Search Results (32)

Imaging moving targets in synthetic aperture radar (SAR) remains a significant challenge due to the defocusing and azimuthal displacement caused by target motion. To address this, this paper proposes a velocity estimation and motion compensation technique to mitigate the impact of moving targets on SAR imaging quality. The core innovation of this study lies in a wake-independent method for determining the radar beam center crossing time. Unlike traditional approaches that rely on wake features, our proposed method determines the crossing time by detecting the abrupt change in echo intensity along the time axis (i.e., the azimuth direction) of the time–frequency spectrum. Using this estimated timing, the target’s radial and azimuthal velocities are estimated. Subsequently, using the estimated velocity, the motion compensation of the moving target echoes is carried out through phase correction. Due to the difficulty in obtaining AIS data strictly synchronized with real SAR acquisitions, simulation data are initially utilized to verify the proposed method. The simulation results of moving ships with different velocities under three incidence angles demonstrate that the estimated errors of the radar radial and the azimuthal velocities generally remain below 0.1 m/s (2% relative error) and 0.5 m/s (5% relative error), respectively. Furthermore, after motion compensation, the azimuthal displacement caused by radial velocity is effectively corrected, restoring targets to their actual positions. Finally, the Level-0 raw data of ships acquired by Sentinel-1 SAR are applied to further verify the effectiveness of the method proposed in this paper. Full article

This paper presents a study on the nonlinear vibrations in the impact-echo (IE) method for void flaw detection of solid structures. Linear theory has historically served as the foundational framework for non-destructive methods, including the IE method, particularly for estimating flaws in solids. This paper gives a comprehensive analysis of the nonlinear theory behind the IE method for detection of voids in solids such as concrete structures. The general equation of motion is presented for the flexural vibration of a void-defected solid with general nonlinear constitutive material properties, and then the simplified solutions for polynomial nonlinearity and hysteresis nonlinearity are derived comprehensively. The solutions of principal frequency and sub- and super-harmonics as well as the frequency of combined modes are elaborated, and the theoretical formula of resonant frequency shift with amplitude is derived. As conventional nonlinear IE methods have been conducted by only using a phenomenological model of linear shift in resonant frequency with amplitude, the proposed new frame of nonlinear vibration theory can be used to implement the IE method more comprehensively and accurately for void detection in solids. Full article

The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo (IE), are critically evaluated in terms of their effectiveness in detecting debonding, voids, delaminations, and other defects. Recent technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) in NDT applications, have significantly improved defect characterization, automated inspections, and real-time data analysis. This review highlights AI-driven NDT approaches such as automated crack detection, hybrid NDT frameworks, and drone-assisted thermographic inspections, which enhance accuracy and efficiency in large-scale infrastructure assessments. Additionally, economic considerations and cost–performance trade-offs are analyzed, addressing the feasibility of different NDT methods in real-world FRP-strengthened structures. Finally, the review identifies key research gaps, including the need for standardization in FRP-NDT applications, AI-enhanced defect quantification, and hybrid inspection techniques. By consolidating state-of-the-art research and emerging innovations, this paper serves as a valuable resource for engineers, researchers, and practitioners involved in the assessment, monitoring, and maintenance of FRP-strengthened concrete structures. Full article

Condition assessment of concrete bridge decks is critical for ensuring structural integrity and public safety, particularly as infrastructure ages. Traditionally, such assessments are made using destructive techniques, such as coring through concrete to collect samples for analysis. However, these invasive methods create weak points within the structure and risk damaging essential components, such as cutting through rebars. This paper explores the use of three non-destructive testing (NDT) methods—electrical resistivity (ER), impact echo (IE), and infrared thermography (IRT)—to evaluate the structural health of concrete bridge decks and overlays. These techniques are tested individually and in combination through a mock-up experiment to detect defects such as delamination and corrosion. The findings demonstrate that while each NDT method has specific strengths—surface ER with a 46.67% detection rate, IE with 40%, and IRT with 53.33%—the combined detection rate increased to 60%. This combined approach provides a more comprehensive assessment and is expected to help establish better maintenance strategies for aging infrastructure. The study highlights the importance of optimizing NDT methods for real-world applications, addressing current limitations such as environmental sensitivity and scanning speed, to improve the early detection and prevention of structural failures. Full article

