Fast Fourier Transform

Computer Aided Civil and Infrastructure Engineering

Structural crack detection using deep convolutional neural networks

Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakages and the leakage information (area, location, lining segments, etc.).

Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data.

This work presents the combination of Deep-Learning (DL) and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability and experience of the engineer as well as on the total workload (stress and tiredness may influence the ability to observe and record information). As a result of these issues, in the last decade there is the desire to automate the monitoring using new methods of inspection. The present paper has the goal of combining DL with traditional image processing to create a tool able to detect, locate and measure the structural defect.

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Deep learning and image processing for automated crack detection and defect measurement in underground structures

. The civil engineering and construction sector, including the railway industry, is seeking innovative approaches to reduce costs on repetitive and labour-intensive tasks and avoid the use of highly qualified staff for simple manual duties. Such tasks can include the visual inspection of tunnels, where the process is still dominated by manual operations. Our work compares Close Range Photogrammetry (CRP) and Terrestrial Laser Scanning (TLS), both performed with low-end sensors to reflect the industry’s tendency towards easy to use and easy to maintain hardware. It also analyses the benefits of substituting conventional visual inspections of tunnels with automated survey approaches and computer vision techniques. The project’s outcomes suggest that photogrammetry is a valid alternative to laser scanning for visual inspection of concrete segmentally lined tunnels: from the geometric point of view it provides global accuracy at comparable level to laser scanning, in addition it halves the time to generate the 3D model and provides the user with photo-realistic outputs. It is generally more versatile and it is easier to inspect, visualise and navigate the data. The authors argue that the results presented here will push tunnel inspection in the direction of automated approaches with direct benefits on surveying costs as well as Health & Safety (H&S). Utilising available technology supports risk-based asset management and thus ensures safe and operational performance of a railway for passengers to use.

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Cost-benefit Analysis of Rail Tunnel Inspection for Photogrammetry and Laser Scanning

Tunnel inspection and asset management is typically a labour-intensive process where engineering judgement and experience is heavily relied upon to identify and assess tunnel condition over kilometres of homogeneous structures. Novel work flows and digital applications have been developed by the authors to create various smart image-based inspection and analysis tools that reduce the potential subjectivity and inconsistency of these inspections. This has resulted in significant improvements to existing tunnel inspection practices and structural health assessment. Current advances in image capture technology and computational processing power has enabled high integrity data to be easily captured, visualised and analysed. The work flows and tools developed take advantage of existing low-cost image capture hardware, open-source processing software and couples this with the creation of unique machine learning algorithms and analytics. Core innovations include: (i) use of low-cost photographic equipment for high quality imagery capture (ii) use of automated inspection vehicles for data capture (iii) Deep learning for automatic defect object recognition and defect classification (iv) Creation of immersive dashboards and 3D visualisations. This results in a suite of image based service offerings and deliverables, relevant to specific tunnel engineering issues and asset management aims. Thanks to deep learning, defect detection and asset condition metrics are automatically created, enabling: (i) the tunnel owner to gain greater insights into their asset resilience and operations, (ii) the tunnel engineer to focus on key issues aided by machine learning.

F. Panella

Papers

Deep learning and image processing for automated crack detection and defect measurement in underground structures

Cost-benefit Analysis of Rail Tunnel Inspection for Photogrammetry and Laser Scanning

Smart Image Based Technology and Deep Learning for Tunnel Inspection and Asset Management