Potential damage detection of bridge deck based on single phase point cloud

2021 ◽  
Author(s):  
Xiang Lei Liu ◽  
Tianke Su
Keyword(s):  
Damage Detection ◽  
Point Cloud ◽  
Single Phase ◽  
Bridge Deck ◽  
Phase Point ◽  
Potential Damage

scholarly journals Damage Detection and Analysis of Urban Bridges Using Terrestrial Laser Scanning (TLS), Ground-Based Microwave Interferometry, and Permanent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR)

2019 ◽  
Vol 11 (5) ◽  
pp. 580 ◽  
Author(s):  
Xianglei Liu ◽  
Peipei Wang ◽  
Zhao Lu ◽  
Kai Gao ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Damage Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Synthetic Aperture ◽  
Damage Area ◽  
Microwave Interferometry ◽  
Bridge Damage ◽  
Potential Damage

This paper presents a practical framework for urban bridge damage detection and analysis by using three key techniques: terrestrial laser scanning (TLS), ground-based microwave interferometry, and permanent scatterer interferometry synthetic aperture radar (PS-InSAR). The proposed framework was tested on the Beishatan Bridge in Beijing, China. Firstly, a Digital Surface Model (DSM) of the lower surface of the bridge was constructed based on the point cloud generated by using TLS to obtain the potential damage area. Secondly, the dynamic time-series displacement of the potential damage area was acquired by ground-based microwave interferometry, and the Extreme-Point Symmetric Mode Decomposition (ESMD) method was applied to detect damages by the use of signal decomposition and instantaneous frequency calculation. Lastly, the PS-InSAR technique was applied to obtain the surface deformation around Beishatan Bridge by using COSMO-SkyMed images with a ground resolution of 3 m × 3 m, and finally, we analyzed the causes of bridge damage. The experimental results showed that the proposed framework can effectively obtain the potential damage area of the bridge by the DSM from the point cloud by TLS and further judge whether the bridge was damaged by the ESMD method, based on the time-series displacement data. The results also showed that the subway shield construction may be the reason for damage to Beishatan Bridge.

scholarly journals Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6337
Author(s):  
Quang-Quang Pham ◽  
Ngoc-Loi Dang ◽  
Quoc-Bao Ta ◽  
Jeong-Tae Kim
Keyword(s):  
Damage Detection ◽  
Prestressed Concrete ◽  
Impedance Model ◽  
Artificial Damage ◽  
Smart Aggregate ◽  
Damage Monitoring ◽  
Concrete Damage ◽  
Potential Damage ◽  
Optimal Localization ◽  
Real Scale

This study investigates the feasibility of smart aggregate (SA) sensors and their optimal locations for impedance-based damage monitoring in prestressed concrete (PSC) anchorage zones. Firstly, numerical stress analyses are performed on the PSC anchorage zone to determine the location of potential damage that is induced by prestressing forces. Secondly, a simplified impedance model is briefly described for the SA sensor in the anchorage. Thirdly, numerical impedance analyses are performed to explore the sensitivities of a few SA sensors in the anchorage zone under the variation of prestressing forces and under the occurrence of artificial damage events. Finally, a real-scale PSC anchorage zone is experimentally examined to evaluate the optimal localization of the SA sensor for concrete damage detection. Impedance responses measured under a series of prestressing forces are statistically quantified to estimate the performance of damage monitoring via the SA sensor in the PSC anchorage.

scholarly journals UAV-BASED STRUCTURAL DAMAGE MAPPING – RESULTS FROM 6 YEARS OF RESEARCH IN TWO EUROPEAN PROJECTS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 187-194 ◽  
Author(s):  
N. Kerle ◽  
F. Nex ◽  
D. Duarte ◽  
A. Vetrivel
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Point Cloud ◽  
Feature Fusion ◽  
Texture Features ◽  
Mobile App ◽  
Detection Methods ◽  
Support Vector ◽  
3D Point Cloud ◽  
Damage Mapping

<p><strong>Abstract.</strong> Structural disaster damage detection and characterisation is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of UAV in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.</p>

New Damage Detection Method Based on Support Reaction and Mid-Span Displacement for Simply Supported Bridges

2010 ◽  
Vol 29-32 ◽  
pp. 1537-1542
Author(s):  
Z.J. Ma ◽  
S.J. Zhang
Keyword(s):  
Numerical Simulation ◽  
Damage Detection ◽  
Model Experiment ◽  
Direct Relationship ◽  
Detection Method ◽  
Simply Supported ◽  
Local Mass ◽  
Support Reaction ◽  
Basic Characteristics ◽  
Potential Damage

Capabilities to locate damage, and applicability to simply supported bridges are considered as basic characteristics of the method to be investigated in this paper. A new method which employs support reaction and mid-span displacement as indicators is presented to detect a certain type of damage. Unlike the existing study, this work proposes the direct relationship between the change in local mass caused by damage and the measured support reaction and mid-span displacement values. With the cooperation of inspection means, this method is capable of successfully identifying the location of local potential damage. The feasibility of this method is demonstrated by numerical simulation and model experiment.

