Automated Bridge Inspection for Concrete Surface Defect Detection Using Deep Neural Network Based on LiDAR Scanning

Structural inspection and maintenance of bridges are essential to improve the safety and sustainability of the infrastructure systems. Visual inspection using non-equipped eyes is the principal method of detecting surface defects of bridges, which is time-consuming, unsafe, and encounters inspectors falling risks. Therefore, there is a need for automated bridge inspection. Recently, Light Detection and Ranging (LiDAR) scanners are used for detecting surface defects. LiDAR scanners can collect high-quality 3D point cloud datasets. In order to automate the process of structural inspection, it is important to collect proper datasets and use an efficient approach to analyze them and find the defects. Deep Neural Networks (DNNs) have been recently used for detecting 3D objects within 3D point clouds. PointNet and PointNet++ are deep neural networks for classification, part segmentation, and semantic segmentation of point clouds that are modified and adapted in this work to detect surface concrete defects. The research contributions are: (1) Designing a LiDAR-equipped UAV platform for structural inspection using an affordable 2D scanner for data collection, and (2) Proposing a method for detecting concrete surface defects using deep neural networks based on LiDAR generated point clouds. Training and testing datasets are collected from four concrete bridges in Montréal and annotated manually. The point cloud dataset prepared in five areas, which contain more than 51 million points and 2,572 annotated defects in four classes of crack, light spalling, medium spalling, and severe spalling. The accuracies of 75% (adapted PointNet) and 79% (adapted PointNet++) in detecting defects are achieved in binary semantic segmentation

Development of Bridge Information Model (BrIM) for digital twinning and management using TLS technology

In the current modern era of information and technology, the concept of Building Information Model (BIM), has made revolutionary changes in different aspects of engineering design, construction, and management of infrastructure assets, especially bridges. In the field of bridge engineering, Bridge Information Model (BrIM), as a specific form of BIM, includes digital twining of the physical asset associated with geometrical inspections and non-geometrical data, which has eliminated the use of traditional paper-based documentation and hand-written reports, enabling professionals and managers to operate more efficiently and effectively. However, concerns remain about the quality of the acquired inspection data and utilizing BrIM information for remedial decisions in a reliable Bridge Management System (BMS) which are still reliant on the knowledge and experience of the involved inspectors, or asset manager, and are susceptible to a certain degree of subjectivity. Therefore, this research study aims not only to introduce the valuable benefits of Terrestrial Laser Scanning (TLS) as a precise, rapid, and qualitative inspection method, but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction using TLS-based point cloud, and to contribute to BrIM development. Moreover, this study presents a comprehensive methodology for incorporating generated BrIM in a redeveloped element-based condition assessment model while integrating a Decision Support System (DSS) to propose an innovative BMS. This methodology was further implemented in a designed software plugin and validated by a real case study on the Werrington Bridge, a cable-stayed bridge in New South Wales, Australia. The finding of this research confirms the reliability of the TLS-derived 3D model in terms of quality of acquired data and accuracy of the proposed novel slice-based method, as well as BrIM implementation, and integration of the proposed BMS into the developed BrIM. Furthermore, the results of this study showed that the proposed integrated model addresses the subjective nature of decision-making by conducting a risk assessment and utilising structured decision-making tools for priority ranking of remedial actions. The findings demonstrated acceptable agreement in utilizing the proposed BMS for priority ranking of structural elements that require more attention, as well as efficient optimisation of remedial actions to preserve bridge health and safety

THE USE OF POINT CLOUD DATA TO SUPPORT CONCRETE BRIDGE DECK CONDITION ASSESSMENT

Bridge deck condition assessments are typically conducted through visual physical inspections, utilizing traditional contact sensors for Non-Destructive Evaluation techniques such as hammer Sounding and chain dragging which require the expertise of trained inspectors. However, the accuracy of these inspections is limited by the level of deterioration of the bridge deck, as the ability of the inspectors is proportional to the apparent level of damage. This study aims to improve the accuracy of bridge deck inspection processes by utilizing non-destructive evaluation techniques, including the analysis of point cloud data gathered via Light Detection and Ranging (LiDAR) as a geometry-capturing tool. The overall goal of this research is to evaluate and quantify the effectiveness and efficiency of LiDAR sensors in contributing to the suite of technologies available to perform bridge deck condition assessment. To achieve this, the research proposes to understand the deterioration pattern of New Jersey bridges, evaluate the results gathered from point cloud data collected on a full-scale bridge deck, quantify the information gained from deploying LiDAR on operating bridges in New Jersey, and investigate the costs related to current bridge condition assessment practices and the impact of incorporating the use of LiDAR sensors. Two data processing approaches were chosen to measure gross and fine dimensions of the evaluated bridge decks, resulting in an accuracy of 96% with respect to results gathered from inspection reports

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information

Advanced considerations in LiDAR technology : application enhancement, inspection workflow implementation and data collection quality management

Bridge inspection is a critical topic in infrastructure management and is facing unprecedented challenges as the public is concerned more about bridge safety after a series of bridge failures. LiDAR based remote sensing is recommended as a way in supplementing the prevailing visual inspection to quantify critical bridge information. In this research, focus will be placed on the advanced considerations of LiDAR technology in bridge inspection, including the application evaluation, inspection workflow implementation, and data collection quality management. Particularly, efforts on improving the computational performance of the original damage detection algorithm have been carried out and the use of reflectivity data is introduced as a new feature to enhance the algorithm’s capability in defect recognition. The specific applications that using LiDAR technology to evaluate bridge deck joint and monitoring simulated slope erosion have been studied. This research further studied the inspection workflow implementation and the sources of errors in the LiDAR bridge inspection. Quality management has also been considered to improve the bridge inspection data quality besides the development of advanced inspection technology. In the end, comparative cost analysis is conducted to determine the savings for implementing LiDAR technology into bridge inspection workflow

