Evaluation of the Size of a Defect in Reinforcing Steel Using Magnetic Flux Leakage (MFL) Measurements.

This study aimed to evaluate 2D magnetic flux leakage (MFL) signals (Bx, By) in D19-size reinforcing steel with several defect conditions. The magnetic flux leakage data were collected from the defected and new specimens using an economically designed test setup incorporating permanent magnets. A two-dimensional finite element model was numerically simulated using COMSOL Multiphysics to validate the experimental tests. Based on the MFL signals (Bx, By), this study also intended to improve the ability to analyze defect features such as width, depth, and area. Both the numerical and experimental results indicated a high cross-correlation with a median coefficient of 0.920 and a mean coefficient of 0.860. Using signal information to evaluate defect width, the x-component (Bx) bandwidth was found to increase with increasing defect width and the y-component (By) amplitude rise with increasing depth. In this two-dimensional MFL signal study, both parameters of the two-dimensional defects (width and depth) affected each other and could not be evaluated individually. The defect area was estimated from the overall variation in the signal amplitude of the magnetic flux leakage signals with the x-component (Bx). The defect areas showed a higher regression coefficient (R2 = 0.9079) for the x-component (Bx) amplitude from the 3-axis sensor signal. It was determined that defect features are positively correlated with sensor signals.

Evaluation of Early Concrete Damage Caused by Chloride-Induced Steel Corrosion Using a Deep Learning Approach Based on RNN for Ultrasonic Pulse Waves.

The objective of this study is to explore the feasibility of using ultrasonic pulse wave measurements as an early detection method for corrosion-induced concrete damages. A series of experiments are conducted using concrete cube specimens, at a size of 200 mm, with a reinforcing steel bar (rebar) embedded in the center. The main variables include the water-to-cement ratio of the concrete (0.4, 0.5, and 0.6), the diameter of the rebar (10 mm, 13 mm, 19 mm, and 22 mm), and the corrosion level (ranging from 0% to 20% depending on rebar diameter). The impressed current technique is used to accelerate corrosion of rebars in concrete immersed in a 3% NaCl solution. Ultrasonic pulse waves are collected from the concrete specimens using a pair of 50 kHz P-wave transducers in the through-transmission configuration before and after the accelerated corrosion test. Deep learning techniques, specifically three recurrent neural network (RNN) models (long short-term memory, gated recurrent unit, and bidirectional long short-term memory), are utilized to develop a classification model for early detection of concrete damage due to rebar corrosion. The performance of the RNN models is compared to conventional ultrasonic testing parameters, namely ultrasonic pulse velocity and signal consistency. The results demonstrate that the RNN method outperforms the other two methods. Among the RNN methods, the bidirectional long short-term memory RNN model had the best performance, achieving an accuracy of 74% and a Cohen's kappa coefficient of 0.48. This study establishes the potentiality of utilizing deep learning of ultrasonic pulse waves with RNN models for early detection of concrete damage associated with steel corrosion.

Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP.

Chronic kidney disease (CKD) is a condition distinguished by structural and functional changes to the kidney over time. Studies show that 10% of adults worldwide are affected by some kind of CKD, resulting in 1.2 million deaths. Recently, CKD has emerged as a leading cause of mortality worldwide, making it necessary to develop a Computer-Aided Diagnostic (CAD) system to diagnose CKD automatically. Machine Learning (ML) based CAD system can be used by a clinician to automatically diagnoses mass people. Since ML models are considered a black box, it is also necessary to expose influential causes behind a model's prediction of a particular output. So that, a doctor can make a more rational decision based on the model's output and analysis of the features influence on the model. In this paper, we have used the XGBoost as the ML classifier to predict whether a patient has CKD or not. Using the XGBoost classifier, we have obtained an accuracy, precision, recall, and F1 score of [Formula: see text] and [Formula: see text] respectively using all [Formula: see text] features. Furthermore, we have used Biogeography Based Optimization (BBO) algorithm to find an effective subset of the features. The BBO algorithm selected almost half of the initial features. We have obtained an accuracy, precision, recall, and F1 score of [Formula: see text] and [Formula: see text] respectively using only 13 features selected by the BBO algorithm. Finally, we have explained the impact of the feature on the ML models using the SHapley Additive exPlanations (SHAP) analysis. Using SHAP analysis and BBO algorithm, we have found that hemoglobin and albumin mostly contribute to the detection of CKD.

Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves.

This study investigated the applicability of using ultrasonic wave signals in detecting early fire damage in concrete. This study analyzed the reliability of using the linear (wave velocity) and nonlinear (coherence) parameters from ultrasonic pulse measurements and the applicability of machine learning in assessing the thermal damage of concrete cylinders. While machine learning has been used in some damage detections for concrete, its feasibility has not been fully investigated in classifying thermal damage. Data was collected from laboratory experiments using concrete specimens with three different water-to-binder ratios (0.54, 0.46, and 0.35). The specimens were subjected to different target temperatures (100 °C, 200 °C, 300 °C, 400 °C, and 600 °C) and another set of cylinders was subjected to room temperature (20 °C) to represent the normal temperature condition. It was observed that P-wave velocities increased by 0.1% to 10.44% when the concretes were heated to 100 °C, and then decreased continuously until 600 °C by 48.46% to 65.80%. Conversely, coherence showed a significant decrease after exposure to 100 °C but had fluctuating values in the range of 0.110 to 0.223 thereafter. In terms of classifying the thermal damage of concrete, machine learning yielded an accuracy of 76.0% while the use of P-wave velocity and coherence yielded accuracies of 30.26% and 32.31%, respectively.

Finding the influential clinical traits that impact on the diagnosis of heart disease using statistical and machine-learning techniques.

In recent years, the omnipresence of cardiac problems has been recognized as an epidemic. With the correct and quick diagnosis, both mortality and morbidity from cardiac disorders can be dramatically reduced. However, frequent medical check-ups are pricey and out of reach for a large number of people, particularly those living in low-income areas. In this paper, certain time-honored statistical techniques are used to determine the factors that lead to heart disease. Also, the findings were validated using various promising machine learning tools. Feature importance approach was employed to rank the clinical parameters of the patients based on the correlation of heart disease. In the case of statistical investigations, nonparametric tests such as the Mann Whitney U test and the Chi square test, as well as correlation analysis with Pearson correlation and Spearman Correlation were used. For additional validation, seven of the potential feature important based ML algorithms were applied. Moreover, Borda count was implemented to acknowledge the combined observation of those ML models. On top of that, SHAP value was calculated as a feature importance technique and for detailed evaluation. This research reveals two aspects of heart disease diagnosis.We found that eight clinical traits are sufficient to diagnose cardiac disorders, in which three traits are the most important sign of heart disease. One of the discoveries of this investigation uncovered chest pain, number of major blood vessels, thalassemia, age, maximum heart rate, cholesterol, oldpeak, and sex as sufficient clinical signs of individuals for the diagnosis of cardiac disorders. Over the above, considering the findings of all three approaches, chest pain, the number of major blood vessels, and thalassemia were identified as the prime factors of heart disease. The research also found, fasting blood sugar does not have a direct impact on cardiac disease. These findings will have the potency to be incredibly useful in clinical investigations as well as risk assessment for patients. Limiting the most critical features can have a significant impact on the diagnosis of heart disease and reduce the severity of health risks and death of patients.

IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review.

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people's lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems' effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

Electrical Resistivity Measurements for Nondestructive Evaluation of Chloride-Induced Deterioration of Reinforced Concrete-A Review.

