Adaptive Wavelet Neural Network for Terrestrial Laser Scanner-Based Crack Detection

Objective, accurate, and fast assessment of civil infrastructure conditions is critical to timely assess safety risks. Current practices rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field, which are labor intensive, subject to personal judgment and experience, and prone to error. Terrestrial laser scanners (TLS) are promising for automatically identifying structural condition indicators, as they are capable of providing coverage for large areas with accuracy at long ranges. Major challenges in using this technology are in storing significant amount of data and extracting appropriate features enabling condition assessment. This paper proposes a novel adaptive wavelet neural network (WNN)-based approach to compress data into a combination of low- and high-resolution surfaces, and automatically detect concrete cracks and other forms of damage. The adaptive WNN is designed to sequentially self-organize and self-adapt in order to construct an optimized representation. The architecture of the WNN is based on a single-layer neural network consisting of Mexican hat wavelet functions. The strategy is to first construct a low-resolution representation of the point cloud, then detect and localize anomalies, and finally construct a high-resolution representation around these anomalies to enhance their characterization. The approach was verified on four cracked concrete specimens. The experimental results show that the proposed approach was capable of fitting the point cloud, and of detecting and fitting the crack. The results demonstrated data compression of 99.4%, 72.2%, 92.4% and 78.9% for the four specimens when using low resolution fit for crack detection. For specimens 1, 2 and 3, 97.1%, 42.5% and 63.9% compression of data were obtained for crack localization, which is a significant improvement over previous TLS based crack detection and measurement approaches. Using the proposed method for crack detection would enable automatic and remote assessment of structural conditions. This would, in turn, result in reducing costs associated with infrastructure management, and improving the overall quality of our infrastructure by enhancing maintenance operations

Robot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework; 35214292

Robot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visual and structural features, make traditional robotics methods based on cameras, lasers, or wheel encoders unreliable. Fortunately, tunnels provide other types of valuable information that can be used for localization purposes. On the one hand, radio frequency signal propagation in these types of scenarios shows a predictable periodic structure (periodic fadings) under certain settings, and on the other hand, tunnels present structural characteristics (e.g., galleries, emergency shelters) that must comply with safety regulations. The solution presented in this paper consists of detecting both types of features to be introduced as discrete sources of information in an alternative graph-based localization approach. The results obtained from experiments conducted in a real tunnel demonstrate the validity and suitability of the proposed system for inspection applications. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Automated manufacturing of smart tunnel segments

Tunnels, essential infrastructures, require regular inspections and maintenance to ensure their prolonged service life. While conventional methods heavily rely on expert human manpower, modern tunnel structural monitoring techniques, such as sensor-based Structural Health Monitoring (SHM), are increasingly utilized in both existing and newly constructed tunnels. Despite providing valuable insights into post-construction structural behaviour, these methods often overlook the behaviour of individual precast elements, such as tunnel segments, before their installation. This thesis explores the concept of smart tunnel segments instrumented by robotic means to address this gap. In this project lab-scale tunnel segments were instrumented using a 6-axis robotic arm making them smart enabling their properties to be tracked from manufacturing through the operational phase of the tunnel. The research involves a comprehensive review of current tunnel instrumentation practices, identifying structural strains as the most monitored parameters. Vibrating Wire Strain Gauges (VWSGs) were identified as the most suitable sensors for this application due to their compatibility with a modular system and superior long-term properties, especially when embedded in concrete. Furthermore, the study identifies untapped potential in fully automated precast factories and proposes repurposing certain features of industrial robots to deploy VWSGs nodes via robotic pick-and-place. Through a novel evaluation framework, the research demonstrates the effectiveness of automated sensor deployment by robots. This includes the robotic installation of a pair of embedded VWSGs in lab-scale tunnel segments, thereby rendering them "smart," and subjecting them to repetitive flexural loadings to evaluate their performance and accuracy. The calculated strain transfer exhibits consistent and repeatable behaviour across segments. Finally, the thesis outlines the economic justification for smart segments, which outperform traditional on-site wired and wireless alternatives, thereby contributing to a more comprehensive and cost-effective tunnel maintenance strategyTunnels, essential infrastructures, require regular inspections and maintenance to ensure their prolonged service life. While conventional methods heavily rely on expert human manpower, modern tunnel structural monitoring techniques, such as sensor-based Structural Health Monitoring (SHM), are increasingly utilized in both existing and newly constructed tunnels. Despite providing valuable insights into post-construction structural behaviour, these methods often overlook the behaviour of individual precast elements, such as tunnel segments, before their installation. This thesis explores the concept of smart tunnel segments instrumented by robotic means to address this gap. In this project lab-scale tunnel segments were instrumented using a 6-axis robotic arm making them smart enabling their properties to be tracked from manufacturing through the operational phase of the tunnel. The research involves a comprehensive review of current tunnel instrumentation practices, identifying structural strains as the most monitored parameters. Vibrating Wire Strain Gauges (VWSGs) were identified as the most suitable sensors for this application due to their compatibility with a modular system and superior long-term properties, especially when embedded in concrete. Furthermore, the study identifies untapped potential in fully automated precast factories and proposes repurposing certain features of industrial robots to deploy VWSGs nodes via robotic pick-and-place. Through a novel evaluation framework, the research demonstrates the effectiveness of automated sensor deployment by robots. This includes the robotic installation of a pair of embedded VWSGs in lab-scale tunnel segments, thereby rendering them "smart," and subjecting them to repetitive flexural loadings to evaluate their performance and accuracy. The calculated strain transfer exhibits consistent and repeatable behaviour across segments. Finally, the thesis outlines the economic justification for smart segments, which outperform traditional on-site wired and wireless alternatives, thereby contributing to a more comprehensive and cost-effective tunnel maintenance strateg

