Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military ...To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military standards.The PDT method holds the view that there exist defects such as machining scratches and service cracks in the tenon-groove structures of aeroengine disks.However,it is challenging to conduct PDT assessment due to the scarcity of effective Probability of Detection(POD)model and anomaly distribution model.Through a series of Nondestructive Testing(NDT)experiments,the POD model of real cracks in tenon-groove structures is constructed for the first time by employing the Transfer Function Method(TFM).A novel anomaly distribution model is derived through the utilization of the POD model,instead of using the infeasible field data accumulation method.Subsequently,a framework for calculating the Probability of Failure(POF)of the tenon-groove structures is established,and the aforementioned two models exert a significant influence on the results of POF.展开更多
High-performance lattice structures produced through powder bed fusion-laser beam exhibit high specific strength and energy absorption capabilities.However,a significant deviation exists between the mechanical propert...High-performance lattice structures produced through powder bed fusion-laser beam exhibit high specific strength and energy absorption capabilities.However,a significant deviation exists between the mechanical properties,service life of lattice structures,and design expectations.This deviation arises from the intense interaction between the laser and powder,which leads to the formation of numerous defects within the lattice structure.To address these issues,this paper proposes a high-performance defect detection model for metal lattice structures based on YOLOv4,called YOLO-Lattice(YOLO-L).The main objectives of this paper are as follows:(1)utilize computed tomography to construct datasets of the diamond lattice and body-centered cubic lattice structures;(2)in the backbone network of YOLOv4,employ deformable convolution to enhance the feature extraction capability of the model for small-scale defects;(3)adopt a dual-attention mechanism to suppress invalid feature information and amplify the distinction between defect and background regions;and(4)implement a channel pruning strategy to eliminate channels carrying less feature information,thereby improving the inference speed of the model.The experimental results on the diamond lattice structure dataset demonstrate that the mean average precision of the YOLO-L model increased from 96.98% to 98.8%(with an intersection over union of 0.5),and the inference speed decreased from 51.3 ms to 32.5 ms when compared to YOLOv4.Thus,the YOLO-L model can be effectively used to detect defects in metal lattice structures.展开更多
The steel-epoxy-steel sandwich structures provide enhanced corrosion resistance and fatigue resistance,making them suitable for pipeline rehabilitation with effective repair and long-term durability.However,the repair...The steel-epoxy-steel sandwich structures provide enhanced corrosion resistance and fatigue resistance,making them suitable for pipeline rehabilitation with effective repair and long-term durability.However,the repair quality can be compromised by disbond between the steel and epoxy layers,whichmay result frominsufficient epoxy injection.Conventional ultrasonic testing faces challenges in accurately locating disbond defects due to aliased echo interference at interfaces.This paper proposes a signal processing algorithm for improving the accuracy of ultrasonic reflection method for detecting disbond defects between steel and epoxy layers.First,a coati optimization algorithmvariational mode decomposition(COA-VMD)is applied to adaptively decompose the ultrasonic signals and extract the intrinsic mode function components that show high correlation with the defect-related signals.Then,by calculating the relative reflectance at the interface and establishing a quantitative evaluation index based on acoustic impedance discontinuity,the locations of disbond defects are identified.Experimental results demonstrate that this method can effectively detect the locations of disbond defects between steel and epoxy layers.展开更多
The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the probl...The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.展开更多
Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigi...Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigid pressure sensors.However,arising from the limited compressibility of soft materials and the hardening of microstructures at the device interface,there is always a trade-off between high sensitivity and broad sensing range for most flexible pressure sensors,which results in a gradual saturation response and limits their practical applications.Herein,inspired by the distinct pressure perception function of crocodile receptors,a highly sensitive and wide-range flexible pressure sensor with multiscale microdomes and interlocked architecture is developed via a facile PS-decorated molding method.Combined with interlocked architecture,the multiscale dome-shaped structured interface enhances the compressibility of the material through structural complementarity,increases the contact area between functional materials,which compensates for the stiffness induced by the deformation of dense microscale columns.This effectively mitigates structural hardening across a wide pressure range,leading to the overall high performance of the sensor.As a result,the obtained sensor exhibits a low detection limit of 5 Pa,a high sensitivity of 6.14 kPa^(-1),a wide measurement range up to 231 kPa,short response/recovery time of 56 ms/69 ms,outstanding stability over 10,000 cycles.Considering these excellent properties,the sensor shows promising potential in health monitoring,human-computer interaction,wearable electronics.This study presents a strategy for the fabrication of flexible pressure sensors exhibiting high sensitivity and a wide pressure response range.展开更多