The objective of this study is to review, evaluate, and compare the existing research and practices on electrical resistivity as a nondestructive technique in evaluating chloride-induced deterioration of reinforced concrete elements in buildings and civil infrastructure systems. First, this paper summarizes the different measurement techniques for gathering electrical resistivity (ER) values on concrete. Second, comparison analyses are performed to review the correlation of ER to different parameters representing corrosive environment and activity of steel corrosion in concrete, such as degree of water saturation, chloride penetration and diffusivity, and corrosion rate. In addition, this research enumerates and individually discusses the different environmental and interference factors that are not related to the electrochemical process of steel corrosion in concrete but directly affect the ER measurements, including temperature, the presence of steel reinforcement, cracks and delamination defects, specimen geometry, and concrete composition. Lastly and most importantly, discussions are made to determine the current gap of knowledge, to improve the utilization of this method in field and laboratory measurements, and future research.

Prediction of Compressive Strength of Partially Saturated Concrete Using Machine Learning Methods.

The aim of this research is to recommend a set of criteria for estimating the compressive strength of concrete under marine environment with various saturation and salinity conditions. Cylindrical specimens from three different design mixtures are used as concrete samples. The specimens are subjected to different saturation levels (oven-dry, saturated-surface dry and three partially dry conditions: 25%, 50% and 75%) on water and water-NaCl solutions. Three parameters (P- and S-wave velocities and electrical resistivity) of concrete are measured using two NDT equipment in the laboratory while two parameters (density and water-to-binder ratio) are obtained from the design documents of the concrete cylinders. Three different machine learning methods, which include, artificial neural network (ANN), support vector machine (SVM) and Gaussian process regression (GPR), are used to obtain multivariate prediction models for compressive strength from multiple parameters. Based on the R-squared value, ANN results in the highest accuracy of estimation while GPR gives the lowest root-mean-squared error (RMSE). Considering both the data analysis and practicality of the method, the prediction model based on two NDE parameters (P-wave velocity measurement and electrical resistivity) and one design parameter (water-to-binder ratio) is recommended for assessing compressive strength under marine environment.

Evaluation of a Concrete Slab Track with Debonding at the Interface between Track Concrete Layer and Hydraulically Stabilized Base Course Using Multi-Channel Impact-Echo Testing.

Multi-channel Impact-echo (IE) testing was used to evaluate debonding defects at the interface between track concrete layer, TCL, and hydraulically stabilized base course, HSB, in a real scale mockup model of concrete slab tracks for Korea high-speed railway (KHSR) system. The mockup model includes three debonding defects that were fabricated by inserting three 400 mm by 400 mm (length and width) thin plastic foam boards with three different thicknesses of 5 mm, 10 mm, and 15 mm, before casting concrete in TCL. Multi-channel IE signals obtained over solid concrete and debonding defects were reduced to three critical IE testing parameters (the velocity of concrete, peak frequency, and Q factor). Bilinear classification models were used to evaluate the individual and a combination of the characteristic parameters. It was demonstrated that the best evaluation performance was obtained by using average peak frequency or the combination of average peak frequency and average Q factor, obtained by eight accelerometers in the multi-channel IE device. The results and discussion in this study would improve the understanding of characteristics of multiple IE testing parameters in concrete slab tracks and provide a fundamental basis to develop an effective prediction model of non-destructive evaluation for debonding defects at the interface between TCL and HSB in concrete slab tracks.

Electrochemical Deposition Treatment (EDT) as a Comprehensive Rehabilitation Method for Corrosion-Induced Deterioration in Concrete with Various Severity Levels.

