Search Results (211)

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

In this study, acoustic emission (AE) analysis was carried out to evaluate and quantify the cracking behavior of large-scale multi-layered reinforced concrete beams under flexural tests. Four normal concrete beams were repaired by adding a layer of crumb rubberized engineered cementitious composites (CRECCs) or powder rubberized engineered cementitious composites (PRECCs), in either the tension or compression zone of the beam. Additional three unrepaired control beams, fully cast with either normal concrete, CRECCs, or PRECCs, were tested for comparison. Flexural tests were performed on all the tested beams in conjunction with AE monitoring until failure. AE raw data obtained from the flexural testing was filtered and then analyzed to detect and assess the cracking behavior of all the tested beams. A variety of AE parameters, including number of hits and cumulative signal strength, were utilized to study the crack propagation throughout the testing. Furthermore, b-value and intensity analyses were implemented and yielded additional parameters called b-value, historic index [H (t)], and severity (S_r)_. The analysis of the changes in the AE parameters allowed the identification of the first crack in all tested beams. Moreover, varying the rubber particle size (crumb rubber or powder rubber), repair layer location, or AE sensor location showed a significant impact on the number of hits and signal amplitude. Finally, by using the results of the study, it was possible to develop a damage quantification chart that can identify different damage stages (first crack and ultimate load) related to the intensity analysis parameters (H (t) and S_r). Full article

Bridges are fundamental components of transportation infrastructure, facilitating the efficient movement of people and goods. However, the conservation of heritage bridges introduces additional challenges, encompassing environmental, social, cultural, and economic dimensions of sustainability. This study investigates risk mitigation strategies for a heritage-listed, 120-year-old reinforced concrete bridge in Australia—one of the nation’s earliest examples of reinforced concrete construction, which remains operational today. The structure faces multiple risks, including passage of overweight vehicles, environmental degradation, progressive crack development due to traffic loading, and potential foundation scouring from an adjacent stream. Due to the heritage status and associated legal constraints, only non-invasive testing methods were employed. Ambient vibration testing was conducted to identify the bridge’s dynamic characteristics under normal traffic conditions, complemented by non-contact displacement monitoring using laser distance sensors. A digital twin structural model was subsequently developed and validated against field data. This model enabled the execution of various “what-if” simulations, including passage of overweight vehicles and loss of foundation due to scouring, providing quantitative assessments of potential risk scenarios. Drawing on insights gained from the case study, the article proposes a six-phase Incident Response Framework tailored for heritage bridge management. This comprehensive framework incorporates remote sensing technologies for incident detection, digital twin-based structural assessment, damage containment and mitigation protocols, recovery planning, and documentation to prevent recurrence—thus supporting the long-term preservation and functionality of heritage bridge assets. Full article

Two different magnetoelastic Metglas materials with distinct shapes were compared as sensing elements for the structural health monitoring of concrete beams. One had a ribbon shape, while the other had a microwire shape. The sensing elements were attached to different concrete beams, and a crack was introduced into each beam. The beams were subjected to flexural vibrations, and their deformations were recorded wirelessly by coils, detecting the magnetic signals emitted due to the magnetoelastic nature of the sensors. Fast Fourier Analysis of the received signal revealed the bending mode frequencies of the beams, which serve as a “signature” of their structural health. In these spectra, the ribbon-shaped sensor exhibited a 1.4-times stronger signal than the microwire sensor. However, the extracted mode frequencies were nearly identical, with differences of less than 1% both before and after damage. This indicates that both sensors can be used equivalently to monitor structural damage in concrete beams. The damage-related relative frequency shifts ranged from −0.01 to −0.03, with similar results for both sensors. Thermal annealing was also studied and appeared to significantly enhance the signal by 10–30%, likely due to the relaxation of internal stresses induced during the rapid solidification synthesis of these materials. This enhancement was more pronounced in the ribbon-shaped sensor. This study is the first to utilize a magnetoelastic microwire sensor for damage detection in concrete beams. Full article