Development of new system for detection of bridges construction defects using terrestrial laser remote sensing technology

AbstractThere are probable damages and defects happening throughout structure execution or transport and consignment. Defect experience in construction is precious and remarkable. This research work is a trial to develop an easy system to accomplish the most common tasks in the process of detection and monitoring of the as-built structure. However, assessment programmes involved cannot sufficiently detect and deal with defects that happen at construction process, as they are based on measurements at particular positions and periods, and are not included into full electronic techniques. Therefore there is an important requirement to always observe the construction developments to keep away from major rework prices and interruptions. This research represents an automated advance to register laser remote sensing data of as-built model, with BrIM (Bridge Information Model) for a constructed bridge. An experimental work is carried out to confirm the planned method for monitoring the structural defects through a case study was implemented in the design and inspection data in the Jacques Cartier Bridge in Canada. The outcomes demonstrate that the existing technique can be employed to sense the defected elements or structure imprecision fast and accurately

Application of TLS method in digitization of bridge infrastructures : a path to BrIM development

Over the past years, bridge inspection practices and condition assessments were predi-cated upon long-established manual and paper-based data collection methods which were generally unsafe, time-consuming, imprecise, and labor-intensive, influenced by the experience of the trained inspectors involved. In recent years, the ability to turn an actual civil infrastructure asset into a detailed and precise digital model using state-of-the-art emerging technologies such as laser scanners has become in demand among structural engineers and managers, especially bridge asset managers. Although advanced remote technologies such as Terrestrial Laser Scanning (TLS) are recently established to overcome these challenges, the research on this subject is still lacking a comprehensive methodology for a reliable TLS-based bridge inspection and a well-detailed Bridge Information Model (BrIM) development. In this regard, the application of BrIM as a shared platform including a geometrical 3D CAD model connected to non-geometrical data can benefit asset managers, and significantly improve bridge management systems. Therefore, this research aims not only to provide a practical methodology for TLS-derived BrIM but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction. This methodology was further verified and demonstrated via a case study on a cable-stayed bridge called Werrington Bridge, located in New South Wales (NSW), Australia. In this case, the process of extracting a precise 3D CAD model from TLS data using the sliced-based method and a workflow to connect non-geomet-rical information and develop a BrIM are elaborated. The findings of this research confirm the reliability of using TLS and the sliced-based method, as approaches with millimeter-level geometric accuracy, for bridge inspection subjected to precise 3D model extraction, as well as bridge asset management and BrIM development

Evaluation of new technologies to support asset management of metro systems

Since 1930, London Underground Limited (LUL) has performed visual inspections to understand the condition of the physical assets such as tunnels, bridges and structures. The major problem with this kind of inspection is the lack in quality of the data, as it depends on the ability of the inspector to assess and interpret the condition of the asset both accurately and with repeatability. In addition, data collection is time-consuming and, therefore, costly when the whole of the metro network needs to be regularly inspected and there are limited periods when access is available. The problems associated with access to the infrastructure have increased significantly with the implementation of the night tube and will increase further as the night tube is extended over the next 5 to 10 years. To determine the condition of metro assets and to predict the need for intervention, monitoring the changes in the assets’ condition is key to any further evaluation and maintenance planning. This thesis presents the outcomes of using new technologies such as Thermography, Kinematic and Static Laser Scanning, Close-Range Photogrammetry and Total Station to measure defects, such as water seepage, mortar loss in joints, lining face loss (in brick tunnels), cracks, corrosion, voids, cavities and spalls. Each technique is explored through three case studies that evaluate the performance and limitation in the determination of the asset condition. The first case study was performed to compare and contrast the use of Euroconsult’s high definition laser survey against a Principal Inspection Report to determine the level of consistency in predicting the asset condition. During this case study, reports from laser surveys and principal inspections of brick tunnels and covered ways were compared. This analysis showed that a direct comparison between the two inspections is not appropriate because the laser inspection does not capture all the defects mentioned in the Engineering Standard S1060. It also showed that to close the gap between the laser survey and visual inspection, laser surveys would have to be performed every year in brick tunnels and then compare any changes in asset condition with that from the previous scan. The second case study was performed using Infrared Thermography (IRT) to identify water seepage in the brick tunnels as well as test the system in a configuration that would allow the survey to be done from an engineering train. A set of calibration tests were performed in the lab and later the technique was trialled on an engineering train. The results showed that it is possible to measure the level of moisture on specific parts of the lining and that the comparison of surveys performed at different times can allow asset managers to react before a seepage is established, potentially reducing the risk of system disruption caused by water ingress in tunnels. The data also revealed that this technique could be used for other purposes, such as examining the condition of other assets such as brackets, cable supports and broken light bulbs. The third case study was performed using a Terrestrial Laser Scanner, Close-Range Photogrammetry and Total Station Survey to identify defects in structures. In order to test these technologies, a wing wall, located on the north-east wing of the HC3 underbridge at Ladbroke Grove Station, was chosen. This case study demonstrated that LUL can easily implement this type of technology to inspect rapidly their buildings and structures, being able to identify defects and monitor their assets for translation, rotation and changes in shape during changes in loading or the decay of the structure (insidious decline) and the construction of nearby assets. In this research, a large volume of data was captured, and further work is needed in order to manage the data using ‘big data’ concepts. Although it may not be possible to fully understand the insidious decline of an asset, the use of these techniques allows us to better understand how a civil asset behaves, potentially reducing the amount of reactive maintenance to a minimum, consequently reducing service costs and falls in revenue due to disruptions in the system. To successfully analyse the data from new technologies a combination of skills is required and different or retrained personal will be needed