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Laser Scanning for Automatic Bridge Assessment

Periodic Reporting for period 1 - BridgeScan (Laser Scanning for Automatic Bridge Assessment)

Reporting period: 2019-01-01 to 2020-12-31

This project “Laser Scanning for Automatic Bridge Assessment” called BridgeScan proposes the framework to use laser scanning to acquire 3D topographic data points of bridges and to process data for bridge assessment purpose. This project will involve (i) investigate a flight path and scanning parameters to scan a bridge structure by UAV with an integrated laser scanning sensor, (ii) develop automatic, robust, efficient methods to process LiDAR data to identify structural deficiencies for bridge condition rating and (iii) develop automatic methods for reconstructing a 3D model of the bridge for bridge assessment based on finite element analysis (FEA). The project will bridge a new framework to inspect the bridge, which can overcome some shortcomings of a current workflow. Implementing the project would inspect the bridges with higher frequency and report any changes of the bridge timely. Moreover, the framework in extracting the point clouds of the bridge components would improve the current pipeline, which mostly relied on manual work. Finally, the proposed methods and outcome of the project can set as a fundamental framework for developing the framework for inspection and assessment of other infrastructures.
The project delivers two pilot case bridges: (1) a metal truss bridge on Gaagweg street, Schipluiden, the Netherlands, (2) a Constructie bridge on Weaslandseweg street, Delft, The Netherlands. All of them were scanned by a terrestrial laser scanner. Unfortunately, due to Covid-19, data acquisition using an UAV-laser scanning to capture a bridge structure could not be organized.

The project proposed two segmentation methods called cell-based and voxel-based region growing methods for point cloud segmenting. The methods can be applied for various types of structures for example bridges and buildings. Moreover, the statistical model has been implemented to support to automatically select parameters for example cell size and voxel size. Particularly, the statistical model can automatically select and adjust thresholds for the segmentation method regarding to the quality of data set and structure size.

The project proposed a new framework to extract point clouds describing surfaces of bridge components. The framework deployed spatial point clouds and contextual knowledge of bridge components to extract bridge structural elements in a consecutive order: superstructure and substructure. For each structural element, the proposed method consists of two main steps called coarse extraction and fine filtering. In the coarse extraction, candidate points of a structure were extracted while the fine filtering aimed to obtain the final points of the surface. As such, the framework would handle a massive data points of the bridge easily but also gave high accuracy of an extraction. The proposed framework has been tested on two box or slab bridge, and all bridge elements were successfully extracted without any manual intervention. Moreover, the framework was also extended for extracting the building structures successfully.

Additionally, the project has developed three methods called point-to-point, point-to-cell and cell-to-cell to measure the deformation of structural members. The method was used to measure the vertical clearance of the bridge and deformation of an individual structure. The method was also extended to measure other structures like beams and columns of a building. Additionally, the project also developed machine learning-based methods to determine the scaling/spalling of a concrete structure.

The project proposed a methodology to generate 3D models of bridge components. The method was successfully reconstructed the 3D bridge pier model by using a sweeping technique based on the cross-sections along a principal direction of the pier.

Results of this project were presented in 8 top conferences (one conference was postponed to 2021 due to Covid-19) in fields of remote sensing, building information modelling (BIM), bridge and building structural engineering. Moreover, one journal manuscript is in review and two others are in preparation. All publications are shared in a TU Delft repository for open access.

During the project, the researcher (Dr. Linh Truong-Hong) was invited to deliver a talk Fugro Company in the Netherlands and at the workshop “Point Cloud – 3D Technology” held at Ho Chi Minh City University of Technology (HCMUT), Viet Nam. Additionally, the researchers have also organized the short course on “Laser scanning for structural and environmental engineering” at HCMUT, Viet Nam, which was attracted more than 20 students and professional surveyors. Results of the project was also presented at NCG (Netherlands Geodetic Commission) symposium 2019 and 2020.

The researcher also presents the project at Research day of Geoscience and Remote sensing, and Geoscience Engineering, in which participants are from academia and professional. Moreover, during the project, the researcher was also served as a guest lecturer for MSc class at Dept. of Geoscience and Remote Sensing and supervised BSc and MSc thesis.
As Covid-19, the researcher cannot organize a half-day workshop, the researcher contacted to potential companies to present the results of the project and to establish collaboration. A list of the companies includes Leap3D (www.leap3D) and Asset Insight ( and SSIFT ( in testing the algorithms and translating knowledge of laser scanning for bridge surveying, bridge inspection, and bridge reconstruction, VMT ( for using laser scanning in building inspection, Acernis ( in generating a digital twin for power transmission tower, and BlackWolf ( for measuring the deformation of oil tanks. Moreover, the researcher also discussed with Fugro ( in collaborating on processing point clouds for the railway.
The project investigates the use of point clouds from laser scanning and photogrammetry for bridge inspection and bridge geometric model generation. The work shows that laser scanning and photogrammetry can be partially replaced physical inspectors in bridge inspection, which can eliminate accidents of inspectors and reduce inspection cost. It is clear that the use of laser scanning and photogrammetry-based point clouds in bridge inspection can reduce the use of special equipment, physical inspectors on-site, and cost. That would increase inspection frequency to provide an alarm to establish timely planning to protect the bridge.
Importantly, the project also developed a framework to extract and reconstruct bridge components automatically. This can reduce huge cost to create the bridge model, which is currently done through manual work with computer-aided drawing (CAD) software. The proposed methods can be integrated into a bridge management system to improve the current system, where engineers can monitor the bridge elements in 3D form and can integrate semantic information, damage, and tracking damage propagation. Finally, the framework on 3D geometric reconstruction in this project can be integrated into a digital twin platform, where an as-service model of the bridge can be updated regularly allowing to predict the response of the bridge more accurately. Additionally, the framework can be also extended for other infrastructures, for example, roads, harbors, and power transmission networks.
Inspecting structural components of a construction project using laser scanning
Quantitative Assessment of Structural Components for Construction Management Using Laser Scanning Da
A framework to extract structural elements of construction site from laser scanning
Extracting Bridge Components from a Laser Scanning Point Cloud
Identifying bridge deformation using laser scanning data
Storage Tank Inspection based Laser Scanning
Automatic detection of Road Edges from Aerial Laser Scanning Data