Laser Scanning Technology for Bridge Monitoring

Chen, Shen-En

doi:10.5772/32794

Cited by 21 publications

(11 citation statements)

References 27 publications

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“…Furthermore, the extension to the classification of other railway assets will be considered as well, along with a more depth analysis of the influence of the point density on the classification results. Finally, an extension to the analysis of buildings and structures close to the railway will also be considered (Park et al, 2007, Chen, 2012, Bitelli et al, 2004, Boreggio et al, 2018.…”

Section: Discussionmentioning

confidence: 99%

Classification of Railway Assets in Mobile Mapping Point Clouds

Corongiu

Masiero

Tucci

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Nowadays, mobile mapping systems are widely used to quickly collect reliable geospatial information of relatively large areas: thanks to such characteristics, the number of applications and fields exploiting their usage is continuously increasing. Among such possible applications, mobile mapping systems have been recently considered also by railway system managers to quickly produce and update a database of the geospatial features of such system, also called assets. Despite several vehicles, devices and acquisition methods can be considered for the data collection of the railway system, the predominant one is probably that based on the use of a mobile mapping system mounted on a train, which moves all along the railway tracks, enabling the 3D reproduction of the entire railway track area.Given the large amount of data collected by such mobile mapping, automatic procedures have to be used to speed up the process of extracting the spatial information of interest, i.e. assets positions and characteristics.This paper considers the problem of extracting such information for what concerns cantilever and portal masts, by exploiting a mixed approach. First, a set of candidate areas are extracted and pre-processed by considering certain of their geometric characteristics, mainly extracted by using eigenvalues of the covariance matrix of a point neighborhood. Then, a 3D modified Fisher vector-deep learning neural net is used to classify the candidates. Tests on such approach are conducted in two areas of the Italian railway system.

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Section: Discussionmentioning

confidence: 99%

Classification of Railway Assets in Mobile Mapping Point Clouds

Corongiu

Masiero

Tucci

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…To achieve sufficient accuracy, these operations require a good discretization, which, for its implementation, may need numerous sensors or sophisticated ones like laser-based system, 23 roved sensors, 13 or (thermal) imaging techniques.…”

Section: Discussionmentioning

confidence: 99%

Rayleigh’s quotient–based damage detection algorithm: Theoretical concepts, computational techniques, and field implementation strategies

Wandji

2017

Structural Health Monitoring

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Rayleigh's quotient-based damage detection algorithm: Theoretical concepts, computational techniques, and field implementation strategies NJOMO Abstract This article proposes a Rayleigh's quotient-based damage detection algorithm. It aims at efficiently revealing nascent structural changes on a given structure with the capability to differentiate between an actual damage and a change in operational conditions. The first three damage detection levels are targeted: existence, location, and severity. The proposed algorithm is analytically developed from the dynamics theory and the virtual energy principle. Some computational techniques are proposed for carrying out computations, including discretization, integration, derivation, and suitable optimization methods. Field implementation strategies are also considered for the purpose of online damage monitoring. In order to prove the efficiency of this strategy, one experimental and three numerical case studies were conducted. The proposed algorithm successfully detected the damage in all simulated cases and estimated the damage severity with acceptable accuracy. The conclusion is that the proposed algorithm was able to efficiently detect damage appearance in a range of structures for various damage levels and locations, and under different operational conditions.

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“…The reference planes were fitted based on the point cloud coordinates of each tested area. To determine the optimum reference plane (π) of the analysed area, an algorithm based on the Mean Sum Error (MSE) presented by Chen [63] was used. Then, the distances d i of all points from reference plane π are calculated.…”

Section: Radiometric Analysis Of the Tls Point Cloudmentioning

confidence: 99%

Comparison of Time-of-Flight and Phase-Shift TLS Intensity Data for the Diagnostics Measurements of Buildings

Suchocki

2020

Materials

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In recent years, the terrestrial laser scanning system (TLS) has become one of the most popular remote and nondestructive testing (NDT) methods for diagnostic measurements of buildings and structures as well as for the assessment of architectural heritage. Apart from 3D coordinates, the power of a laser beam backscattered from the scanned object can be captured by TLS. The radiometric information of the point cloud, called “intensity”, can provide information about changes in the physio–chemical properties of the scanned surface. This intensity can be effectively used to detect defects in the surfaces of walls, such as cracks and cavities, moisture, biodeterioration (mosses and lichens) or weathered parts of the wall. Manufacturers of TLS mainly use two different principles for distance measurement, time-of-flight (TOF) and phase-shift (PS). The power of energy in both types of rangefinders might be absorbed or reflected in a slightly different way and provide more or less detailed radiometric point cloud information. The main aim of this investigation is to compare TOF and PS scanners in the context of using TLS intensity data for the diagnostics of buildings and other structures. The potential of TLS intensity data for detecting defects in building walls has been tested on multiple samples by two TOF (Riegl VZ400i, Leica ScanStation C10) and two PS (Z + F 5016 IMAGER, Faro Focus3D) scanners.

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Laser Scanning Technology for Bridge Monitoring

Cited by 21 publications

References 27 publications

Classification of Railway Assets in Mobile Mapping Point Clouds

Classification of Railway Assets in Mobile Mapping Point Clouds

Rayleigh’s quotient–based damage detection algorithm: Theoretical concepts, computational techniques, and field implementation strategies

Comparison of Time-of-Flight and Phase-Shift TLS Intensity Data for the Diagnostics Measurements of Buildings

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