The primary purposes of this study are to investigate the feasibility of electrochemical deposition treatment (EDT) as a comprehensive rehabilitation method for corrosion-induced deterioration in reinforced concrete with various severity levels, and to propose a guideline for the determination of critical factors to advance EDT. This study includes three experimental phases, each of which simulates the initiation (de-passivation), propagation (high corrosion activity), and acceleration (formation of a surface-breaking crack) periods of corrosion-induced deterioration. After completion of a series of accelerated corrosion tests, damaged concrete samples with different severity levels are rehabilitated by a series of EDT processes using a MgCl2 solution in an electrolyte. The main variables for this experiment are the concentration levels (0, 0.3, 1.0 and 3.0 M) of a MgCl2 solution for test phase 1, charging time (0, 2, and 7 days) in EDT for test phase 2, and configuration of pre- and post-treatment processes in EDT for test phase 3. The rehabilitation performance of EDT is evaluated by analyzing the AC impedance properties of the steel-and-concrete interface using electrochemical impedance spectroscopy (EIS) for the test phases 1 and 2, and microscopic alternation in concrete cracks using optical microscopic image and SEM/EDX. It is demonstrated that EDT is an effective method for preventing and mitigating corrosion-induced deterioration in the initiation and rust propagation periods of corrosion and for repairing (closing and filling) a corrosion-induced surface-breaking crack in the acceleration phase of corrosion. Corrosion-resistant performance of concrete increases as the concentration levels of a MgCl2 solution in an electrolyte increases and as the charging time in EDT increases. In addition, a post-treatment process (applying a NaOH solution) after the electrochemical deposition process significantly improves crack-repairing performance of EDT.

Effect of the Geometrical Constraints to the Wenner Four-Point Electrical Resistivity Test of Reinforced Concrete Slabs.

The main objectives of this study are to evaluate the effect of geometrical constraints of plain concrete and reinforced concrete slabs on the Wenner four-point concrete electrical resistivity (ER) test through numerical and experimental investigation and to propose measurement recommendations for laboratory and field specimens. First, a series of numerical simulations was performed using a 3D finite element model to investigate the effects of geometrical constraints (the dimension of concrete slabs, the electrode spacing and configuration, and the distance of the electrode to the edges of concrete slabs) on ER measurements of concrete. Next, a reinforced concrete slab specimen (1500 mm (width) by 1500 mm (length) by 300 mm (thickness)) was used for experimental investigation and validation of the numerical simulation results. Based on the analytical and experimental results, it is concluded that measured ER values of regularly shaped concrete elements are strongly dependent on the distance-to-spacing ratio of ER probes (i.e., distance of the electrode in ER probes to the edges and/or the bottom of the concrete slabs normalized by the electrode spacing). For the plain concrete, it is inferred that the thickness of the concrete member should be at least three times the electrode spacing. In addition, the distance should be more than twice the electrode spacing to make the edge effect almost negligible. It is observed that the findings from the plain concrete are also valid for the reinforced concrete. However, for the reinforced concrete, the ER values are also affected by the presence of reinforcing steel and saturation of concrete, which could cause disruptions in ER measurements.

Electrical Resistivity Measurements of Reinforced Concrete Slabs with Delamination Defects.

The main objectives of this research are to evaluate the effects of delamination defects on the measurement of electrical resistivity of reinforced concrete slabs through analytical and experimental studies in the laboratory, and to propose a practical guide for electrical resistivity measurements on concrete with delamination defects. First, a 3D finite element model was developed to simulate the variation of electric potential field in concrete over delamination defects with various depths and lateral sizes. Second, for experimental studies, two reinforced concrete slab specimens (1500 mm (width) by 1500 mm (length) by 300 mm (thickness)) with artificial delamination defects of various dimensions and depths were fabricated. Third, the electrical resistivity of concrete over delamination defects in the numerical simulation models and the two concrete slab specimens were evaluated by using a 4-point Wenner probe in accordance with AASHTO (American Association of State Highway and Transportation Office) T-358. It was demonstrated from analytical and experimental studies in this study that shallow (50 mm depth) and deep (250 mm depth) delamination defects resulted in higher and lower electrical resistivity (ER) values, respectively, as compared to measurements performed on solid concrete locations. Furthermore, the increase in size of shallow defects resulted in an increase in concrete resistivity, whereas the increase in sizes of deep delamination defects yielded opposite results. In addition, measurements done directly above the steel reinforcements significantly lowered ER values. Lastly, it was observed from experimental studies that the effect of delamination defects on the values of electrical resistivity decreases as the saturation level of concrete increases.

