Search Results (137)

Bridge decks are continuously subjected to high environmental exposure, traffic loading, and material aging, leading to progressive delamination which can negatively affect structural integrity and public safety. More specifically, subsurface delamination of concrete and corroded steel reinforcement must be repaired to keep the decks operational. Among non-destructive evaluation techniques, Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) offer complementary capabilities for detecting subsurface and near-surface defects; however, effective GPR-IRT data fusion remains challenging due to fundamental differences in sensing principles, spatial resolution and sensitivity. This study introduces a Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework that integrates GPR and IRT data to improve delamination detection in reinforced concrete bridge decks. The proposed approach leverages transfer learning, cross-modal attention mechanisms, and gated fusion to enable robust learning from heterogeneous sensor inputs. Furthermore, a systematic feature selection protocol is integrated to identify physically meaningful indicators that remain consistent across different bridges, enhancing generalization capability. The framework is trained and validated using the publicly available SDNET2021 dataset, comprising co-registered GPR and IRT measurements from five in-service bridge decks with verified delamination ground truth. Results demonstrate substantial performance improvements, with average F1-score gains of up to 55% over IRT-based methods and 25% over GPR-based methods across all tested bridges. Comparative analysis against state-of-the-art methods confirmed the superior generalization capability of the proposed multi-modal approach over single-modality approaches. The findings highlight the potential of deep learning-based sensor fusion as a scalable and data-efficient decision-support tool to prioritize regions for detailed physical investigation during long-term infrastructure monitoring. Full article

Chloride and sulfate ion attacks are among the leading causes of deterioration in reinforced concrete structures, leading to the corrosion of steel reinforcement, expansion, cracking, and premature structural failure. Early detection of these ion-induced deteriorations is essential not only for maintaining safety but also for supporting sustainability objectives by extending service life, reducing material consumption, and minimizing carbon-intensive repairs. This review synthesizes current advances in non-destructive testing (NDT) techniques used to identify and quantify the impacts of chloride and sulfate ions in reinforced concrete. The mechanisms of ion ingress and their associated degradation processes are examined together with the operating principles, strengths, and limitations of key NDT methods, including electrical resistivity, acoustic emission, infrared thermography, ground penetrating radar, and ultrasonic pulse velocity. By enabling timely maintenance decisions and reducing unnecessary demolition or intrusive testing, these NDT methods contribute directly to sustainable infrastructure management. Through comparative analysis and real-world case studies, the paper highlights the most effective NDT applications for deterioration scenarios and outlines emerging innovations that enhance accuracy, data interpretation, and long-term monitoring capabilities. The findings demonstrate how advancements in NDT support the development and preservation of durable and sustainable concrete structures. Full article

Concrete wall cavities are common hidden defects in construction engineering that seriously reduce structural safety, durability, and construction quality, especially in old buildings and projects without complete design documents. Traditional detection methods have obvious limitations: the manual tapping method relies heavily on subjective experience and lacks quantitative standards, while advanced non-destructive testing methods such as ultrasonic testing and infrared thermography are expensive, complex to operate, and difficult to apply on a large scale. At present, the quantitative correlation between acoustic signal characteristics and cavity defects has not been fully studied. To address these problems, this study combines literature analysis, controlled experiments, and acoustic signal processing to propose a quantitative identification method for concrete wall cavities based on autocorrelation analysis of sound signals. Tapping signals from normal and cavity walls are collected and processed using band-pass filtering and amplitude normalization. The autocorrelation function (ACF) is then used to extract characteristic parameters. The results show that the proposed method exhibits significantly improved accuracy and efficiency compared with traditional manual detection. Obvious differences in autocorrelation characteristics can be observed between normal and cavity walls. The method realizes the transformation from subjective auditory judgment to objective quantitative identification, with low cost, strong anti-interference ability, and high sensitivity to small defects. It provides a reliable technical tool for the rapid and quantitative non-destructive testing of concrete wall cavities in engineering practice. Full article

Cracks in concrete structures significantly affect structural safety, durability, and serviceability. To address key limitations of conventional concrete defect detection techniques, this study proposes a new crack localization method based on the AE signal attenuation characteristics. In a laboratory environment, multiple sets of concrete columns are prepared, and a controlled excitation method is used to generate vibration sources. A series of AE sensors are arranged to monitor and analyze the propagation and attenuation characteristics of vibration signals in the concrete medium in real time. The research results indicate that by analyzing the maximum amplitude attenuation characteristics of signals collected by four sensors, this method can effectively determine the approximate location of cracks on the concrete surface, providing a reliable basis for the preliminary identification of cracks. This method differs from the conventional detection concept centered on “wave velocity changes” and does not require large detection equipment. It is suitable for rapid non-destructive testing of concrete beams and columns on site. This technical approach has not yet been widely reported in existing research. This provides a new technical reference for the detection of cracks in concrete structures and adds promising solutions to the field of non-destructive test. Full article