The incorporation of natural fibers into concrete has recently emerged as a popular approach in the field of construction materials due to its sustainability and environmental friendliness. In comparison to artificial fibers, natural fibers are more cost-effective and widely available globally. Among the various natural fibers, coconut fiber (CF) stands out for its unique set of advantages. This study aims to investigate the mechanical properties and durability of coconut-fiber-reinforced self-healing concrete (CFR-SHC) in the context of corrosion resistance. Additionally, Bacillus subtilis bacteria (10% by mass) was incorporated into the CFR-SHC. The impact of ±50 mm long CF with varying contents of 0.25%, 0.5%, and 0.75% by mass was examined. Specimens were subjected to corrosion acceleration for 48, 96, and 168 h. Non-destructive testing (NDT) methods of Electrical Resistivity (ER) and Impact Echo (IE) were conducted to test the corrosion resistance. The experimental results demonstrate that CFR-SHC increased the compressive strength by 6% and the flexural strength by 40%. CFR-SHC also exhibits excellent resistance to corrosion, characterized by low inrush current, high ER value, and high IE frequency. The most favorable overall outcomes were observed for the CFR-SHC sample containing 0.5% of the cement mass. Full article

Sentinel-6 Michael Freilich (S6-MF) is the first altimeter operating in a continuous high-rate pulse mode, i.e., interleaved mode. This ensures the generation of low-resolution (LR) mode measurements with a pulse repetition frequency (PRF) of ∼9 kHz (variable along the orbit) for the Ku-band as well as the processing of high-resolution (HR) echoes on ground. This operating mode provides an elevated number of highly correlated single looks with respect to the fewer number, weakly correlated echoes of Jason-3 altimeter. A theoretical model is exploited to envisage the correlation properties of S6-MF pulse limited waveform echoes for different sea-state conditions; after that, the model is validated by comparison with the equivalent number of looks (ENL) empirically estimated from real data. The existence of a significant dependence of the statistical properties on the range is verified, and its impact on the precision and on the accuracy in the estimation of the geophysical parameters is assessed in case of the 9 kHz PRF of S6-MF. By applying pulse decimation before the multilook processing, an investigation on new processing techniques is performed, aimed at exploiting the higher ENL in S6-MF low-resolution mode waveforms. It is shown that a bias of less than 0.4 cm is found for SSH and about 1.5 cm for SWH at SWH = 2 m when the decimated waveforms processing is compared with full high-PRF processing. Full article

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0. Full article

Accurate precipitation forecasting is essential for emergency management, aviation, and marine agencies to prepare for potential weather impacts. However, traditional radar echo extrapolation has limitations in capturing sudden weather changes caused by convective systems. Deep learning models, an alternative to radar echo extrapolation, have shown promise in precipitation nowcasting. However, the quality of the forecasted radar images deteriorates as the forecast lead time increases due to mean absolute error (MAE, a.k.a L1) or mean squared error (MSE, a.k.a L2), which do not consider the perceptual quality of the image, such as the sharpness of the edges, texture, and contrast. To improve the quality of the forecasted radar images, we propose using the Structural Similarity (SSIM) metric as a regularization term for the Conditional Generative Adversarial Network (CGAN) objective function. Our experiments on satellite images over the region 83° W–76.5° W and 33° S–40° S in 2020 show that the CGAN model trained with both L1 and SSIM regularization outperforms CGAN models trained with only L1, L2, or SSIM regularizations alone. Moreover, the forecast accuracy of CGAN is compared with other state-of-the-art models, such as U-Net and Persistence. Persistence assumes that rainfall remains constant for the next few hours, resulting in higher forecast accuracies for shorter lead times (i.e., <2 h) measured by the critical success index (CSI), probability of detection (POD), and Heidtke skill score (HSS). In contrast, CGAN trained with L1 and SSIM regularization achieves higher CSI, POD, and HSS for lead times greater than 2 h and higher SSIM for all lead times. Full article