With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detec...With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detection.Audit logs,such as Sysmon,offer valuable insights;however,existing approaches typically flatten event sequences or rely on generic graph models,thereby discarding the natural parent-child process hierarchy that is critical for analyzing multiprocess attacks.This paper proposes a structure-aware threat detection framework that transforms audit logs into a unified two-dimensional(2D)spatio-temporal representation,where process hierarchy is modeled as the spatial axis and event chronology as the temporal axis.In addition,entropy-based features are incorporated to robustly capture obfuscated and non-linguistic strings,overcoming the limitations of semantic embeddings.The model’s performance was evaluated on publicly available datasets,achieving competitive results with an accuracy exceeding 95%and an F1-score of at least 0.94.The proposed approach provides a promising and reproducible solution for detecting attacks with unknown indicators of compromise(IoCs)by analyzing the relationships and behaviors of processes recorded in large-scale audit logs.展开更多
Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few de...Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.展开更多
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe...A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.展开更多
A kind of photoelectric system that is suitable to measuring and to testing the damage of the composite material intelligent structure was presented. It can measure the degree of damage of the composite intelligent st...A kind of photoelectric system that is suitable to measuring and to testing the damage of the composite material intelligent structure was presented. It can measure the degree of damage of the composite intelligent structure and it also can tell us the damage position in the structure. This system consists of two parts ： software and hardware. Experiments of the damage detection and the analysis of the composite material structure with the photoelectric system were performed, and a series of damage detection experiments was conducted. The results prove that the performance of the system is well and the effects of the measure and test are evident. Through all the experiments, the damage detection technology and test system are approved to be real-time, effective and reliable in the damage detection of the composite intelligent structure.展开更多
This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） i...This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） is proposed for detecting serious faults, and variable structure （VS） model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation. Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.展开更多
Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied...Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied. At first, the wave propagation mechanisms in concrete were analyzed. Then, an active sensing system with integrated actuators/sensors was constructed. One PZT patch was used as an actuator to generate high frequency waves, and the other PZT patches were used as sensors to detect the propagating wave. Scattered wave signals from the damage can be obtained by subtracting the baseline signal of the intact structure from the recorded signal of the damaged structure. In the experimental study, progressive cracked damage inflicted artificially on the plain concrete beam is assessed by using both lateral and thickness modes of the PZT patches. The results indicate that with the increasing number and severity of cracks, the magnitude of the sensor output decreases for the surface bonded PZT patches, and increases for the embedded PZT patches.展开更多
Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operat...Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.展开更多
The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） meth...The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） method for detecting damage in offshore platform structures based on a traditional modal strain energy method （the Stubbs index method）. The most significant difference from the Stubbs index method was the application of modal frequencies. The goal was to improve the robustness of the traditional method. To demonstrate the effectiveness and practicality of the proposed IMSE method, both numerical and experimental studies were conducted for different damage scenarios using a jacket platform structure. The results demonstrated the effectiveness of the IMSE method in damage location when only limited, spatially incomplete, and noise-polluted modal data is available. Comparative studies showed that the IMSE index outperformed the Stubbs index and exhibited stronger robustness, confirming the superiority of the proposed approach.展开更多
As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,...As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.展开更多
Damage detection based on strain responses of vibration is highly attractive for monitoring long-span reticulated structures.However,there are a lot of structure members in reticulated structures and it is impossible ...Damage detection based on strain responses of vibration is highly attractive for monitoring long-span reticulated structures.However,there are a lot of structure members in reticulated structures and it is impossible to install strain sensors in each member.Therefore,how to locate and quantify damages with the incomplete mode shapes obtained from few strain sensors is a challenge topic.A new strategy,named incomplete strain mode damage detection（ISMDD） strategy,is proposed in this paper.In the strategy,the distribution of the strain sensors in the reticulated structures can be optimized through sensitive analysis on strain mode perturbation matrix,which can be obtained by perturbation theory.Mode assurance criterion（MAC） value is applied in damage location,and the members with relative large MAC values are defined as damage members.In addition,damage index obtained by solving the perturbation equation is used for damage quantification.Numerical analysis on a long-span reticulated structure,including damage location and quantification for single-and multi-member damages,detection for different damage quantity,the effect analysis of sensor quantity,are performed to verify the effectiveness of the proposed ISMDD strategy.It can be shown from the analysis that the ISMDD strategy is effective in damage location and quantification for both single-and multi-member damages.And the quantity of strain sensors has effect on damage location,but has no obvious influence on damage quantification.Additionally,the anti-noise pollution ability analysis of the ISMDD strategy is carried out,which shows that the ISMDD strategy has excellent anti-noise pollution ability for both single-and multi-damaged members.展开更多
Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspectio...Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspection methods are inefficient and have low accuracy,while automated inspection machines are bulky and inconvenient to carry and use.Based on the shortcomings of existing detection technologies,this paper proposes a portable structural surface crack detection system based on the Android platform using a portable hand-held image acquisition device.The system captures cracks on the structure's surface and obtains high-definition crack images.Then,these images are transmitted to portable smartphone terminals through Wi-Fi.Next,the image is pre-processed using weighted averaging,grayscale linear transformation,and adaptive median filtering.Then,the improved Canny edge detection algorithm is applied to identify crack information,and the edge segmentation algorithm is used to determine the crack width.Finally,based on camera calibration,the pixels are converted into the length data required for actual measurement.The results show that the system is easy to operate,and it not only has crack storage and tracking functions,but also can effectively measure the crack width on the surface of components.The measurement accuracy of this system reaches the sub-pixel level,and in actual testing,compared with the crack width gauge,the maximum relative error does notexceed6.25%.展开更多
A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including ...A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam鈥揷olumn connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.展开更多
With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interf...With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.展开更多
The development of damage detection techniques for offshore jacket structures is vital to prevent catastrophic events. This paper applies a frequency response based method for the purpose of structural health monitori...The development of damage detection techniques for offshore jacket structures is vital to prevent catastrophic events. This paper applies a frequency response based method for the purpose of structural health monitoring. In efforts to fulfill this task, concept of the minimum rank perturbation theory has been utilized. The present article introduces a promising methodology to select frequency points effectively. To achieve this goal, modal strain energy ratio of each member was evaluated at different natural frequencies of structure in order to identify the sensitive frequency domain for damage detection. The proposed methodology opens up the possibility of much greater detection efficiency. In addition, the performance of the proposed method was evaluated in relation to multiple damages. The aforementioned points are illustrated using the numerical study of a two dimensional jacket platform, and the results proved to be satisfactory utilizing the proposed methodology.展开更多
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the National Major Science and Technology Project,China(No.J2019-Ⅳ-0007-0075)the Fundamental Research Funds for the Central Universities,China(No.JKF-20240036)。
文摘To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military standards.The PDT method holds the view that there exist defects such as machining scratches and service cracks in the tenon-groove structures of aeroengine disks.However,it is challenging to conduct PDT assessment due to the scarcity of effective Probability of Detection(POD)model and anomaly distribution model.Through a series of Nondestructive Testing(NDT)experiments,the POD model of real cracks in tenon-groove structures is constructed for the first time by employing the Transfer Function Method(TFM).A novel anomaly distribution model is derived through the utilization of the POD model,instead of using the infeasible field data accumulation method.Subsequently,a framework for calculating the Probability of Failure(POF)of the tenon-groove structures is established,and the aforementioned two models exert a significant influence on the results of POF.
基金supported by Natural Science Foundation of China(Grant No.52175488)Scientific Research Program for Young Outstanding Talent of Higher Education of Hebei Province(China)(Grant No.BJ2021045)S&T Program of Hebei(China)(Grant No.236Z1808G).
文摘High-performance lattice structures produced through powder bed fusion-laser beam exhibit high specific strength and energy absorption capabilities.However,a significant deviation exists between the mechanical properties,service life of lattice structures,and design expectations.This deviation arises from the intense interaction between the laser and powder,which leads to the formation of numerous defects within the lattice structure.To address these issues,this paper proposes a high-performance defect detection model for metal lattice structures based on YOLOv4,called YOLO-Lattice(YOLO-L).The main objectives of this paper are as follows:(1)utilize computed tomography to construct datasets of the diamond lattice and body-centered cubic lattice structures;(2)in the backbone network of YOLOv4,employ deformable convolution to enhance the feature extraction capability of the model for small-scale defects;(3)adopt a dual-attention mechanism to suppress invalid feature information and amplify the distinction between defect and background regions;and(4)implement a channel pruning strategy to eliminate channels carrying less feature information,thereby improving the inference speed of the model.The experimental results on the diamond lattice structure dataset demonstrate that the mean average precision of the YOLO-L model increased from 96.98% to 98.8%(with an intersection over union of 0.5),and the inference speed decreased from 51.3 ms to 32.5 ms when compared to YOLOv4.Thus,the YOLO-L model can be effectively used to detect defects in metal lattice structures.
