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

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

GPR applications across Engineering and Geosciences disciplines in Italy: a review

In this paper, a review of the main ground-penetrating radar (GPR) applications, technologies, and methodologies used in Italy is given. The discussion has been organized in accordance with the field of application, and the use of this technology has been contextualized with cultural and territorial peculiarities, as well as with social, economic, and infrastructure requirements, which make the Italian territory a comprehensive large-scale study case to analyze. First, an overview on the use of GPR worldwide compared to its usage in Italy over the history is provided. Subsequently, the state of the art about the main GPR activities in Italy is deepened and divided according to the field of application. Notwithstanding a slight delay in delivering recognized literature studies with respect to other forefront countries, it has been shown how the Italian contribution is now aligned with the highest world standards of research and innovation in the field of GPR. Finally, possible research perspectives on the usage of GPR in Italy are briefly discussed

Automating Inspection of Tunnels With Photogrammetry and Deep Learning

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

A Systematic Review of Convolutional Neural Network-Based Structural Condition Assessment Techniques

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent years, a broad range of CNN architectures has been developed by researchers to accommodate the extent of lighting and weather conditions, the quality of images, the amount of background and foreground noise, and multiclass damage in the structures. This paper presents a detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance. The review is categorized into multiple classes depending on the specific application and development of CNNs applied to data obtained from a wide range of structures. The challenges and limitations of the existing literature are discussed in detail at the end, followed by a brief conclusion on potential future research directions of CNN in structural condition assessment

Analysis using surface wave methods to detect shallow manmade tunnels

Multi-method seismic surface wave approach was used to locate and estimate the dimensions of shallow horizontally-oriented cylindrical voids or manmade tunnels. The primary analytical methods employed were Attenuation Analysis of Rayleigh Waves (AARW), Surface Wave Common Offset (SWCO), and Spiking Filter (SF). Surface wave data were acquired at six study sites using a towed 24-channel land streamer and elastic-band accelerated weight-drop seismic source. Each site was underlain by one tunnel, nominally 1 meter in diameter and depth. The acquired surface wave data were analyzed automatically. Then interpretations compared to the field measurements to ascertain the degree of accuracy. The purpose of this research is to analyze the field response of Rayleigh waves to the presence of shallow tunnels. The SF technique used the variation of seismic signal response along a geophone array to determine void presence in the subsurface. The AARW technique was expanded for practical application, as suggested by Nasseri (2006), in order to indirectly estimate void location using a Normalized Energy Distance (NED) parameter for vertical tunnel dimension measurements and normalized Cumulative Logarithmic Decrement (CALD) values for horizontal tunnel dimension measurements. Confidence in tunnel detects is presented as a measure of NED signal strength. Conversely, false positives are reduced by AARW through analysis of sub-array data. The development of such estimations is a promising tool for engineers that require quantitative measurements of manmade tunnels in the shallow subsurface --Abstract, page iii

Optical fiber sensors in physical intrusion detection systems: A review

Fiber optic sensors have become a mainstream sensing technology within a large array of applications due to their inherent benefits. They are now used significantly in structural health monitoring, and are an essential solution for monitoring harsh environments. Since their first development over 30 years ago, they have also found promise in security applications. This paper reviews all of the optical fiber-based techniques used in physical intrusion detection systems. It details the different approaches used for sensing, interrogation, and networking, by research groups, attempting to secure both commercial and residential premises from physical security breaches. The advantages and the disadvantages of the systems are discussed, and each of the different perimeter protection methods is outlined, namely, in-ground, perimeter fence, and window and door protection. This paper reviews the progress in optical fiber-based intrusion detection techniques from the past through to the current state-of-the-art systems and identifies areas, which may provide opportunities for improvement, as well as proposing future directions in this field

Aeronautical Engineering. A continuing bibliography with indexes, supplement 156

This bibliography lists 288 reports, articles and other documents introduced into the NASA scientific and technical information system in December 1982

Investigating the dynamic response of rock mass to reservoir drainage at Grimsel test site, Switzerland, as an analogue for glacial retreat