The tendon that is inserted into the duct is a crucial component of prestressed concrete (PSC) bridges and, when exposed to air, can quickly corrode, and cause structural collapse. It can interpret the signal measured by non-destructive testing (NDT) to determine the condition (normal or void) inside the duct. However, it requires the use of expensive NDT equipment such as ultrasonic waves or the hiring of experts. In this paper, we proposed an impact–echo (IE) method based on deep support vector data description (Deep SVDD) for economical void detection inside a duct. Because the pattern of IE changes for various reasons such as difference of specimen or bridge, supervised learning is not suitable. Deep SVDD is classified as normal and defective, which is a broad distribution as a hypersphere that encloses a multi-dimensional feature space for normal data represented by an autoencoder. Here, an autoencoder was developed based on the ELMo (embeddings from language model)-like structure to obtain an effective representation for IE. In the experiment, we evaluated the performance of the IE data measured in different specimens. Thus, our proposed model showed an accuracy of about 77.84% which is an improvement of up to about 47% compared to the supervised learning approach. Full article

Impact echo (IE) is a non-destructive evaluation method commonly used to detect subsurface delamination in reinforced concrete bridge decks. Existing analysis methods are based on frequency domain which can lead to inaccurate assessments of reinforced concrete bridge decks since they do not consider features of the IE signals in the time domain. The authors propose a new method for IE classification by combining features in the time and the frequency domains. The features used in this study included normalized peak values, energy, power, time of peaks, and signal lengths that were extracted from IE signals after they are preprocessed. We used a dataset containing IE data collected from four in-service bridges, annotated using chain dragging. A support vector machine (SVM) classifier was constructed using combined features to classify IE signals. A 1DCNN with unfiltered IE signals and a two-dimensional CNN using wavelet scalograms (2D representations of unfiltered IE signals) were also used to classify IE signals. The SVM model performed significantly better than the other models, with an accuracy rate, true positive rate, and true negative rate of 97%, 92%, and 98%, respectively. The SVM model also generated more accurate defect maps for all investigated bridges. IE data from the Federal Highway Administration’s InfoBridge website were used to investigate the efficacy of the developed models. The investigation yielded promising results for the proposed SVM model when used for a new set of IE data. Full article

Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI) models that can detect bridge structure defects autonomously. Most defect datasets contain visual images of surface defects; however, subsurface defect data such as delamination which are critical for effective bridge deck evaluations are typically rare or limited to laboratory specimens. Three Non-Destructive Evaluation (NDE) methods (Infrared Thermography (IRT), Impact Echo (IE), and Ground Penetrating Radar (GPR)) were used for concrete delamination detection and reinforcement corrosion detection. The authors have developed a unique NDE dataset, Structural Defect Network 2021 (SDNET2021), which consists of IRT, IE, and GPR data collected from five in-service reinforced concrete bridge decks. A delamination survey map locating the areas, extent and classes of delamination served as the ground truth for annotating IRT, IE and GPR field tests’ data in this study. The IRT were processed to create an ortho-mosaic maps for each deck and were aligned with the ground truth maps using image registration, affine transformation, image binarization, morphological operations, connected components and region props techniques to execute a semi-automatic pixel–wise annotation. Conventional methods such as Fast Fourier transform (FFT)/peak frequency and B-Scan were used for preliminary analysis for the IE and GPR signal data respectively. The quality of NDE data was verified using conventional Image Quality Assessment (IQA) techniques. SDNET2021 dataset consists of 557 delaminated and 1379 sound IE signals, 214,943 delaminated and 448,159 sound GPR signals, and about 1,718,083 delaminated and 2,862,597 sound IRT pixels. SDNET2021 addresses one of the major gaps in benchmarking, developing, training, and testing advanced deep learning models for concrete bridge evaluation by providing a publicly available annotated and validated NDE dataset. Full article

The Impact-Echo (IE) test is an effective method for determining the presence, depth, and area of cracks in concrete as well as the dimensions of the sound concrete without defects. In addition, shallow delamination can be measured by confirming a flexural mode in the low-frequency region. Owing to the advancement of non-contact sensors and automated measurement equipment, the IE test can be measured at multiple points in a short period. To analyze and distinguish a large volume of data, applying supervised learning (SL) associated with various contemporary algorithms is necessary. However, SL has limitations due to the difficulty in accurate labeling for increased volumes of test data, and reflection of new specimen characteristics, and it is necessary to apply semi-supervised learning (SSL) to overcome them. This study analyzes the accuracy and evaluates the applicability of a model trained with SSL rather than SL using the data from the air-coupled IE test based on dynamic preconditions. For the detection of delamination defects, the dynamic behavior-based flexural mode was identified, and 21 features were extracted in the time and frequency domains. Three principal components (PCs) such as the real moment, real RMS, and imaginary moment were derived through principal component analysis (PCA). PCs were identical in slab, pavement, and deck. In the case of SSL considering a dynamic behavior, the accuracy increased by 7–8% compared with SL, and it could categorize good, fair, and poor status to a higher level for actual structures. The applicability of SSL to the IE test was confirmed, and because the crack progress varies under field conditions, other parameters must be considered in the future to reflect this. Full article