基金supported by the Research Funding of Hangzhou International Innovation Institute of Beihang University(Grant No.015731201-2024KQ126)National Key R&D Program of China(Grant No.2023YFF0716600)National Natural Science Foundation of China(Grant No.62271021).
文摘The steel-epoxy-steel sandwich structures provide enhanced corrosion resistance and fatigue resistance,making them suitable for pipeline rehabilitation with effective repair and long-term durability.However,the repair quality can be compromised by disbond between the steel and epoxy layers,whichmay result frominsufficient epoxy injection.Conventional ultrasonic testing faces challenges in accurately locating disbond defects due to aliased echo interference at interfaces.This paper proposes a signal processing algorithm for improving the accuracy of ultrasonic reflection method for detecting disbond defects between steel and epoxy layers.First,a coati optimization algorithmvariational mode decomposition(COA-VMD)is applied to adaptively decompose the ultrasonic signals and extract the intrinsic mode function components that show high correlation with the defect-related signals.Then,by calculating the relative reflectance at the interface and establishing a quantitative evaluation index based on acoustic impedance discontinuity,the locations of disbond defects are identified.Experimental results demonstrate that this method can effectively detect the locations of disbond defects between steel and epoxy layers.
基金Supported by the Special Fund for Basic Scientific Research of Central-Level Public Welfare Scientific Research Institutes(2024-9007)。
文摘The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.
基金supported by the National Natural Science Foundation of China(No.52175269)the Innovative Research Groups of the National Natural Science Foundation of China(No.52021003)+2 种基金Natural Science Foundation of Jilin Province of China(No.20210101052JC)Science and Technology Research Project of Education Department of Jilin Province(JJKH20231146KJ,JJKH20241262KJ)China Postdoctoral Science Foundation(2024M751086).
文摘Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigid pressure sensors.However,arising from the limited compressibility of soft materials and the hardening of microstructures at the device interface,there is always a trade-off between high sensitivity and broad sensing range for most flexible pressure sensors,which results in a gradual saturation response and limits their practical applications.Herein,inspired by the distinct pressure perception function of crocodile receptors,a highly sensitive and wide-range flexible pressure sensor with multiscale microdomes and interlocked architecture is developed via a facile PS-decorated molding method.Combined with interlocked architecture,the multiscale dome-shaped structured interface enhances the compressibility of the material through structural complementarity,increases the contact area between functional materials,which compensates for the stiffness induced by the deformation of dense microscale columns.This effectively mitigates structural hardening across a wide pressure range,leading to the overall high performance of the sensor.As a result,the obtained sensor exhibits a low detection limit of 5 Pa,a high sensitivity of 6.14 kPa^(-1),a wide measurement range up to 231 kPa,short response/recovery time of 56 ms/69 ms,outstanding stability over 10,000 cycles.Considering these excellent properties,the sensor shows promising potential in health monitoring,human-computer interaction,wearable electronics.This study presents a strategy for the fabrication of flexible pressure sensors exhibiting high sensitivity and a wide pressure response range.
基金supported by the Nuclear Safety Research Program through Korea Foundation of Nuclear Safety(KoFONS)using the financial resource granted by the Nuclear Safety and Security Commission(NSSC)of the Republic of Korea(Grant number:2106061,50%)supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2025-25394739,Development of Security Enhancement Technology for Industrial Control Systems Based on S/HBOM Supply Chain Protection,50%).
文摘With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detection.Audit logs,such as Sysmon,offer valuable insights;however,existing approaches typically flatten event sequences or rely on generic graph models,thereby discarding the natural parent-child process hierarchy that is critical for analyzing multiprocess attacks.This paper proposes a structure-aware threat detection framework that transforms audit logs into a unified two-dimensional(2D)spatio-temporal representation,where process hierarchy is modeled as the spatial axis and event chronology as the temporal axis.In addition,entropy-based features are incorporated to robustly capture obfuscated and non-linguistic strings,overcoming the limitations of semantic embeddings.The model’s performance was evaluated on publicly available datasets,achieving competitive results with an accuracy exceeding 95%and an F1-score of at least 0.94.The proposed approach provides a promising and reproducible solution for detecting attacks with unknown indicators of compromise(IoCs)by analyzing the relationships and behaviors of processes recorded in large-scale audit logs.