The dielectric properties of cement-based composites (CBC) are highly sensitive to environmental humidity, which seriously restricts the quantitative interpretation accuracy of ground-penetrating radar (GPR) in the non-destructive testing of cement concrete pavement. In view of the lack of targeted prediction models due to the unclear mechanism of humidity influence in existing research, the core innovations of this study are: (1) the synergistic mechanism of water vapor dipole polarization and adsorbed water multi-layer polarization is clarified, revealing the intrinsic reason for the accelerated growth of permittivity in the high humidity range; (2) the constructed four-component dielectric model of “cement mortar–aggregate–water vapor–adsorbed water” achieves high-precision prediction within the range of 50~100% RH (R² > 0.94, relative error < 5%), and shows good predictive ability within the test scope of this study; (3) a GPR humidity correction protocol based on the model is proposed, which can effectively improve the accuracy of nondestructive testing of cement concrete structures. In this study, CBC samples with water–cement ratios of 0.4~0.6 were prepared using P.O 32.5/P.O 42.5 cement and limestone aggregate. Under the conditions of 20 ± 0.5 °C, relative humidity (RH) of 50~100%, and 2 GHz (common GPR frequency), the permittivity was measured using an Agilent P5001A network analyzer to verify the model. The results show that the permittivity increases monotonically with humidity, and the growth rate in the high humidity range (70~100%) is 2.2 times that of the low humidity range (50~70%); The higher the water–cement ratio, the shorter the age, and the lower the cement strength grade, the stronger the humidity sensitivity of CBC dielectric properties. This model provides a reliable humidity correction tool for GPR detection, and significantly improves the accuracy of nondestructive evaluation of cement concrete structures. Full article

This paper aims to present a non-destructive, optimized variational mode decomposition (VMD)-based ground-penetrating radar (GPR) method developed for identifying void defects in reinforced concrete (RC) structures. This study also presents an enhanced framework for defect detection in RC by integrating advanced spectrum analysis with deep learning techniques. A GPR investigation was conducted on an RC bridge deck with known structural defects to generate a representative dataset reflecting both intact and void-defective conditions. In addition to conventional spectral techniques such as fast Fourier transform (FFT), spectrogram, and scalogram, an optimized variational mode decomposition (VMD) method was implemented. The VMD approach decomposes GPR signals into intrinsic mode functions, enabling refined feature extraction beyond traditional spectral methods and allowing clear differentiation between intact and defective signals. The limited availability and quality of GPR small datasets have restricted the application of a functional 1D-CNN which generally requires at least several hundred datasets. To address this challenge, a data augmentation strategy is adopted. FFT-based features were successfully utilized to train a one-dimensional convolutional neural network (1D-CNN) for automated defect identification. The results demonstrate that both the advanced spectrum-based approach and the hybrid framework combining spectral analysis with deep learning significantly improve defect detection performance. Overall, the proposed methodology provides an effective and intelligent solution to support timely, data-driven decision-making for maintenance and safety assurance of bridge infrastructure. Full article

The early detection and monitoring of stress-induced damage in concrete is a key goal for nondestructive evaluation and structural health monitoring of civil structures. Both linear and nonlinear ultrasonic testing methods have been developed for this purpose. The Ultrasonic Pulse Velocity (UPV) test is the standard linear technique and is reliable and easy to use, but it typically detects defects only after micro-cracks coalesce or grow beyond a threshold size. To enable earlier detection, features extracted from the nonlinear ultrasonic response—especially harmonics generation—have been proposed. However, these approaches often require complex measurement protocols, and their signal-to-noise ratio (SNR) can be limited. In this study, we leverage an exponential swept-sine pulse-compression (ESS–PuC) procedure to characterize both linear and nonlinear responses from a single measurement. We define and extract several features from both responses, and use them to monitor micro-crack initiation and growth in concrete specimens under gradually increasing compressive load. This enables a qualitative comparison of their characteristics and performance in detecting crack formation. Full article