A path for microsecond structural health monitoring for high-rate nonstationary time-varying systems

In this dissertation, a new area of research identified as high-rate state estimation is established along with its associated research challenges, and a path for a solution is provided. High-rate dynamic systems are defined as systems being exposed to highly dynamic environments that are comprised of high-rate and high-amplitude events (greater than 100 g for a duration under 100 ms). Engineering systems experiencing high-rate dynamic events, including airbag, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. This task of high-rate state estimation is particularly challenging for real-time applications, where the rate of an observer\u27s convergence needs to be in the microsecond range. On the other hand, the benefits include a high potential to reduce economic loss and save lives. The problem is discussed in-depth addressing the fundamental challenges of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are found to be advantageous due to their adaptability to complex problems and lack of dependence on system model. An adaptive neuro-observer is designed to examine the particular challenges in selecting an appropriate input space for high-rate state estimation to increase convergence rates of adaptive observers. It is found that the choice of inputs has a more significant influence on the observer\u27s performance for high-rate dynamics when compared against a lower rate environment. Additionally, misrepresentation of a system dynamics through incorrect input spaces produces large errors in the estimation, which could potentially trick the decision making process in a closed-loop system in making bad judgments. A novel adaptive wavelet neural network (WNN)-based approach to compress data into a combination of low- and high-resolution surfaces is proposed to automatically detect concrete cracks and other forms of damage. The adaptive WNN is designed to sequentially self-organize and self-adapt in order to construct an optimized representation. The architecture of the WNN is based on a single-layer neural network consisting of Mexican hat wavelet functions. The approach was verified on four cracked concrete specimens. A variable input space concept is proposed for incorporating data history of high-rate dynamics, with the objective to produce an optimal representation of the system of interest minimizing convergence times of adaptive observers. Using the embedding theory, the algorithm sequentially selects and adapts a vector of inputs that preserves the essential dynamics of the high-rate system. The variable input space is integrated with a WNN, which constitutes a variable input observer. The observer is simulated using experimental data from a high-rate system. Different input space adaptation methods are studied and the performance is compared against an optimized fixed input strategy. The variable input observer is further studied in a hybrid model-/data-driven formulation, and results demonstrate significant improvement in performance gained from the added physical knowledge. An experimental test bed, developed to validate high-rate structural health monitoring (SHM) methods in a controllable and repeatable laboratory environment, is modeled as a clamped-pinned-free beam with mass at the free end. The Euler-Bernoulli beam theory is applied to this unique configuration to develop analytical solutions of the system. The transverse vibration of a clamped-pinned-free beam with a point mass at the free end is discussed in detail. Results are derived for varying pin locations and mass values. Eigenvalue plots of the first five modes are presented along with their respective mode shapes. The theoretical calculations are experimentally validated and discussed

Autonomous robotic system for tunnel structural inspection and assessment

International audienc