基金The authors would like to thank CNPq(Conselho Nacional de Desenvolvimento Científico e Tecnológico)—grants 407256/2022-9,303550/2025-2,402533/2023-2 and 303982/2022-5FAPEMIG(Fundação de AmparoàPesquisa do Estado de Minas Gerais)—grants APQ-00032-24 and APD-01113-25 for their financial support.
文摘Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.
基金the National Key Research and Development Program of China(No.2023 YFC2811600)the National Natural Science Foundation of China(Nos.52301349,52088102)+1 种基金the Major Science and Technology Innovation Program of Qingdao(No.223-3-hygg-10-hy)the Qingdao Science Foundation for Post-doctoral Scientists(Nos.QDBSH20220202070,QDBSH20220201015)。
文摘A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.
文摘A kind of photoelectric system that is suitable to measuring and to testing the damage of the composite material intelligent structure was presented. It can measure the degree of damage of the composite intelligent structure and it also can tell us the damage position in the structure. This system consists of two parts ： software and hardware. Experiments of the damage detection and the analysis of the composite material structure with the photoelectric system were performed, and a series of damage detection experiments was conducted. The results prove that the performance of the system is well and the effects of the measure and test are evident. Through all the experiments, the damage detection technology and test system are approved to be real-time, effective and reliable in the damage detection of the composite intelligent structure.
基金This work was supported by National Natural Science Foundation of China (60574083)Key Laboratory of Process Industry Automation, Ministry ofEducation of China (PAL200514)Innovation Scientific Fund of Nanjing University of Aeronautics and Astronautics (Y0508-031)
文摘This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） is proposed for detecting serious faults, and variable structure （VS） model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation. Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.
基金Funded by the National Natural Science Foundation of China (51178305)the Key Projects in the Science & Technology Pillar Program of Tianjin (11ZCKFSF00300)
文摘Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied. At first, the wave propagation mechanisms in concrete were analyzed. Then, an active sensing system with integrated actuators/sensors was constructed. One PZT patch was used as an actuator to generate high frequency waves, and the other PZT patches were used as sensors to detect the propagating wave. Scattered wave signals from the damage can be obtained by subtracting the baseline signal of the intact structure from the recorded signal of the damaged structure. In the experimental study, progressive cracked damage inflicted artificially on the plain concrete beam is assessed by using both lateral and thickness modes of the PZT patches. The results indicate that with the increasing number and severity of cracks, the magnitude of the sensor output decreases for the surface bonded PZT patches, and increases for the embedded PZT patches.
基金Supported by National Natural Science Foundation of China(Grant No.61573233)Guangdong Provincial Natural Science Foundation of China(Grant No.2021A1515010661)Guangdong Provincial Special Projects in Key Fields of Colleges and Universities of China(Grant No.2020ZDZX2005).
文摘Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China （51209189, 51379196）, and the Natural Science Foundation of Shandong Province （ZR2013 EEQ006, ZR2011 EL049）
文摘The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） method for detecting damage in offshore platform structures based on a traditional modal strain energy method （the Stubbs index method）. The most significant difference from the Stubbs index method was the application of modal frequencies. The goal was to improve the robustness of the traditional method. To demonstrate the effectiveness and practicality of the proposed IMSE method, both numerical and experimental studies were conducted for different damage scenarios using a jacket platform structure. The results demonstrated the effectiveness of the IMSE method in damage location when only limited, spatially incomplete, and noise-polluted modal data is available. Comparative studies showed that the IMSE index outperformed the Stubbs index and exhibited stronger robustness, confirming the superiority of the proposed approach.
基金support from the European Union's Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement No.101024139,the RILEM technical committee TC 279 WMR(valorisation of waste and secondary materials for roads),RILEM technical committee TC-264 RAP(asphalt pavement recycling)the Swiss National Science Foundation(SNF)grant 205121_178991/1 for the project titled“Urban Mining for Low Noise Urban Roads and Optimized Design of Street Canyons”,National Natural Science Foundation of China(No.51808462,51978547,52005048,52108394,52178414,52208420,52278448,52308447,52378429)+9 种基金China Postdoctoral Science Foundation(No.2023M730356)National Key R&D Program of China(No.2021YFB2601302)Natural Science Basic Research Program of Shaanxi(Program No.2023-JC-QN-0472)Postdoctoral Science Foundation of Anhui Province(2022B627)Shaanxi Provincial Science and Technology Department(No.2022 PT30)Key Technological Special Project of Xinxiang City(No.22ZD013)Key Laboratory of Intelligent Manufacturing of Construction Machinery(No.IMCM2021KF02)the Applied Basic Research Project of Sichuan Science and Technology Department(Free Exploration Type)(Grant No.2020YJ0039)Key R&D Support Plan of Chengdu Science and Technology Project-Technology Innovation R&D Project(Grant No.2019-YF05-00002-SN)the China Postdoctoral Science Foundation(Grant No.2018M643520).