Damage detection of coupled bridge deck-girder system

2006 ◽  
Vol 42 (11) ◽  
pp. 942-949 ◽  
Author(s):  
S. Roy ◽  
S. Chakraborty ◽  
S.K. Sarkar
Keyword(s):  
Damage Detection ◽  
Bridge Deck

scholarly journals Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures

2019 ◽  
Vol 8 (12) ◽  
pp. 527 ◽  
Author(s):  
Mohammad Ebrahim Mohammadi ◽  
Richard L. Wood ◽  
Christine E. Wittich
Keyword(s):  
Damage Detection ◽  
Real World ◽  
Point Cloud ◽  
Detection Method ◽  
False Positive Rate ◽  
Point Clouds ◽  
Surface Damage ◽  
Normal Surface ◽  
Synthetic Datasets

Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged areas. Two synthetic datasets with planar and cylindrical geometries with known ground truth damage were created and used to test the developed workflow. In addition, the developed method was further validated on three real-world point cloud datasets using lidar and structure-from-motion techniques, which represented different underlying geometries and exhibited varying severity and mechanisms of damage. The analysis of the synthetic datasets demonstrated the robustness of the proposed damage detection method to classify vertices as surface damage with high recall and precision rates and a low false-positive rate. The real-world datasets illustrated the scalability of the damage detection method and its ability to classify areas as damaged and undamaged at the centimeter level. Moreover, the output classification of the damage detection method automatically bins the damaged vertices into different confidence intervals for further classification of detected likely damaged areas. Moving forward, the presented workflow can be used to bolster structural inspections by reducing subjectivity, enhancing reliability, and improving quantification in surface-evident damage.

Building Damage Detection Based on Single-Phase High-Resolution Remote Sensing Images

2012 ◽  
Vol 433-440 ◽  
pp. 6422-6429
Author(s):  
Hong Zhang
Keyword(s):  
Damage Detection ◽  
Single Phase ◽  
Color Space ◽  
Detection Algorithm ◽  
Building Damage ◽  
Google Earth ◽  
Building Detection ◽  
Hsv Color Space ◽  
Detection Approach ◽  
Gray Level Histogram

This paper presents a building detection approach based on HSV color space. The method is based on the gray level histogram features, which can separate the housing construction units from complex background. A building damage detection algorithm based on regional statistical information is also proposed in this paper, and a set of performance parameters of feature vector is studied to identify the extent of the housing collapse. The experiments on Haiti post-earthquake images from Google Earth and Yushu post-earthquake images from Internet are discussed in the paper. The experimental results show that proposed approach is effective and feasible.

Building Damage Detection Method Based on Normal Vector Computation of Airborne Point Cloud Data

2021 ◽  
Author(s):  
Shusong Huang ◽  
Yong Zeng ◽  
Wei Yi ◽  
Weirong Chen ◽  
Wenbo Su ◽  
...  
Keyword(s):  
Damage Detection ◽  
Point Cloud ◽  
Detection Method ◽  
Building Damage ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Cloud Data

Damage detection on a steel-free bridge deck using random vibration

2005 ◽  
Author(s):  
Zhengjie Zhou ◽  
Bruce F. Sparling ◽  
Leon D. Wegner
Keyword(s):  
Damage Detection ◽  
Random Vibration ◽  
Bridge Deck

scholarly journals DIGITAL DOCUMENTATION, BRIDGE DECK LINEARITY DEFORMATION AND DECK THICKNESS MEASUREMENT USING TERRESTRIAL LASER SCANNER (TLS) AND CLOSE RANGE PHOTOGRAMMETRY (CRP)

2018 ◽  
Vol IV-5 ◽  
pp. 47-51 ◽  
Author(s):  
S. K. P. Kushwaha ◽  
H. Pande ◽  
S. Raghavendra
Keyword(s):  
Point Cloud ◽  
Bridge Deck ◽  
Laser Scanner ◽  
Bottom Layer ◽  
Thickness Measurement ◽  
Terrestrial Laser Scanner ◽  
Road Transportation ◽  
The Road ◽  
Close Range Photogrammetry ◽  
Close Range

<p><strong>Abstract.</strong> Bridges are one of the vital and valuable engineer structure from decades. As they play a major role in the road transportation sector. Few old bridges lacks its documents about the measurements of the structure. The study has been carried out on three different types of bridges like Truss, Beam and Cable bridges. Documenting these bridges can be utilised to reconstruct or renovate the bridge in case of any disaster or damage. 3D documentation is made from the point cloud Dataset acquired from Terrestrial Laser Scanner – TLS (Riegl VZ 400) and Close Range photogrammetry – CRP (Nikon DSLR 5300). TLS and CRP point cloud are merged together to increase the density of points. Over the duration of time the bridge gets older and due to the load on the bridge deck, linearity in the deck effects and this linearity deformation measurement is important to know the present deformation in the deck. To know exactly at which part there is more linearity deformation, deflection is calculated at sample intervals between the present linearity conditions of the deck to the idle linearity conditions of the deck. The bridge deck thickness is also measured with the point cloud dataset. A slice is cut through the deck of point cloud dataset, the difference between the top and bottom layer of the deck gives us the thickness of the deck including the road. This thickness can be used to measure when a new deck layer is constructed or during filling up of any potholes. This study is mainly focused to help the construction and maintenance authority, bridge monitoring department and researchers.</p>

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