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

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

Terrestrial Laser Scanner as a Tool for Assessment of Saturation and Moisture Movement in Building Materials

Non Destructive Testing (NDT) is a key element of modern civil engineering. It is especially important in civil and structural engineering helping both in quality control of produced elements and technical assessments of existing structures. Existing NDT methods are being continuously improved and new methods are developed or adopted from different engineering fields. Terrestrial Laser Scanner (TLS) method which is commonly used for geodetic applications has a great potential to be successfully harnessed in civil and structural engineering. TLS can be used for remote sensing of saturation of building materials. A research programme was prepared in order to prove this concept. Specimens representing most popular European building materials were scanned using TLS. Tested specimens were in different saturation states including capillary rising saturation. The saturation assessment was based on differences of values of intensity. The concept proved to be feasible and technically realistic

On the use of the OptD method for building diagnostics

Terrestrial laser scanner (TLS) measurements can be used to assess the technical condition of buildings and structures; in particular, high-resolution TLS measurements should be taken in order to detect defects in building walls. This consequently results in the creation of a huge amount of data in a very short time. Despite high-resolution measurements typically being needed in certain areas of interest, e.g., to detect cracks, reducing redundant information on regions of low interest is of fundamental importance in order to enable computationally efficient and effective analysis of the dataset. In this work, data reduction is made by using the Optimum Dataset (OptD) method, which allows to significantly reduce the amount of data while preserving the geometrical information of the region of interest. As a result, more points are retained on areas corresponding to cracks and cavities than on flat and homogeneous surfaces. This approach allows for a thorough analysis of the surface discontinuity in building walls. In this investigation, the TLS dataset was acquired by means of the time-of-flight scanners Riegl VZ-400i and Leica ScanStation C10. The results obtained by reducing the TLS dataset by means of OptD show that this method is a viable solution for data reduction in building and structure diagnostics, thus enabling the implementation of computationally more efficient diagnostic strategies

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

A machine learning approach for the detection of supporting rock bolts from laser scan data in an underground mine

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordRock bolts are a crucial part of underground infrastructure support; however, current methods to locate and record their positions are manual, time consuming and generally incomplete. This paper describes an effective method to automatically locate supporting rock bolts from a 3D laser scanned point cloud. The proposed method utilises a machine learning classifier combined with point descriptors based on neighbourhood properties to classify all data points as either ‘bolt’ or ‘not-bolt’ before using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to divide the results into candidate bolt objects. The centroids of these objects are then computed and output as simple georeferenced 3D coordinates to be used by surveyors, mine managers and automated machines. Two classifiers were tested, a random forest and a shallow neural network, with the neural network providing the more accurate results. Alongside the different classifiers, different input feature types were also examined, including the eigenvalue based geometric features popular in the remote sensing community and the point histogram based features more common in the mobile robotics community. It was found that a combination of both feature sets provided the strongest results. The obtained precision and recall scores were 0.59 and 0.70 for the individual laser points and 0.93 and 0.86 for the bolt objects. This demonstrates that the model is robust to noise and misclassifications, as the bolt is still detected even if edge points are misclassified, provided that there are enough correct points to form a cluster. In some cases, the model can detect bolts which are not visible to the human interpreter.University of Exete

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Geotechnical Engineering for the Preservation of Monuments and Historic Sites III

The conservation of monuments and historic sites is one of the most challenging problems facing modern civilization. It involves, in inextricable patterns, factors belonging to different fields (cultural, humanistic, social, technical, economical, administrative) and the requirements of safety and use appear to be (or often are) in conflict with the respect of the integrity of the monuments. The complexity of the topic is such that a shared framework of reference is still lacking among art historians, architects, structural and geotechnical engineers. The complexity of the subject is such that a shared frame of reference is still lacking among art historians, architects, architectural and geotechnical engineers. And while there are exemplary cases of an integral approach to each building element with its static and architectural function, as a material witness to the culture and construction techniques of the original historical period, there are still examples of uncritical reliance on modern technology leading to the substitution from earlier structures to new ones, preserving only the iconic look of the original monument. Geotechnical Engineering for the Preservation of Monuments and Historic Sites III collects the contributions to the eponymous 3rd International ISSMGE TC301 Symposium (Naples, Italy, 22-24 June 2022). The papers cover a wide range of topics, which include: 　 - Principles of conservation, maintenance strategies, case histories - The knowledge: investigations and monitoring - Seismic risk, site effects, soil structure interaction - Effects of urban development and tunnelling on built heritage - Preservation of diffuse heritage: soil instability, subsidence, environmental damages The present volume aims at geotechnical engineers and academics involved in the preservation of monuments and historic sites worldwide

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of “volunteer mappers”. Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protection