Background: The influence of age and gender on strain-imaging-derived myocardial work (MW) was recently investigated in healthy subjects. No information is available on the impact of heart rate (HR) on MW. Methods: 177 healthy subjects (47% men, mean age 42 years) underwent an echo-Doppler exam, including quantification of global longitudinal strain (GLS). Cuff blood pressure was used as a surrogate of left ventricular peak pressure to estimate global work index (GWI), global constructive work (GCW), global wasted work (GWW) and global work efficiency (GWE). Statistical analyses were performed according to age and HR tertiles. Results: GWW was higher in the third HR tertile, i.e., ≥74 bpm (74.7 ± 33.6 mmHg %) than in the first HR tertile (<66 bpm) (61.0 ± 32.5 mmHg %) (p < 0.02). In the pooled population, by adjusting for systolic blood pressure, GLS, E/e’ ratio and left atrial volume index, age was independently associated with GCW (β = 0.748) and GWI (β = 0.685) (both p < 0.0001) and HR with GWW (β = 0.212, p = 0.006) and GWE (β = −0.204, p = 0.007). Conclusions: In healthy subjects age shows a mild influence on GCW. HR exerts an independent negative impact on GWW and GWE: the higher HR the greater wasted work and lower myocardial efficiency. Full article

When spalling occurs at a concrete pavement joint, partial depth repair (PDR) is implemented by removing the damaged part of a slab and filling the space with repair materials. However, re-repair is frequently also required because additional distress develops at the boundary of the repaired area due to improper PDR size in addition to poor quality of materials and construction methods. For the sustainability of pavement structures, it is necessary to study the PDR size based on the mechanical theory. Therefore, in this study, the PDR size for spalling was suggested based on the results of laboratory and field tests conducted using the impact echo (IE) method. The dynamic modulus estimated in the laboratory using the IE and forced resonance methods were compared for concrete specimens subjected to repetitive freeze–thaw cycles. In addition, the correlations of the dynamic modulus estimated by the methods with the compressive strength and absorption coefficient were analyzed. As a result, the IE method, for which vibration could be estimated on the same side of the specimen where impaction was applied, was selected for use on the pavement surface. Furthermore, the short-time Fourier transform technique was used instead of the fast Fourier transform, which has been commonly used for nondestructive methods, to minimize the noise in the field and, consequently, to estimate the dynamic modulus more accurately. The dynamic modulus was estimated according to the distance from the spalling end using the IE method at the Korea Expressway Corporation test road to identify the damaged range in the slab based on the severity of spalling. The dynamic modulus, compressive strength, and absorption coefficient tests were conducted in the laboratory for specimens cored from the concrete slab where the field test was performed. Finally, the PDR size was suggested according to the severity of spalling based on the damaged range in the slab, as determined by the field test and laboratory test results. Full article

The aim of this paper is to present a method of determining sound speed in water, based on temperature measurements executed by means of a laboratory low-cost thermometer with a probe provided with a long cable. It has been assumed that the salinity variation in respect to depth, found in a shallow water area, has insignificant impact on the sound velocity distribution determined by the temperature changes. The salinity data were obtained via the Internet service from the closest measuring station that registers surface water parameters. The sound speed in water was determined based on the formulas widely adopted in hydroacoustics and compared with the results obtained from the measurements executed by means of a Conductivity/Salinity Temperature Depth (CTD/STD) probe. The impact of inaccuracy in determining the sound speed in respect to the SingleBeam EchoSounder (SBES) immersion depth, i.e., a method commonly used by unmanned surface vessels in seaport measurements, was estimated. The measurements were taken in water areas of the Baltic Sea of low salinity and then verified with measurements in the Mediterranean Sea representing quite high salinity. The method is an alternative for calibrating the SBES the bar check way and has the capacity to meet the requirements in respect to its application in hydrographic surveys. Full article