This study investigated the changes in the acoustic emission (AE) activity emitted in fiber-reinforced concrete (FRC) under flexure at two temperatures (25 °C and −20 °C). Seven concrete mixtures were developed with different water-binder ratios (w/b) (0.4 and 0.55), different fiber materials (steel fiber (SF) and synthetic polypropylene fiber (Syn-PF)), different fiber lengths (19 mm and 38 mm), and various Syn-PF contents (0%, 0.2%, and 1%). Prisms with dimensions of 100 × 100 × 400 mm from each mixture underwent a four-point monotonic flexure load while collecting the emitted acoustic waves via attached AE sensors. AE parameter-based analyses, including b-value, improved b-value (Ib-value), intensity, and rise time/average signal amplitude (RA) analyses, were performed using the raw AE data to highlight the change in the AE activity associated with different stages of damage (micro- and macro-cracking). The results showed that the number of hits, average frequency, cumulative signal strength (CSS), and energy were higher for the waves released at −20 °C compared to those obtained at 25 °C. The onset of the first visible micro- and macro-cracks was noticed to be associated with a significant spike in CSS, historic index (H (t)), severity (S_r) curves, a noticeable dip in the b-value curve, and a compression in bellows/fluctuations of the Ib-value curve for both testing temperatures. In addition, time and load thresholds of micro- and macro-cracks increased when samples were cooled down and tested at −20 °C, especially in the mixtures with higher w/b, longer fibers, and lower fiber content. This improvement in mechanical performance and cracking threshold limits was associated with higher AE activity in terms of an overall increase in CSS, S_r, and H (t) values and an overall reduction in b-values. In addition, varying the concrete mixture design parameters, including the w/b ratio as well as fiber type, content, and length, showed a significant impact on the flexural behavior and the AE activity of the tested mixtures at both temperatures (25 °C and −20 °C). Intensity and RA analysis parameters allowed the development of two charts to characterize the detected AE events, whether associated with micro- and macro-cracks considering the temperature effect. Full article

In order to investigate the mechanical behavior of a novel pre-tensioned polygonal prestressed T-beam subject to combined bending, shear, and torsion, this study meticulously designed and fabricated a full-scale specimen with a calculated span of 28.28 m, a beam height of 1.8 m, and a top flange width of 1.75 m. A systematic static loading test was conducted. A multi-source data acquisition methodology was employed throughout the experiment. A variety of embedded and external sensors were strategically arranged, in conjunction with non-contact digital image correlation (VIC-3D) technology, to thoroughly monitor and analyze key mechanical performance indicators, including deformation capacity, strain distribution characteristics, cracking resistance, and crack propagation behavior. This study provides valuable insights into the damage evolution process of novel polygonal pre-tensioned T-beams under complex loading conditions. The experimental results indicate that the loading process of the specimen when subjected to combined bending, shear, and torsion, can be divided into two distinct stages: the elastic stage and the crack development stage. Cracks initially manifested at the junction of the upper flange and web at the extremities of the beam and at the bottom flange of the loaded segment. Subsequently, numerous diagonal and flexural–shear cracks developed within the web, while diagonal cracks also commenced to form on the top surface, exhibiting a propensity to propagate toward the support section. Following the appearance of diagonal cracks in the web concrete, both stirrup strain and concrete strain demonstrated abrupt changes. The peak strain observed within the upper stirrups was markedly greater than that measured in the middle and lower regions. On the front elevation of the web, the principal strain peak was concentrated near the connection line between the loading bottom and the upper support. In contrast, on the back elevation of the web, the principal tensile strain was more pronounced near the connection line between the loading top and the lower support. Full article

This study proposes a cyber–physical framework for the integration of a heterogeneous swarm of robots, sensors, microrobots, and AR for structural health monitoring and confined space inspection based on the application’s unique challenges. The structural issues investigated are cracks in the walls, deformation of the structures, and damage to the culverts and devices commonly used in buildings. The PC and augmented reality interfaces are incorporated for human–robot collaboration to provide the necessary information to the human user while teleoperating the robots. The proposed interfaces use edge computing and machine learning to enhance operator interactions and to improve damage detection in confined spaces and challenging environments. The proposed swarm inspection framework is called HeSARIC. Full article

According to high-precision damage identification of aircraft complex configuration fatigue key structures, a high-precision mathematical model of complex configuration structure is established with the use of digital twin technology to realize the real-time and accurate characterization of a physical entity from the macro to the micro state. Meanwhile, the damage identification information obtained by different sensor technologies and systems is used to deduce the exact state of the damage or crack. In other words, the advantage of a multi-source data drive is used to improve the effectiveness of the overall monitoring system and eliminate the limitations of single-sensor monitoring technology. Each sensor system directly transmits their filtered data information to the fusion center (digital twin system). There is no influence between each sensor; the digital twin carries out comprehensive processing and fusion analysis of each piece of information in an appropriate manner, according to the built-in algorithm and mechanism, and then outputs the final damage identification and fault diagnosis results. Full article

