256 research outputs found

    Review on Machine Learning-based Defect Detection of Shield Tunnel Lining

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    At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected

    Water leakage mapping in concrete railway tunnels using LiDAR generated point clouds

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    Dissertation (MEng (Transportation Engineering)) University of Pretoria, 2021.Light detection and ranging (LiDAR) is a key non-destructive testing (NDT) method used in modern civil engineering inspections and commonly known for its ability to generate high-density coordinated point clouds of scanned environments. In addition to the coordinates of each point an intensity value, highly dependent on the backscattered energy of the laser beam, is recorded. This value has proven to vary largely for different material properties and surfaces. In this study properties such as surface colour, roughness and state of saturation are reviewed. Different coloured and concrete planar targets were scanned using a mobile LiDAR scanning system to investigate the effect distance, incidence angle and ambient lighting have on targets of differing properties. The study comprised controlled laboratory scans and field surveying of operational concrete railway tunnels. The aim of field tests was to automatically extract water leakage areas, visible on tunnel walls, based on the intensity information of points. Laboratory results showed that darker coloured targets resulted in a lower recorded intensity value and larger standard deviation of range. Black targets recorded the lowest intensities (0 - 4 units) with 50% higher standard deviations of range, on average, compared to all other coloured targets which recorded standard deviations of around 12 mm. The roughness of each coloured target showed to largely influence the recorded intensity, with smooth surfaces recording higher standard deviations of measurements. Concrete targets proved that a difference in roughness and saturation was detectable from intensity data. The biggest change was seen with saturated targets where a 70 to 80 % lower intensity value was recorded, on average, when compared to the same targets in their dry state. The difference in target roughness showed to have no effect on intensity when saturated. The laboratory data provided an important reference for the interpretation and filtering of field point clouds. Ambient lighting had no significant effect on all measurements for both the coloured and concrete targets. Field tests conducted on an operational concrete railway tunnel confirmed and demonstrated the ability to rapidly identify, extract and record areas of water leakage based on the intensity and spatial information of point cloud data. This is particularly useful as water ingress is known to degrade concrete, resulting in the earlier onset of corrosion, spalling and loss of strength. The mobile LiDAR scanning system used here proved capable of reducing survey time, which would allow for shorter interval revisits, while providing more quantitative information of the leakage areas. Long-term continuous monitoring of the internal structure of a tunnel will reduce the life cycle costs by removing the need for personnel to enter the tunnels for visual assessments and enable remedial work to be better planned by analysing a virtual 3D point cloud of the tunnel before stepping foot onto site.Transnet Freight RailChair in Railway EngineeringCivil EngineeringMEng (Transportation Engineering)Unrestricte

    Digital technology for quality management in construction:A review and future research directions

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    Significant developments in digital technologies can potentially provide managers and engineers with the ability to improve the quality of the construction industry. Acknowledging the current and future use of digital technologies in construction quality management (CQM), we address the following research question: What developments in digital technologies can be used to improve quality in the construction industry? In addressing this research question, a systematic review approach is used to examine the studies that have been used for the management of quality in the construction industry. This review indicates that there is a need for digital technology-based quality management to be: (1) enhance defect management for concealed work, (2) enhance pre-construction defects prevention as well as post-completion product function testing, and (3) research on construction compliance inspection as a direction. We suggest that future research focus on quality culture development, advanced data analytics, and behavioral quality assessment