Interpretation of Impact-Echo Testing Data from a Fire-Damaged Reinforced Concrete Slab Using a Discrete Layered Concrete Damage Model.

The main objectives of this study are to investigate the spectral responses of a fire-damaged concrete slab using Impact-echo (IE) testing, and to develop a simplified model for interpreting the frequency shift due to heat-induced concrete damage after the fire. For these purposes, a reinforced concrete slab specimen (1000 mm (width) by 5000 mm (length) by 210 mm (thickness)) was fabricated in the laboratory. Heat damage in the concrete slab specimen was induced by exposing the bottom of the specimen to the temperatures corresponding to the standard fire curve described in the ASTM E 119 for 3 h. Impact-echo testing was performed on the bottom surface of the concrete slab specimen before and after inducing the fire damage. It was observed that the spectral responses of the fire-damaged concrete were dominated by several non-propagating waves, which resulted in main peak frequencies around 4500 Hz and 5100 Hz. A discrete layered concrete damage model developed in this study was used to reconstruct the variation of the P-wave velocity with the depth of the fire-damaged concrete. It was demonstrated that the predicted P-wave velocity profile using the simplified model showed a good agreement with the measured values from the five core samples, which measured 100 mm (diameter) by 200 mm (height) cylinders, using ultrasonic pulse velocity (UPV) measurements at eight different depths. In addition, the peak frequencies predicted by the simplified model were consistent with the measured peak frequencies. The experimental results in this study demonstrated that IE testing is effective for evaluating the post-fire damage of reinforced concrete slabs. Particularly, the simplified model in this study can be effective for better interpreting the spectral responses of fire-damaged concrete slabs by IE testing.

A Practical Approach to Condition Assessment of Asphalt-Covered Concrete Bridge Decks on Korean Expressways by Dielectric Constant Measurements Using Air-Coupled GPR.

The main objectives of this study are to investigate the variations of the dielectric constant of concrete on Korean expressways by using a 1 GHz air-coupled Ground Penetrating Radar (GPR) system and to develop a practical approach to the condition assessment of concrete bridge decks with asphalt overlay on Korean expressways by dielectric constant measurements. A total of 684 GPR investigations of 601 actual concrete bridge decks, which are in service between 2 and 43 years, were carried out during the period between 1999 and 2013. Statistical analysis revealed that the dielectric constant of asphalt-covered concrete bridge decks reduced with service age and this trend continued until service age of over 40 years. As a result, this study provides a practical dielectric constant curve that could be used for condition evaluation of top concrete in asphalt-covered bridge decks with consideration of concrete age. Based on regression analyses of the GPR field survey data and experiences through the field survey, a double cut-off dielectric constant criterion was proposed for condition assessment of asphalt-covered concrete bridge decks on Korean expressways. In addition, a GPR field survey was performed at an actual bridge on the Yeongdong expressway in Korea to test the proposed GPR signal interpretation method. The field survey results provide fundamental data to better understand the variation of the dielectric constant of concrete in actual bridges with asphalt overlay and to develop a practical approach to condition assessment of asphalt-covered concrete bridge decks on Korean expressways by dielectric constant measurements using air-coupled GPR.

Effects of Saturation Levels on the Ultrasonic Pulse Velocities and Mechanical Properties of Concrete.