Establishing dependable and resilient methodologies for identifying damage that may compromise the integrity of reinforced concrete (RC) infrastructures is imperative for preventing potential catastrophic failures. Continuous evaluation and Structural Health Monitoring (SHM) can play a key role in extending the lifespan of new or existing buildings. At the same time, early crack detection in critical members prevents bearing capacity loss and potential failures, enhancing safety and reliability. Furthermore, implementing discrete fibers in concrete has significantly improved the ductility and durability of Fiber-Reinforced Concrete (FRC). The present study employs a hierarchical clustering analysis (HCA) to identify damage in FRC by analyzing the raw Electromechanical Impedance (EMI) signature of piezoelectric lead zirconate titanate (PZT) transducers. The experimental program consisted of three FRC standard cylinders subjected to repeated loading. The loading procedure consists of 6 incremental steps carefully selected to gradually deteriorate FRC’s structural integrity. Additionally, three PZT patches were adhered across the height of its specimen using epoxy resin, and their EMI response was captured between each loading step. Subsequently, the HCA was conducted for each PZT transducer individually. The experimental investigation demonstrates the efficacy of HCA in detecting load-induced damage in FRC through the variations in the EMI signatures of externally bonded PZT sensors. Full article

Floating wind turbines (FWTs) operate in offshore environments under harsh and varying operating conditions, making frequent in situ monitoring dangerous for maintenance teams and costly for operators. Remote and automated diagnosis, including the stages of detection, identification, and severity characterization of early stage damages in FWTs through advanced vibration-based structural health monitoring (SHM) methods of the machine learning (ML) type, is evidently critical for timely repairs, extending their operational lifecycle, reducing maintenance costs, and enhancing safety. This study investigates, for the first time, the complete (all stages) damage diagnosis problem by employing well-established ML SHM methods and conducting hundreds of experiments on a lab-scale FWT model operating under different wind speeds and directions, both in healthy and damaged states. The latter include two distinct blade cracks of limited length, two added masses attached to the blade edge simulating possible accumulation of ice, and connection degradation at the mounting of the main tower with the floater. The results indicate that the proper training of advanced ML methods using damage-sensitive feature vectors that represent the structural dynamics within the entire frequency bandwidth of measurements may achieve flawless damage diagnosis, reaching $100\%$ success at all diagnosis stages, even when only a minimal number of vibration signals from a limited number of sensors (a single sensor in this study) are used. Full article

Bedrock fault dislocation is a crucial structural factor influencing landslide movement. Accurately predicting the location and scale of rupture zones within a slope body is essential for effective slope construction design and risk mitigation. Based on an analysis of seismic damage in slope cross-bedrock faults, this article creatively realizes the physical model test of the slope and its covering layer site with soil rupture zones at the top and toe of the slope caused by the dislocation of the bedrock normal fault. Through the model test, macroscopic phenomena were observed, and microscopic analysis was obtained by deploying sensors. The main results were as follows: (i) The evolutionary process of the instability mechanism could be divided into three stages: crack damage stage (Stage I), crack expansion and penetration stage (Stage II), and slope instability stage (Stage III). (ii) Two rupture modes of the soil body in the slope under bedrock dislocation were identified, with the rupture mode at the slope crest having a greater impact on the soil slope. (iii) Inferring the position of bedrock faults through the location of the main rupture zones on the slope surface represents a feasible supplementary method for identifying seismogenic structures during field surveys. These research results provide a scientific basis for the stability assessment of cross-fault slopes and the reinforcement design of landslide disasters. Full article

Existing methods for detecting road damage mainly depend on manual inspections or sensor-equipped vehicles, which are inefficient, have limited coverage, and are susceptible to errors and delays. These traditional methods also struggle with detecting minor damage, such as small cracks and initial potholes, making real-time road monitoring challenging. To address these issues and improve the performance for real-time road damage detection using Street View Image Data (SVRDD), this study propose DAPONet, a new deep learning model. DAPONet proposes three main innovations: (1) a dual attention mechanism that combines global context and local attention, (2) a multi-scale partial overparameterization module (CPDA), and (3) an efficient downsampling module (MCD). Experimental results on the SVRDD public dataset show that DAPONet reaches a mAP50 of 70.1%, surpassing YOLOv10n (an optimized version of YOLO) by 10.4%, while reducing the model’s size to 1.6 M parameters and cutting FLOPs to 1.7 G, resulting in a 41% and 80% decrease, respectively. Furthermore, the model’s mAP50-95 of 33.4% on the MS COCO2017 dataset demonstrates its superior performance, with a 0.8% improvement over EfficientDet-D1, while reducing parameters and FLOPs by 74%. Full article