文摘As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.
基金supported by the Six Kinds of Peak Talents in Jiangsu Provincethe Momentous Research Plan in National Natural Science Foundation of China (No. 90915004)+1 种基金333 High-level Talent Project in Jiangsu Provincethe National Key Technology R&D Program of China (No. 2011BAK02B03)
文摘Damage detection based on strain responses of vibration is highly attractive for monitoring long-span reticulated structures.However,there are a lot of structure members in reticulated structures and it is impossible to install strain sensors in each member.Therefore,how to locate and quantify damages with the incomplete mode shapes obtained from few strain sensors is a challenge topic.A new strategy,named incomplete strain mode damage detection（ISMDD） strategy,is proposed in this paper.In the strategy,the distribution of the strain sensors in the reticulated structures can be optimized through sensitive analysis on strain mode perturbation matrix,which can be obtained by perturbation theory.Mode assurance criterion（MAC） value is applied in damage location,and the members with relative large MAC values are defined as damage members.In addition,damage index obtained by solving the perturbation equation is used for damage quantification.Numerical analysis on a long-span reticulated structure,including damage location and quantification for single-and multi-member damages,detection for different damage quantity,the effect analysis of sensor quantity,are performed to verify the effectiveness of the proposed ISMDD strategy.It can be shown from the analysis that the ISMDD strategy is effective in damage location and quantification for both single-and multi-member damages.And the quantity of strain sensors has effect on damage location,but has no obvious influence on damage quantification.Additionally,the anti-noise pollution ability analysis of the ISMDD strategy is carried out,which shows that the ISMDD strategy has excellent anti-noise pollution ability for both single-and multi-damaged members.
基金Supported by Shaanxi Provincial Key Research and Development Program(2024GX-YBXM-288)the National Natural Science Foundation of China(52172324)+1 种基金Beilin District Science and Technology Program(GX2350)the Special Fund Project for Basic Research Business Expenses of Central level Public Welfare Research Institutes(2023-9062)。
文摘Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspection methods are inefficient and have low accuracy,while automated inspection machines are bulky and inconvenient to carry and use.Based on the shortcomings of existing detection technologies,this paper proposes a portable structural surface crack detection system based on the Android platform using a portable hand-held image acquisition device.The system captures cracks on the structure's surface and obtains high-definition crack images.Then,these images are transmitted to portable smartphone terminals through Wi-Fi.Next,the image is pre-processed using weighted averaging,grayscale linear transformation,and adaptive median filtering.Then,the improved Canny edge detection algorithm is applied to identify crack information,and the edge segmentation algorithm is used to determine the crack width.Finally,based on camera calibration,the pixels are converted into the length data required for actual measurement.The results show that the system is easy to operate,and it not only has crack storage and tracking functions,but also can effectively measure the crack width on the surface of components.The measurement accuracy of this system reaches the sub-pixel level,and in actual testing,compared with the crack width gauge,the maximum relative error does notexceed6.25%.
基金supported by the National Natural Science Foundation of China (90815025, 90715032 and 50808013)
文摘A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam鈥揷olumn connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.
基金National Natural Science Foundation of China(No.62101219)Natural Science Foundation of Jiangsu Province(Nos.BK20201026,BK20210921)+1 种基金Science Foundation of Jiangsu Normal University(No.19XSRX006)Open Research Fund of Jiangsu Key Laboratory of Resources and Environmental Information Engineering(No.JS202107)。
文摘With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.
基金Financial Support by the Pars Oil and Gas Company(Grant No. 88-065)
文摘The development of damage detection techniques for offshore jacket structures is vital to prevent catastrophic events. This paper applies a frequency response based method for the purpose of structural health monitoring. In efforts to fulfill this task, concept of the minimum rank perturbation theory has been utilized. The present article introduces a promising methodology to select frequency points effectively. To achieve this goal, modal strain energy ratio of each member was evaluated at different natural frequencies of structure in order to identify the sensitive frequency domain for damage detection. The proposed methodology opens up the possibility of much greater detection efficiency. In addition, the performance of the proposed method was evaluated in relation to multiple damages. The aforementioned points are illustrated using the numerical study of a two dimensional jacket platform, and the results proved to be satisfactory utilizing the proposed methodology.