The main objective of this research is to investigate the effect of water content in concrete on the velocities of ultrasonic waves (P- and S-waves) and mechanical properties (elastic modulus and compressive strength) of concrete. For this study, concrete specimens (100 mm × 200 mm cylinders) were fabricated with three different water-to-binder ratios (0.52, 0.35, and 0.26). These cylinders were then submerged in water to be saturated in different degrees from 25% to 100% with an interval of 25% saturation. Another set of cylinders was also oven-dried to represent the dry condition. The dynamic properties of concrete were then assessed using a measurement of elastic wave accordance with ASTM C597-16 and using resonance tests following ASTM C215-19, before and after immersion in water. The static properties of saturated concrete were also assessed by the uniaxial compressive testing according to ASTM C39/C39M-20 and ASTM C469/C469M-14. It was observed that the saturation level of concrete affected the two ultrasonic wave velocities and the two static mechanical properties of concrete in various ways. The relationship between P-wave velocity and compressive strength of concrete was highly sensitive to saturation condition of concrete. In contrast, S-wave velocity of concrete was closely correlated with compressive strength of concrete, which was much less sensitive to water saturation level compared to P-wave velocity of concrete. Finally, it was noticed that water saturation condition only little affects the relationship between the dynamic and elastic moduli of elasticity of concrete studies in this study.

A Practical Guide to Source and Receiver Locations for Surface Wave Transmission Measurements across a Surface-Breaking Crack in Plate Structures.

The main objectives of this study are to investigate the interference of multiple bottom reflected waves in the surface wave transmission (SWT) measurements in a plate and to propose a practical guide to source-and-receiver locations to obtain reliable and consistent SWT measurements in a plate. For these purposes, a series of numerical simulations, such as finite element modelling (FEM), are performed to investigate the variation of transmission coefficient of surface waves across a surface-breaking crack in various source-to-receiver configurations in plates. Main variables in this study include the crack depths (0, 10, 20, 30, 40 and 50 mm), plate thicknesses (150, 200, 300, 400 and 800 mm), source-to-crack distances (100, 150, 200, 250 and 300 mm) and receiver-to-crack distances. The validity of numerical simulation results was verified by comparison with results from experiments using Plexiglas specimens using two types of noncontact sensors (laser vibrometer and air-coupled sensor) in the laboratory. Based on simulation and experimental results in this study, practical guidelines for sensor-to-receiver locations are proposed to reduce the effects of the interference of bottom reflected waves on the SWT measurements across a surface-breaking crack in a plate. The findings in this study will help obtain reliable and consistent SWT measurements across a surface-breaking crack in plate-like structures.

Evaluation of Static and Dynamic Residual Mechanical Properties of Heat-Damaged Concrete for Nuclear Reactor Auxiliary Buildings in Korea Using Elastic Wave Velocity Measurements.

The main objectives of this study are (1) to investigate the effects of heating and cooling on the static and dynamic residual properties of 35 MPa (5000 psi) concrete used in the design and construction of nuclear reactor auxiliary buildings in Korea; and (2) to establish the correlation between static and dynamic properties of heat-damaged concrete. For these purposes, concrete specimens (100 mm × 200 mm cylinder) were fabricated in a batch plant at a nuclear power plant (NPP) construction site in Korea. To induce thermal damages, the concrete specimens were heated to target temperatures from 100 °C to 1000 °C with intervals of 100 °C, at a heating rate of 5 °C/min and allowed to reach room temperature by natural cooling. The dynamic properties (dynamic elastic modulus and dynamic Poisson's ratio) of concrete were evaluated using elastic wave measurements (P-wave velocity measurements according to ASTM C597/C597M-16 and fundamental longitudinal and transverse resonance tests according to ASTM C215-14) before and after the thermal damages. The static properties (compressive strength, static elastic modulus and static Poisson's ratio) of heat-damaged concrete were measured by the uniaxial compressive testing in accordance with ASTM C39-14 and ASTM C469-14. It was demonstrated that the elastic wave velocities of heat-damaged concrete were proportional to the square root of the reduced dynamic elastic moduli. Furthermore, the relationship between static and dynamic elastic moduli of heat-damaged concrete was established in this study. The results of this study could improve the understanding of the static and dynamic residual mechanical properties of Korea NPP concrete under heating and cooling.

Corrosion Monitoring of Reinforced Steel Embedded in Cement Mortar under Wet-And-Dry Cycles by Electrochemical Impedance Spectroscopy.