    Automating Inspection of Tunnels With Photogrammetry and Deep Learning

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    Asset Management of large underground transportation infrastructure requires frequent and detailed inspections to assess its overall structural conditions and to focus available funds where required. At the time of writing, the common approach to perform visual inspections is heavily manual, therefore slow, expensive, and highly subjective. This research evaluates the applicability of an automated pipeline to perform visual inspections of underground infrastructure for asset management purposes. It also analyses the benefits of using lightweight and low-cost hardware versus high-end technology. The aim is to increase the automation in performing such task to overcome the main drawbacks of the traditional regime. It replaces subjectivity, approximation and limited repeatability of the manual inspection with objectivity and consistent accuracy. Moreover, it reduces the overall end-to-end time required for the inspection and the associated costs. This might translate to more frequent inspections per given budget, resulting in increased service life of the infrastructure. Shorter inspections have social benefits as well. In fact, local communities can rely on a safe transportation with minimum levels of disservice. At last, but not least, it drastically improves health and safety conditions for the inspection engineers who need to spend less time in this hazardous environment. The proposed pipeline combines photogrammetric techniques for photo-realistic 3D reconstructions alongside with machine learning-based defect detection algorithms. This approach allows to detect and map visible defects on the tunnel’s lining in local coordinate system and provides the asset manager with a clear overview of the critical areas over all infrastructure. The outcomes of the research show that the accuracy of the proposed pipeline largely outperforms human results, both in three-dimensional mapping and defect detection performance, pushing the benefit-cost ratio strongly in favour of the automated approach. Such outcomes will impact the way construction industry approaches visual inspections and shift towards automated strategies

    Evaluation of new technologies to support asset management of metro systems

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    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

    Shaking table model tests of reinforced concrete tunnels under multiple earthquake shakings

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    The cumulative effect of multiple relatively low or moderate seismic events on tunnels is not well-understood within an earthquake prone region. To investigate the effect of multiple earthquakes on the integrity of tunnel structures, 1 g shaking table tests were performed. This research also explored the impact of tunnel presence on the soil response, namely analyzing soil-structure interaction effects. Within the tests, a free-field model and a soil-tunnel model were employed synchronously. The shaking table study was designed and conducted following a new set of scaling laws able to faithfully simulate cracking of tunnel lining, and white noise tests were applied after each seismic shaking for dynamic identification. Except for the measurement of acceleration and bending strain, a new cracking monitoring system equipped with wireless mini-cameras was proposed to detect the evolution of tunnel damages during the tests, while Light Detection and Ranging (LiDAR) technology was utilized to examine the ground deformations in the two model configurations. Based on the point cloud data, it was observed that sand densification effects were obvious in the two models and the influence of tunnel presence on the soil response was restricted in a limited region. The trend in the evolution of an image-based damage index kept similar to that in the progression of surface settlements, implying that the seismically-induced ground failure might play an important role in the seismic response of shallow tunnels. Also, the frequency shifting behaviour of lining did not follow the intuitive pattern, where a reduction in natural frequencies is expected when structural damage occurs. Moreover, the variation of acceleration amplification factors of the tunnel was almost consistent with that of the soil, and the trend of strain agreed with that of surface settlements. The findings from this study provide an insight to better understand the resilience and life-long performance of earthquakes exposed underground structures

    Flexural Behaviour of Steel Fibre Reinforced Concrete Tunnel Linings

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    The promotion of steel fibre reinforced concrete (SFRC) as a construction material for tunnel linings has prompted a number of researchers to focus on methods of evaluating their flexural strength and stiffness. This thesis presents the results of an experimental and numerical investigation of the flexural behaviour of full-scale steel fibre reinforced concrete tunnel lining segments. A series of a three-point flexure tests were performed to evaluate the maximum load carrying capacity, the load-deformation behaviour and crack propagation characteristics of these segments. The material properties of the steel fibre reinforced concrete were also studied, using both destructive and non-destructive methods. Element compression and tension tests were conducted to characterize the compressive and tensile strength properties of the SFRC. Additionally, computed tomographic scanning was conducted to analyse and estimate the density fraction and fibre orientation of the fibres in SFRC cores. Three-dimensional finite element analyses were conducted to calibrate a concrete damage plasticity constitutive model and provide better understanding of the segment flexural behaviour. The experimental program indicated that the variation in structural performance of the segments was likely due to an inhomogeneity of fibre distribution and orientation. Modifying the numerical model to account for these variations resulted in a more accurate analysis. Furthermore, from the numerical finite element analysis it was found that the non-linear elasto-plastic concrete damage plasticity model in the crack zone of the beam was mesh dependent. Parametric analyses also revealed that the model was particularly sensitive to small changes to the tensile material property input parameters
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