The long-term safety and durability of concrete building structures depend largely on their internal quality. Wall voids, as typical hidden defects, directly affect the structural bearing capacity and service life. Such defects may not only lead to surface hollowing and peeling but also develop into serious safety accidents such as local collapse. At present, acoustic detection methods are widely used in engineering practice due to their convenience and efficiency. Among them, the strike method and the slide method, as two basic excitation methods, are commonly used on-site detection methods. However, existing studies still lack the systematic comparative analysis of these two methods, especially in terms of objective evaluation based on quantitative characteristics. To fill this research gap, this study designed a strict controlled experimental scheme. By collecting acoustic signals under these two excitation methods, the time-domain waveform characteristics, frequency-domain response characteristics, and time-frequency distribution patterns were systematically analyzed. The results show that, compared with the traditional strike excitation, the slide excitation method shows significant advantages in concrete wall void detection. It not only has higher detection accuracy but also exhibits better stability and repeatability. Further analysis found that the slide signal is superior to the strike signal in terms of feature distinguish ability and anti-interference ability. Its voltage distribution curve shows more obvious separation characteristics, which significantly reduces the risk of misjudgment. Through systematic quantitative comparison, this study provides a reliable experimental basis for the acoustic detection of concrete wall voids and has important reference value for promoting the standardization and intelligent development of non-destructive testing technology. Full article

Digital image-based determination of aggregate and rock gradation has been only limitedly addressed in the existing literature despite its considerable potential to transform conventional material characterization practices in civil engineering. Rapid and accurate estimation of aggregate and rock particle size distributions using advanced image-based analytical methods can significantly improve efficiency, consistency, and scalability in design, construction, and quality control processes, particularly in large-scale structural and geotechnical engineering projects where traditional sieve analysis is time-consuming, labor-intensive, and difficult to apply under field conditions. In this study, an image-based methodology is proposed to rapidly detect aggregate particles and determine their size-based proportions within a pile by employing image enhancement, segmentation, and boundary detection algorithms. The results obtained from digital image processing are comparatively evaluated against experimental sieve analysis data, demonstrating a strong correlation between the two approaches. Low RMSE values achieved for larger aggregate sizes, such as 25.4 mm and 19 mm, indicate high detection accuracy, while the relatively higher yet acceptable RMSE values obtained for smaller particles, including 12.7 mm and 9.5 mm, confirm that the method maintains practical sensitivity across different size ranges. By analyzing samples collected from various aggregate and rock piles, the study further demonstrates the originality, robustness, and effectiveness of the proposed approach in evaluating heterogeneous material groups. Overall, the findings highlight that digital image-based determination offers a fast, reproducible, and non-destructive alternative to traditional sieve analysis, making it particularly valuable for reinforced concrete aggregate assessment and port fill rock characterization in large-scale structural and geotechnical engineering applications. Full article

Plastic shrinkage and self-desiccation, along with the associated early-age cracking, are still among the most important factors that influence long-term performance of concrete structures, including durability. Superabsorbent polymers (SAPs) have been widely researched for application in concrete to mitigate shrinkage through facilitating effective internal curing by releasing water into the mixture to promote continuous hydration of cement. The acoustic emission (AE) monitoring technique, due to its high sensitivity, has proven very effective in tracking the process of water release by SAPs in concrete during early-stage curing. Typically, AE parameters such as cumulative activity, amplitude and energy are utilized to characterize the kinetics of curing processes. While these parameters indicate well the internal activity of SAPs in time, they do not offer information on the precise location of the active sources within the material’s volume, leaving a crucial gap in the understanding of the ongoing microstructural changes caused by internal water distribution and cement hydration. In this sense, AE event source localization can offer information about the active zones of water hydration activity in the material 3D domain, allowing detection of their evolution during concrete curing. Meanwhile, Acoustic Emission Tomography (AET) computes ultrasonic velocity distributions in different periods of monitoring, which are governed by acoustic characteristics of the concrete mixtures, to visualize material stiffness development spatially and temporally. This level of insight is particularly important for SAP concrete, where uniformity of internal water curing is essential for ensuring long-term durability and material soundness. By visualizing how the hydration sources evolve in real time, these methods offer an effective, non-destructive, and cost-effective solution for early-age concrete quality control, which would be challenging to achieve through other techniques. Full article

Post-earthquake bridge crack detection is a critical step in assessing structural safety. Traditional manual detection of bridge cracks is time-consuming, labor-intensive, and poses significant risks. This paper focuses on the automatic identification of structural cracks by analyzing their morphology, orientation, and distribution characteristics, and preliminarily distinguishes them from non-structural damages such as surface stains and coating peeling. Therefore, this paper proposes a bridge crack recognition algorithm based on image processing. First, the input crack image undergoes preprocessing to obtain a binary image, reducing measurement errors caused by environmental factors or uneven illumination, using an improved Retinex algorithm to enhance image brightness. Second, an improved wavelet transform method is employed to remove large-area noise. Then, connected component analysis is used to filter out point-like and patch-like noise, resulting in a complete and clear crack skeleton. Finally, the crack length, width, and other characteristic values are obtained using an image pixel coordinate calculation method, achieving non-contact, non-destructive measurement of concrete surface crack characteristics. The algorithm is based on two-dimensional image processing and does not directly measure crack depth, but the extracted parameters such as length, width, and area ratio provide important surface-based evidence for rapid post-earthquake bridge structural safety assessment. Multiple experimental results show that the proposed algorithm has a maximum width measurement relative error of less than 2.3%, a length measurement relative error within 8%, and an average peak signal-to-noise ratio (PSNR) of the denoised image increased to 74.73 dB. This algorithm provides an effective automated detection tool for rapid post-earthquake bridge safety assessment. Full article