The primary objective of the present work is to measure the corrosion rate of reinforcing steel embedded in concrete structures in a simulated marine environment of high chloride concentration. The selection of a single frequency that corresponds to the solution resistance and single frequency that corresponds to the charge transfer resistance were performed and measurements were carried out in a relatively faster time. A total of seven cement mortar specimens were prepared. The effect of varying cover thickness (5-50 mm) and rebar distance (10-80 mm) on the electrical resistance of the concrete and corrosion rate of the steel was examined. To simulate the corrosion of reinforced concrete in a marine environment, cement mortars were exposed to 25 wet-dry cycles that involve an immersion for 8 h in 3 wt.% NaCl solution and drying time of 16 h under room temperature. Alternative current (AC) impedance measurements were carried out within a frequency range from 100 kHz to 1 mHz. Results show that the formation of rust layers on rebars has caused a significant decrease in the maximum phase shift to θ = -30°. An accelerated corrosion rate of the rebars was observed during drying stage.

Evaluation of Delamination in Concrete by IE Testing Using Multi-Channel Elastic Wave Data.

The main objectives of this study are to develop a non-destructive test method for evaluating delamination defects in concrete by the Impact-echo test using multi-channel elastic wave data and to verify the validity of the proposed method by experimental studies in the laboratory. First, prototype equipment using an eight-channel linear sensor array was developed to perform elastic wave measurements on the surface of the concrete. In this study, three concrete slab specimens (1500 mm (width) by 1500 mm (length) by 300 mm (thickness)), with simulated delamination defects of various lateral dimensions and depth, were designed and constructed in the laboratory. Multi-channel elastic wave signals measured on the three concrete specimens were converted to the frequency-phase velocity image by using the phase-shift method. A data processing method was proposed to extract the dominant propagating waves and non-propagating waves from the dispersion images. The dominant wave modes were used to evaluate delamination defects in concrete. It was demonstrated that the surface wave velocity values were useful for characterizing the shallow delamination defects in concrete. In addition, the peak frequency of non-propagating wave modes extracted from the dispersion images gives information on the lateral dimensions and depths of the delamination defects. This study also discussed the feasibility of combined use of the results from propagating and non-propagating wave modes to better understand the information on delamination defects in concrete. As will be discussed, the multi-channel elastic wave measurements enable more accurate, consistent, and rapid measurements and data processing for evaluation of delamination defects in concrete than the single-channel sensing method.

Effects of sensor locations on air-coupled surface wave transmission measurements across a surface-breaking crack.

Previous studies show that the surface wave transmission (SWT) method is effective to determine the depth of a surface-breaking crack in solid materials. However, nearfield wave scattering caused by the crack affects the reliability and consistency of surface wave transmission measurements. Prior studies on near-field scattering have focused on the case where crack depth h is greater than wavelength λ of surface waves (i.e., h/λ > 1). Near-field scattering of surface waves remains not completely understood in the range of h/λ for the SWT method (i.e., 0 ≤ h/λ ≤ 1/3), where the transmission coefficient is sensitive to crack depth change and monotonically decreases with increasing h/λ. In this study, the authors thoroughly investigated the near-field scattering of surface waves caused by a surface-breaking crack using experimental tests and numerical simulations for 0 ≤ h/λ ≤ 1/3. First, the effects of sensor locations on surface wave transmission coefficients across a surface-breaking crack are studied experimentally. Data are collected from Plexiglas and concrete specimens using air-coupled sensors. As a result, the variation of transmission coefficients is expressed in terms of the normalized crack depth (h/λ) as well as the normalized sensor location (x/λ). The validity of finite element models is also verified by comparing experimental results with numerical simulations (finite element method). Second, a series of parametric studies is performed using the verified finite element model to obtain more complete understanding of near-field scattering of surface waves propagating in various solid materials with different mechanical properties and geometric conditions. Finally, a guideline for selecting appropriate sensor arrangements to reliably obtain the crack depth using the SWT method is suggested.