non-destructive structural health monitoring of concrete structures. However, the limitation of this technology is a lack of geolocalisation in the space for multispectral architectural documentation. The aim of this study is to examine different geolocalisation methods in order to determine the position of the sensor system, which will then allow to georeference the results of measurements performed by this device and apply corrections to the sensor response, which is a crucial element required for further data processing related to the object structure and its features. The classical surveying, terrestrial laser scanning (TLS), and Structure-from-Motion (SfM) photogrammetry methods were used in this investigation at three test sites. The methods were reviewed and investigated. The results indicated that TLS technique should be applied for simple structures and plain textures, while the SfM technique should be used for marble-based and other translucent or semi-translucent structures in order to achieve the highest accuracy for geolocalisation of the proposed sensor system.

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Section: The Structure-from-motion/close-range Photogrammetrymentioning

confidence: 99%

The Quality Assessment of Different Geolocalisation Methods for a Sensor System to Monitor Structural Health of Monumental Objects

Markiewicz

Łapiński

Kot

et al. 2020

Sensors

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“…Structure from Motion (SfM)/Digital Photogrammetry provides the ability to build three-dimensional (3D) models from two-dimensional (2D) images [1,2]. Comparing 3D models built from images acquired at different times, can then be used to analyze deformation or displacement of an object over that time frame [3]. In this regard, imaging technologies can also enable readily-accessible processed spatial data for assessment of infrastructure systems [4][5][6][7].…”

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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“…Very few efforts have been conducted to investigate the application of photogrammetry for retaining wall infrastructure systems providing a more budget-friendly evaluation for geotechnical assets. Integrating non-contact photogrammetry techniques is advantageous with widely available quality digital single lens reflex (DSLR) cameras and advancing computer processing capabilities with open source software options [16,[34][35][36][37][38][39]. Applying this to the vast geotechnical infrastructure to not only provide automatic easy data-logged qualitative documentation of condition, but also quantitative changes, can further advance retaining wall asset management [10,13,14,16].…”

Section: Introductionmentioning

confidence: 99%

“…Recent advancements in lower-cost optical cameras, image processing, and three-dimensional (3D) modeling have enabled photogrammetry to three-dimensionally reconstruct objects from digital images, for civil or transportation structure applications [16,[32][33][34][35][36]. Photogrammetry provides the ability to obtain quantitative measurements from 3D models created from quality easily documented two-dimensional (2D) images, and has been used for assessing the condition of transportation assets [21,37].…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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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Application of a Photogrammetric System for Monitoring Civil Engineering Structures

Cited by 8 publications

References 8 publications

The Quality Assessment of Different Geolocalisation Methods for a Sensor System to Monitor Structural Health of Monumental Objects

The Quality Assessment of Different Geolocalisation Methods for a Sensor System to Monitor Structural Health of Monumental Objects

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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