Internal stress in structural steel members is an important parameter for steel structures in their design, construction, and service stages. However, it is hard to measure via traditional approaches. Among the existing non-destructive testing (NDT) methods, the ultrasonic method has received the most research attention. Longitudinal critically refracted (Lcr) waves, which propagate parallel to the surface of the material within an effective depth, have shown great potential as an effective stress measurement approach. This paper presents a systematic non-destructive evaluation method to determine the internal stress in in-service structural steel members using Lcr waves. Based on theory of acoustoelasticity, a stress evaluation formula is derived. Factor of stress to acoustic time difference is used to describe the relationship between stress and measurable acoustic results. A testing facility is developed and used to demonstrate the performance of the proposed method. Two steel members are measured by using the proposed method and the traditional strain gauge method for verification. Parametric studies are performed on three steel members and the aluminum plate to investigate the factors that influence the testing results. The results show that the proposed method is effective and accurate for determining stress in in-service structural steel members.

Guided Wave Testing (GWT) using novel Electromagnetic Acoustic Transducers (EMATs) is proposed for the inspection of large structures operating at high temperatures. To date, high temperature EMATs have been developed only for thickness measurements and they are not suitable for GWT. A pair of water-cooled EMATs capable of exciting and receiving Shear Horizontal (SH0) waves for GWT with optimal high temperature properties (up to 500 C) has been developed. Thermal and Computational Fluid Dynamic (CFD) simulations of the EMAT design have been performed and experimentally validated. The optimal thermal EMAT design, material selection and operating conditions were calculated. The EMAT was successfully tested regarding its thermal and GWT performance from ambient temperature to 500 C.

A practical compact solid-state terahertz imaging system is presented. Various beam guiding architectures were explored and hardware performance assessed to improve its compactness, robustness, multi-functionality and simplicity of operation. The system performance in terms of image resolution, signal-to-noise ratio, the electronic signal modulation versus optical chopper, is evaluated and discussed. The system can be conveniently switched between transmission and reflection mode according to the application. A range of imaging application scenarios was explored and images of high visual quality were obtained in both transmission and reflection mode.

Wheels are very important for the safety of a train. The diameter of the wheel is a significant parameter that needs regular inspection. Traditional methods only use the contact points of the wheel tread to fit the rolling round. However, the wheel tread is easily influenced by peeling or scraping. Meanwhile, the circle fitting algorithm is sensitive to noise when only three points are used. This paper proposes a dynamic measurement method based on structured-light vision. The axle of the wheelset and the tread are both employed. The center of the rolling round is determined by the axle rather than the tread only. Then, the diameter is calculated using the center and the contact points together. Simulations are performed to help design the layout of the sensors, and the influences of different noise sources are also analyzed. Static and field experiments are both performed, and the results show it to be quite stable and accurate.

Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms.

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures.

During the last decades, structural health monitoring (SHM) systems are used in order to detect damage in structures. We have developed a novel structural health monitoring approach, the so-called “effective structural health monitoring” (eSHM) system. The current SHM system is incorporated into a metallic structure by means of additive manufacturing (AM) and has the possibility to advance life safety and reduce direct operative costs. It operates based on a network of capillaries that are integrated into an AM structure. The internal pressure of the capillaries is continuously monitored by a pressure sensor. When a crack nucleates and reaches the capillary, the internal pressure changes signifying the existence of the flaw. The main objective of this paper is to evaluate the crack detection capacity of the eSHM system and crack location accuracy by means of various non-destructive testing (NDT) techniques. During this study, detailed acoustic emission (AE) analysis was applied in AM materials for the first time in order to investigate if phenomena like the Kaiser effect and waveform parameters used in conventional metals can offer valuable insight into the damage accumulation of the AM structure as well. Liquid penetrant inspection, eddy current and radiography were also used OPEN ACCESS Sensors 2015, 15 26710 in order to confirm the fatigue damage and indicate the damage location on un-notched four-point bending AM metallic specimens with an integrated eSHM system. It is shown that the eSHM system in combination with NDT can provide correct information on the damage condition of additive manufactured metals.

Quality of embedment of optical fibre sensors in carbon fibre-reinforced polymers plays an important role in the resultant properties of the composite, as well as for the correct monitoring of the structure. Therefore, availability of a tool able to check the optical fibre sensor-composite interaction becomes essential. High-resolution 3D X-ray Micro-Computed Tomography, or Micro-CT, is a relatively new non-destructive inspection technique which enables investigations of the internal structure of a sample without actually compromising its integrity. In this work the feasibility of inspecting the position, the orientation and, more generally, the quality of the embedment of an optical fibre sensor in a carbon fibre reinforced laminate at unit cell level have been proven.

Non-destructive eddy current testing (ECT) is widely used to examine structural defects in ferromagnetic pipe in the oil and gas industry. Implementation of giant magnetoresistance (GMR) sensors as magnetic field sensors to detect the changes of magnetic field continuity have increased the sensitivity of eddy current techniques in detecting the material defect profile. However, not many researchers have described in detail the structure and issues of GMR sensors and their application in eddy current techniques for nondestructive testing. This paper will describe the implementation of GMR sensors in non-destructive testing eddy current testing. The first part of this paper will describe the structure and principles of GMR sensors. The second part outlines the principles and types of eddy current testing probe that have been studied and developed by previous researchers. The influence of various parameters on the GMR measurement and a factor affecting in eddy current testing will be described in detail in the third part of this paper. Finally, this paper will discuss the limitations of coil probe and compensation techniques that researchers have applied in eddy current testing probes. A comprehensive review of previous studies on the application of GMR sensors in non-destructive eddy current testing also be given at the end of this paper.

The purpose of this paper is to present a multi-sensor approach to the detection and inspection of steel bars in reinforced concrete structures. In connection with our past experience related to non-destructive testing of different materials, we propose using two potentially effective methods: active infrared thermography with microwave excitation and the eddy current technique. In this article active infrared thermography with microwave excitation is analyzed both by numerical modeling and experiments. This method, based on thermal imaging, due to its characteriatics should be considered as a preliminary method for the assessment of relatively shallowly located steel bar reinforcements. The eddy current technique, on the other hand, allows for more detailed evaluation and detection of deeply located rebars. In this paper a series of measurement results, together with the initial identification of certain features of steel reinforcement bars will be presented.

The importance of the analysis of the radiofrequency signal is by now recognized in the field of tissue characterization via ultrasound. The RF signal contains a wealth of information and structural details that are usually lost in the B-Mode representation. The HyperSPACE (Hyper SPectral Analysis for Characterization in Echography) algorithm presented by the authors in previous papers for clinical applications is based on the radiofrequency ultrasonic signal. The present work describes the method in detail and evaluates its performance in a repeatable and standardized manner, by using two test objects: a commercial test object that simulates the human parenchyma, and a laboratory-made test object consisting of human blood at different dilution values. In particular, the sensitivity and specificity in discriminating different density levels were estimated. In addition, the robustness of the algorithm with respect to the signal-to-noise ratio was also evaluated.