The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or periodically in frequent time stamps. This paper briefly introduces a new type of sensor, called a Customized Crack Propagation Sensor (CCPS), which is an alternative for crack gauges, but with enhanced functional features and customizability. Due to those characteristics, it is necessary to develop a family of computer-aided supporting models for rapid prototyping and analysis of the new designs of sensors of various shapes and configurations, which this paper presents by use of simulation tools. For a prototyping of the sensor lay out, an algorithm is elaborated, based on an application created in LabVIEW 2022 software, which generates two spreadsheets formatted by the requirements of Autodesk Inventor 2014 and COMSOL Multiphysics 5.6 software, based on data entered by the user. As a result, a tailored-in-shape CCPS layout is prepared. A parametric model of the sensor is prepared in Autodesk Inventor software, which automatically changes its geometric dimensions after changing data in an MS Excel spreadsheet. Then, the generated layout is analyzed to obtain electromechanical characteristics for defined CCPS geometry and materials used in the COMSOL Multiphysics software. Another application is devoted to purely mechanical analysis. The graphical user interface (GUI) add-on based on the Abaqus 2018 software engine is prepared for advanced mechanical analysis simulations of sensor materials in selected loading scenarios. The GUI is used for entering material libraries and the selection of loading conditions and a type of specimen, while the results of the numerical analysis are delivered through Abaqus. The main advantage of the developed GUI is the capacity for personnel inexperienced in using the Abaqus environment to perform analysis. Some results of simulation tests carried out in both COMSOL Multiphysics as well as Abaqus software are delivered in this paper, using a predefined parametric sensor model. For example, using a rigid epoxy resin for an insulating layer shows a negligible difference in the level of strain compared to the structure during a simulated tensile test, specifically in the tested layer thickness range of up to 0.3 mm. However, during bending tests, an approx. 17% change in principal strain level can be observed through the top to bottom edge of the epoxy resin layer. The adopted methodology for carrying out simulation studies assumes the parallel use of a set of various computer-aided tools. This approach allows for taking advantage of individual software environments, which allows for expanding the scope of analyses and using the developed models and applications in further research activities. Full article

Piezoelectric sensors convert mechanical stress into electrical charge via the piezoelectric effect, and when fabricated as fibers, they offer flexibility, lightweight properties, and adaptability to complex shapes for self-powered wearable sensors. Polyvinylidene fluoride (PVDF) nanofibers have garnered significant interest due to their potential applications in various fields, including sensors, actuators, and energy-harvesting devices. Achieving optimal piezoelectric properties in PVDF nanofibers requires the careful optimization of polarization. Applying a high electric field to PVDF chains can cause significant mechanical deformation due to electrostriction, leading to crack formation and fragmentation, particularly at the chain ends. Therefore, it is essential to explore methods for polarizing PVDF at the lowest possible voltage to prevent structural damage. In this study, a Design of Experiments (DoE) approach was employed to systematically optimize the polarization parameters using a definitive screening design. The main effects of the input parameters on piezoelectric properties were identified. Heat treatment and the electric field were significant factors affecting the sensor’s sensitivity and β-phase fraction. At the highest temperature of 120 °C and the maximum applied electric field of 3.5 kV/cm, the % β-phase (F(β)) exceeded 95%. However, when reducing the electric field to 1.5 kV/cm and 120 °C, the % F(β) ranged between 87.5% and 90%. The dielectric constant (ɛ′) of polarized PVDF was determined to be 30 at an electric field frequency of 1 Hz, compared to a value of 25 for non-polarized PVDF. The piezoelectric voltage coefficient (g₃₃) for polarized PVDF was measured at 32 mV·m/N at 1 Hz, whereas non-polarized PVDF exhibited a value of 3.4 mV·m/N. The findings indicate that, in addition to a high density of β-phase dipoles, the polarization of these dipoles significantly enhances the sensitivity of the PVDF nanofiber mat. Full article

Passive nondestructive testing (NDT) methods allow one to detect damage by the energies emitted from the internal processes. While the test conditions can be controlled and repeatable, obtained data are random, and the probability of detection (PoD) is affected. However, in concrete with complex fracture behavior, factors such as signal attenuation, sensor-damage distance, and test configuration influence the reliability of the test. The conventional practice of proceeding without assessing credibility prevents the ability to determine whether a configuration modification is required, necessitating reassessment. The main objective of this study is to develop a signal-centric framework to enhance the real-time reliability of inspection by investigating the PoD of acoustic emission (AE), a widely used passive NDT method for the real-time monitoring of structures. This study’s purpose is to evaluate the mechanical processes and the passive signal responses, emphasizing the detectability of cracking in concrete with two PoD approaches, namely, amplitude- and energy-based PoDs. Additionally, critical signal signatures, namely, signal-to-noise ratio (SNR) and frequency, were pinpointed for their direct influence on the detectability of the crack. With the outcomes obtained, a novel framework, which aims to provide an adaptive evaluation of the PoD of the technique, was suggested to achieve the desired quality in the damage detection of structures. Full article