Contactless recognition of concrete surface damage from laser scanning and curvature computation

Teza, Giordano; Galgaro, Antonio; Moro, Francesca

doi:10.1016/j.ndteint.2008.10.009

Cited by 100 publications

(60 citation statements)

References 18 publications

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“…On the other hand, LiDAR is used for collecting point clouds. LiDAR-based methods are highly accurate and able to detect the depth of the defects [3,10,11] and mass losses [12,13,14]. Although the initial cost of the LiDAR is high, it is a time and cost effective method in the long term [3].…”

Section: Methods For Surface Defects Inspectionmentioning

confidence: 99%

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

Bolourian¹,

Soltani²,

Hammad³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-According to Statistics Canada, bridges in Canada have a service life of approximately 43 years. With the majority of bridges passing half of their expected service life, a large amount of investment needs to be made to inspect and maintain them in a safe condition. Manual inspection methods are both time-consuming and costly, which may discourage further inspections and follow-up of defects. Thus, there is a great need for using an automated data collection system. Surface defects (e.g. cracks) in concrete bridges can be inspected using 3D Light Detection and Ranging (LiDAR) scanner as a Non-Destructive Testing (NDT) method. However, the commonly used terrestrial LiDAR is limited to stationary data collection, which reduces the accessibility to some components of the bridges. To tackle this limitation, a LiDAR attached to an Unmanned Aerial Vehicle (UAV) provides more flexibility and accessibility for inspecting large surface areas without threatening inspectors' safety. After providing a comprehensive literature review about the usage of UAVs and LiDAR for the inspection of different types of structures, this paper proposes a high level framework for bridge inspection using LiDAR-equipped UAV. The framework includes the following steps: (1) planning a collisionfree optimized path with respect to the minimum cost and maximum coverage considering a variety of constrains and requirements related to the hardware and the inspection task, and (2) data analysis for detecting the surface defects based on the collected point clouds.

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Section: Methods For Surface Defects Inspectionmentioning

confidence: 99%

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

Bolourian¹,

Soltani²,

Hammad³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Observation of existing works on detecting surface damage of the bridge structures is summarized in Table 2. As an example Figure 3 shows work by Teza et al [26] using a Gaussian curvature in sub-areas of the dataset to recognise damaged area. In this work, the sub-area is considered damage if the standard deviation of the Gaussian curvature in this sub-area was greater than ones of the reference area.…”

Section: Surface Defectsmentioning

confidence: 99%

Application of Terrestrial Laser Scanner In Bridge Inspection: Review and an Opportunity

Truong-Hong¹,

Laefer²

2014

IABSE Reports

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Abstract:Heavy traffic and aggressive environmental conditions can cause unexpected bridge deterioration. Traditional condition evaluation is expensive. An alternative is Terrestrial laser scanning (TLS) which is a non-contact approach that safe, fast, and applicable to a range of weather conditions. This paper reviews applications of TLS on bridge measurement involving geometric documentation, surface defect determination, and corrosion evaluation, and crack identification. Currently, most post-processing of TLS is manual or within third party software. This paper discusses potential approaches to automatic post-processing.

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“…The addition of intensity data to the geometric data has proved useful in several applications [19,26,27] and a study by Teza et al [20] showed a successful method based on curvature analysis of TLS geometric data and aimed at recognizing surface defects of a concrete bridge due to mass loss. However, laser scanner intensity data is not often utilised, because it is affected by several factors such as scanning distance and incidence angle, water content, ambient light, humidity, type of laser and object surface properties [21,22].…”

Section: Introductionmentioning

confidence: 99%