An effective solution for the geologic disposal of nuclear waste, with no environmental risk (i.e. avoidance of harmful release of radioactive material), is a fundamental issue for the environment protection, and for the future continued reliance on nuclear power. Although geological disposal is considered as the best option, there are still elements of risk to be addressed, such as glacial retreat, which could impact the safety performance of a geological disposal facility. In this project two consecutive annual cycles of a reservoir in the Swiss Alps are used as a small scale analogue of the glacial retreat cycles, in order to investigate the response of granitic rock (as a host rock to a geologic disposal facility) to significant load changes. Assuming that the reservoir’s stress changes cause the fractured and weakened rock slopes to slip, I chose to use microseismic monitoring as a tool to monitor the reservoir induced seismicity. A seismic network was deployed in the tunnels adjacent to the reservoir and recorded continuously ground movement over a 3.5-year period (Nov 2014 – Aug 2018). In order to be able to detect microseismic slips in the acquired real field dataset I explore various algorithms from the literature and develop my own methodology. The two main problems my research focuses on are the length of the dataset (big data issues) and the signal to noise ratio of the events I want to detect (small magnitude events in a varying noisy background). My results show, albeit not all of the seismic signals were possible to locate or characterise, that the reservoir unloading increases the frequency of occurrence of microseismic events for a short time period in the region surrounding the reservoir. It is possible therefore that the construction of a geologic disposal facility will have a similar effect. However, the magnitudes of the induced events are very small and hence unlikely to have a significant effect as part of a safety case for a geologic disposal facility. The contributions of this thesis can be summarised to: (i) using a reservoir as a small-scale test site analogue for exploring the seismic hazard in radioactive deep geologic disposal facilities due to glacial retreat; (ii) sensor deployment design and sensor data cleaning with noise characterisation for microseismic monitoring over several years; (iii) proposal of a new algorithm (NpD) for detecting potential seismic signals under not well-constrained conditions and without requirement of a priori knowledge about the expected signal frequencies and amplitudes; (iv) the NpD detection algorithm and acquired 3.5 years dataset are made freely available; (v) detailed discussion of onset time picking and hypocentre localisation methodologies, where again novelty lies in using, comparing suitability and adjusting a number of well-known approaches for the purposes of my project; (vi) compilation of a seismic catalogue related to the dynamic response of the rock mass to reservoir drainage.An effective solution for the geologic disposal of nuclear waste, with no environmental risk (i.e. avoidance of harmful release of radioactive material), is a fundamental issue for the environment protection, and for the future continued reliance on nuclear power. Although geological disposal is considered as the best option, there are still elements of risk to be addressed, such as glacial retreat, which could impact the safety performance of a geological disposal facility. In this project two consecutive annual cycles of a reservoir in the Swiss Alps are used as a small scale analogue of the glacial retreat cycles, in order to investigate the response of granitic rock (as a host rock to a geologic disposal facility) to significant load changes. Assuming that the reservoir’s stress changes cause the fractured and weakened rock slopes to slip, I chose to use microseismic monitoring as a tool to monitor the reservoir induced seismicity. A seismic network was deployed in the tunnels adjacent to the reservoir and recorded continuously ground movement over a 3.5-year period (Nov 2014 – Aug 2018). In order to be able to detect microseismic slips in the acquired real field dataset I explore various algorithms from the literature and develop my own methodology. The two main problems my research focuses on are the length of the dataset (big data issues) and the signal to noise ratio of the events I want to detect (small magnitude events in a varying noisy background). My results show, albeit not all of the seismic signals were possible to locate or characterise, that the reservoir unloading increases the frequency of occurrence of microseismic events for a short time period in the region surrounding the reservoir. It is possible therefore that the construction of a geologic disposal facility will have a similar effect. However, the magnitudes of the induced events are very small and hence unlikely to have a significant effect as part of a safety case for a geologic disposal facility. The contributions of this thesis can be summarised to: (i) using a reservoir as a small-scale test site analogue for exploring the seismic hazard in radioactive deep geologic disposal facilities due to glacial retreat; (ii) sensor deployment design and sensor data cleaning with noise characterisation for microseismic monitoring over several years; (iii) proposal of a new algorithm (NpD) for detecting potential seismic signals under not well-constrained conditions and without requirement of a priori knowledge about the expected signal frequencies and amplitudes; (iv) the NpD detection algorithm and acquired 3.5 years dataset are made freely available; (v) detailed discussion of onset time picking and hypocentre localisation methodologies, where again novelty lies in using, comparing suitability and adjusting a number of well-known approaches for the purposes of my project; (vi) compilation of a seismic catalogue related to the dynamic response of the rock mass to reservoir drainage