Trend Deviation Analysis for Automated Detection of Defects in GPR Data for Road Condition Surveys

This paper presents a novel approach for automated detection of defects and structural changes in GPR data acquired in HMA (Hot Mix Asphalt) road surveys. Unlike the majority of existing approaches for road GPR data processing that are mainly used for extraction of layer profile information, the proposed method focuses on automated identification of significant deviations in subsurface structure and material properties. It is based on the detection of variations in intensity trends of longitudinal lines of interpolated B-scans that are characterized by deviations above a defined threshold. The outputs include mapped defects and deterioration areas together with the locations of detected changes in road structure design

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

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Radio frequency non-destructive testing and evaluation of defects under insulation

PhD ThesisThe use of insulation such as paint coatings has grown rapidly over the past decades. However, defects and corrosion under insulation (CUI) still present challenges for current non-destructive testing and evaluation (NDT&E) techniques. One of such challenges is the large lift-off introduced by thick insulation layer. Inaccessibility due to insulation leads corrosion and defects to be undetected, which can lead to catastrophic failure. Furthermore, lift-off effects due to the insulation layers reduce the sensitivities. The limitations of existing NDT&E techniques heighten the need for novel approaches to the characterisation of corrosion and defects under insulation. This research project is conducted in collaboration with International Paint®, and a radio frequency non-destructive evaluation for monitoring structural condition is proposed. High frequency (HF) passive RFID in conjunction with microwave NDT is proposed for monitoring and imaging under insulation. The small-size, battery-free and cost-efficient nature of RFID makes it attractive for long-term condition monitoring. To overcome the limitations of RFID-based sensing system such as effective monitoring area and lift-off tolerance, microwave NDT is proposed for the imaging of larger areas under thick insulation layers. Experimental studies are carried out in conjunction with specially designed mild steel sample sets to demonstrate the detection capabilities of the proposed systems. The contributions of this research can be summarised as follows. Corrosion detection using HF passive RFID-based sensing and microwave NDT is demonstrated in experimental feasibility studies considering variance in surface roughness, conductivity and permeability. The lift-off effects introduced by insulation layers are reduced by applying feature extraction with principal component analysis and non-negative matrix factorisation. The problem of thick insulation layers is overcome by employing a linear sweep frequency with PCA to improve the sensitivity and resolution of microwave NDT-based imaging. Finally, the merits of microwave NDT are identified for imaging defects under thick insulation in a realistic test scenario. In conclusion, HF passive RFID can be adapted for long term corrosion monitoring of steel under insulation, but sensing area and lift-off tolerance are limited. In contrast, the microwave NDT&E has shown greater potential and capability for monitoring corrosion and defects under insulation

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

Life-Cycle Management of Civil and Marine Structures under Fatigue and Corrosion Effects

Infrastructure systems are under continuous deteriorating effects due to various environmental and mechanical stressors. These effects can be generated by sudden threats such as earthquakes, tornadoes, blast, and fire, or gradual deterioration due to fatigue and corrosion. Moreover, as indicated in the 2013 American Society of Civil Engineers (ASCE) Report Card of America\u27s Infrastructure, the United States\u27 infrastructure systems are highly deteriorating with a required estimated investment of 3.6 trillion USD to improve their condition within the next seven years. Given the limited financial resources, rational methodologies are required to support the optimum budget allocation while maintaining maximum possible safety levels. Uncertainties associated with the performance prediction, damage initiation and propagation, damage detection capabilities, and the effect of maintenance and retrofit on the structural performance add more challenges to this allocation process. In this context, life-cycle engineering provides rational means to optimize budget allocation and manage an infrastructure system starting from the initial design and construction to dismantling and replacing the system at the end of its service life.This study provides novel management methodologies which support the decision-making process for civil and marine large-scale structural systems under fatigue and corrosion deterioration. Multi-objective optimization models that seek the optimal trade-offs between conflicting life-cycle management (LCM) aspects such as the life-cycle cost and the projected service life are proposed. These models provide the optimum intervention schedules (e.g., inspections and maintenance actions) which fulfil the LCM goals. For the first time in the field of life-cycle management, an approach capable of establishing the optimum inspection, monitoring, and repair actions simultaneously is proposed. Maximizing the expected service life, minimizing the total life-cycle cost, minimizing the maintenance delay, and maximizing the probability of damage detection are examples of the considered optimization goals. It is shown that the implementation of optimum solutions resulting from the proposed management plans can significantly reduce the life-cycle cost. A methodology for planning inspection actions for bridges with multiple critical fatigue details is proposed. This is considered a step forward from the traditional approaches which are only capable of considering one critical fatigue detail. Additionally, this study provides methodologies for the reliability-based performance evaluation of structures under fatigue deterioration. Furthermore, rational approaches which make use of structural health monitoring (SHM) and non-destructive inspection information for the near real-time decision making for deteriorating structures are proposed. Specifically, an approach to obtain the fatigue reliability of aluminium high-speed naval vessels based on SHM information is proposed. By using the proposed approach, the effect of individual operational conditions encountered by the ship on the overall fatigue damage accumulation can be quantified. This quantification is not possible by using the traditional fatigue life estimation methods. Probabilistic reliability methods and Monte Carlo simulation are implemented to account for uncertainties associated with different aspects of the LCM process. Existing large-scale structural systems are analysed to demonstrate the feasibility and effectiveness of the proposed methodologies