Terrestrial Laser Scanning-Based Structural Damage Assessment

Olsen, Michael J.; Kuester, Falko; Chang, Barbara; Hutchinson, Tara C.

doi:10.1061/(asce)cp.1943-5487.0000028

Cited by 249 publications

(149 citation statements)

References 6 publications

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“…In this section we describe the protocol for scan alignment applied to the nearcontinuous dataset. As discussed by Abellán et al (2014), aligning point clouds can be undertaken using common surveyed and modelled targets combined with measured global coordinates (Teza et al, 2007;Olsen et al, 2009), featurebased registration based on the planarity and curvature of surfaces (e.g. Besl and Jain, 1988;Belton and Lichti, 2006;Rabbani et al, 2006), and point-to-point and point-to-surface methods, which use iterative closest point (ICP) alignment to progressively reduce the distance between two clouds (Besl and McKay, 1992;Chen and Medioni, 1992;Zhang, 1994).…”

Section: Precise Alignmentmentioning

confidence: 99%

Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency

et al. 2018

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Abstract. We present a monitoring technique tailored to analysing change from near-continuously collected, high-resolution 3-D data. Our aim is to fully characterise geomorphological change typified by an event magnitude-frequency relationship that adheres to an inverse power law or similar. While recent advances in monitoring have enabled changes in volume across more than 7 orders of magnitude to be captured, event frequency is commonly assumed to be interchangeable with the time-averaged event numbers between successive surveys. Where events coincide, or coalesce, or where the mechanisms driving change are not spatially independent, apparent event frequency must be partially determined by survey interval.The data reported have been obtained from a permanently installed terrestrial laser scanner, which permits an increased frequency of surveys. Surveying from a single position raises challenges, given the single viewpoint onto a complex surface and the need for computational efficiency associated with handling a large time series of 3-D data. A workflow is presented that optimises the detection of change by filtering and aligning scans to improve repeatability. An adaptation of the M3C2 algorithm is used to detect 3-D change to overcome data inconsistencies between scans. Individual rockfall geometries are then extracted and the associated volumetric errors modelled. The utility of this approach is demonstrated using a dataset of ∼ 9 × 10 3 surveys acquired at ∼ 1 h intervals over 10 months. The magnitude-frequency distribution of rockfall volumes generated is shown to be sensitive to monitoring frequency. Using a 1 h interval between surveys, rather than 30 days, the volume contribution from small (< 0.1 m 3 ) rockfalls increases from 67 to 98 % of the total, and the number of individual rockfalls observed increases by over 3 orders of magnitude. High-frequency monitoring therefore holds considerable implications for magnitude-frequency derivatives, such as hazard return intervals and erosion rates. As such, while high-frequency monitoring has potential to describe short-term controls on geomorphological change and more realistic magnitude-frequency relationships, the assessment of longer-term erosion rates may be more suited to less-frequent data collection with lower accumulative errors.

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Section: Precise Alignmentmentioning

confidence: 99%

Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency

et al. 2018

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“…It creates mesh or triangulated models of the reference point cloud, which are used to measure the orthogonal distances for each point in the compared cloud (Cignoni and Rocchini 1998, see also Monserrat andCrosetto 2008 andOlsen et al 2010 for recent reviews). This procedure is most suited with sub-planar objects, due to a tendency to smooth out details possibly relevant in local properties evaluation.…”

Section: Point Clouds Comparison Algorithmsmentioning

confidence: 99%

Structure from motion (SfM) processing of UAV images and combination with terrestrial laser scanning, applied for a 3D-documentation in a hazardous situation

Zaragoza

Crepaldi

Piemonte

et al. 2017

Geomatics, Natural Hazards and Risk

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The integration of Terrestrial Laser Scanning (TLS) and Structure from Motion and MultiView Stereo techniques allows to obtain comprehensive models of complex objects by using each technique in contexts presenting the optimal operating conditions, as widely reported in bibliographic references. A different situation occurs for emergency surveys. In this case, time and security act as constraining factors, requiring the use of these techniques also in the most unfavourable conditions. In the case of photogrammetry, these include areas where the object surfaces are not perpendicular to the camera axis, and in the case of TLS, they include areas where laser beams are almost tangent to the surveyed object surfaces. These situations are anyway necessary for safely carrying out these surveys in the minimum possible time and cost. Although this kind of survey results locally in lower precision levels than those obtainable by these techniques in ideal conditions, it entails the possibility of obtaining complete models, e.g. including vertical external walls in inaccessible buildings, with controlled precision.

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“…Gridded data sets can be compared to produce a DEM of difference (DoD), which highlights areas of loss or accumulation [10,15,[23][24][25][26]. Other methods include C2M, where surface models are created from point cloud data sets via meshing or triangulation and compared with subsequently gathered point data sets (e.g., Abellán et al [17]; Abellán et al [18]; and Olsen et al [27]). However, isolating regions of interest and creating DEMs or surface models is time-consuming and requires interpolation [22].…”

Section: Change Detection Via Tlsmentioning

confidence: 99%

“…C2M Analyses C2M comparisons have been used by many workers to detect a change between sequentially gathered point clouds [17,18,27]. Consider two sequential point clouds of the same feature, C b and C a (Figure 1(A)).…”

Section: Change Detection Via Tlsmentioning

confidence: 99%

Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska

2013

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Terrestrial laser scanners (TLS) allow large and complex landforms to be rapidly surveyed at previously unattainable point densities. Many change detection methods have been employed to make use of these rich data sets, including cloud to mesh (C2M) comparisons and Multiscale Model to Model Cloud Comparison (M3C2). Rather than use simulated point cloud data, we utilized a 58 scan TLS survey data set of the Selawik retrogressive thaw slump (RTS) to compare C2M and M3C2. The Selawik RTS is a rapidly evolving permafrost degradation feature in northwest Alaska that presents challenging survey conditions and a unique opportunity to compare change detection methods in a difficult surveying environment. Additionally, this study considers several error analysis techniques, investigates the spatial variability of topographic change across the feature and explores visualization techniques that enable the analysis of this spatiotemporal data set. C2M reports a higher magnitude of topographic change over short periods of time (∼12 h) and reports a lower magnitude of topographic change over long periods of time (∼four weeks) when compared to M3C2. We found that M3C2 provides a better accounting of the sources of uncertainty in TLS change detection than C2M, because it considers the uncertainty due to surface roughness and scan registration. We also found that localized areas of the RTS do not always approximate the overall retreat of the feature and show considerable spatial variability during inclement weather; however, when averaged together, the spatial subsets approximate the retreat of the entire feature. New data visualization techniques are explored to leverage temporally and spatially continuous data sets. Spatially binning the data into vertical strips Remote Sens. 2013, 5 2814 along the headwall reduced the spatial complexity of the data and revealed spatiotemporal patterns of change.

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Terrestrial Laser Scanning-Based Structural Damage Assessment

Cited by 249 publications

References 6 publications

Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency

Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency

Structure from motion (SfM) processing of UAV images and combination with terrestrial laser scanning, applied for a 3D-documentation in a hazardous situation

Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska

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