This study investigates the influence of electromagnetic field polarization in the non-destructive testing of reinforced concrete structures through both theoretical analysis and experimental validation. Theoretical models predict that the orientation of reinforcement bars relative to the incident electric field significantly affects the scattered signal, influencing their detectability. Laboratory experiments on realistic reinforced concrete specimens presenting both vertical bars and horizontal brackets confirm these predictions, demonstrating that polarization can be exploited to enhance measurement accuracy. These findings provide useful insights into the development of microwave-based diagnostic techniques for structural assessment. Full article

Concrete materials are vulnerable to various sorts of structural defects. Reliable measurement and quantification of concrete defects are crucial for ensuring safety and effective maintenance. Deep learning is commonly utilized to detect and classify concrete defects efficiently. However, most available studies do not study multi-class defect identification. This study aims to develop a multi-class concrete defect detection framework to enhance concrete classification accuracy while enabling reliable defect localization. To achieve this, a new image-based non-destructive measurement dataset comprising 2029 images of concrete defects, categorized into five categories, has been compiled. For defect identification, the DenseNet201 model is modified by adding a guided semantic–spatial fusion module with a squeeze-and-excitation architecture, which enhances feature representation and introduces attention mechanisms to the model, enabling it to detect and track defect regions. Experiments are conducted on the collected dataset, and various scenarios and comparisons are performed to verify the proposed model. Results reveal the superiority of the proposed architecture with an accuracy enhancement of 5.6% compared to the original DenseNet201. A graphical user interface is also designed to integrate the trained model into a practical measurement instrument, enabling users to interact with the backend model and detect various defects from intact cases. Full article

Ensuring the structural integrity of prestressed (PS) concrete is essential for the safety and longevity of infrastructure. Magnetic Flux Leakage (MFL) testing is a widely used non-destructive testing (NDT) method for detecting fractures in prestressing steel. This study explores the application of quantum sensors based on nitrogen vacancy (NV) centers in artificial diamonds for MFL testing and presents a novel method for processing continuous-wave optically detected magnetic resonance (CW-ODMR) data into vectorized magnetic field measurements. These sensors offer high sensitivity, low hysteresis, and multi-directional magnetic field detection, making them a promising alternative for advanced NDT applications. A data processing framework was developed to transform CW-ODMR measurements into vectorized magnetic flux density values in the x, y, and z directions. This process enables the conversion of crystallographic sensor orientations into calibrated field directions, ensuring precise magnetic field reconstruction. The method was validated through 121 fracture measurements and 19 open-bar-end measurements, demonstrating its effectiveness in extracting high-resolution vectorized magnetic field data. A subsequent statistical evaluation quantified the influence of sensor displacement, magnetization direction, magnetization distance, and measurement distance. These findings establish a foundation for integrating quantum sensors into MFL-based NDT, with potential applications extending beyond building inspections to a wide range of advanced sensing technologies in scientific and industrial fields. Full article

The paper addresses a critical gap in early-stage corrosion detection in reinforced concrete, a leading cause of structural failures with significant impacts on humans, the economy, and the environment. It presents the M5 (Magnetic Force-Induced Vibration Evaluation) method, an innovative Structural Health Monitoring (SHM) approach that avoids damping in concrete by using electromagnetic excitation and transferring rebar vibrations through magnetic coupling over the sample. By inducing and analyzing natural vibrations directly in reinforcement, M5 enables sensitive, non-destructive evaluation (NDE) of corrosion before deterioration occurs. The study follows a systematic literature review based on PRISMA standards and utilizes EmbedSLR v1.0 free software. The methodology combines NDE with IoT deployment using Low-Power Wide Area Networks (LPWANs) and advanced machine learning (ARA) to detect frequency changes caused by corrosion, ensuring continuous monitoring. Findings suggest that M5 has the potential to enhance sustainable asset management by extending infrastructure lifespan, optimizing maintenance, and reducing waste. Its practical implications are significant for urban planners and engineers aiming to align infrastructure management with smart city strategies. The originality of this work lies in integrating electromagnetic NDT with IoT and data-driven decision-making, offering new insights at the intersection of engineering and sustainable smart city management. Full article