Search Results (114)

This work introduces a novel approach to extract beam bridge mode shapes using the residual response between consecutive contact points of vehicles passing through a bridge. A comprehensive investigation is conducted on several critical parameters, including window size, vehicle velocity, road roughness, and beam damping property, as well as the influence of traffic flow. To enhance the mode shape extraction performance using the approximate expression of the contact points’ displacements under noisy disturbance, two new signal denoising methods, CEEMDAN-NSPCA and CEEMDAN-IWT, are proposed based on complete ensemble empirical mode decomposition (CEEMDAN). CEEMDAN-NSPCA integrates CEEMDAN with principal component analysis and a coefficient-based filtering strategy. While CEEMDAN-IWT utilizes an improved wavelet thresholding technique with adaptive threshold selection. The numerical simulations demonstrate that both methods could effectively attenuate high-frequency noise with small amplitudes and retain low-frequency components. Among them, CEEMDAN-IWT exhibits superior denoising performance and greater stability, making it particularly suitable for robust modal identification in noisy environments. Full article

Using a two-axle test vehicle, a new drive-by-based bridge damage identification method is proposed in this study. The method firstly obtains the vehicle acceleration response of a vehicle passing through an undamaged bridge and a damaged bridge; then, the acceleration response is processed using successive variational modal decomposition (SVMD) to obtain the intrinsic modal function (IMF) corresponding to the driving frequency; finally, the difference of the IMF is used to construct a damage indicator for damage identification of the bridge. The main findings of this study are as follows: (1) the constructed damage index can successfully identify single and multiple damages of bridges; (2) even in the case of pavement roughness, the proposed damage index is still able to identify the location of the damage; (3) the constructed damage index is not only applicable to simply supported bridges, but also applicable to the damage identification of continuous bridges; (4) the experiment shows that the proposed damage index can successfully identify the damage location, but the local vibration of the vehicle and the measurement noise interfere with the damage identification effect severely. Full article

The vehicle scanning method (VSM) is widely used for bridge damage identification (BDI) because it relies solely on vehicle dynamic responses. The recently introduced contact point response, which is derived from vehicle dynamics but devoid of vehicle-related natural frequencies, shows great potential for application in the vehicle scanning method. However, its application in bridge damage detection remains understudied. The aim of this paper is to propose a new bridge damage identification method based on the contact point response. The method uses variational modal decomposition (VMD) to solve the problem of mode mixing and spurious frequencies in the signal. The continuous wavelet transform (CWT) is then utilized for damage identification. The introduction of variational modal decomposition makes the extracted signal more accurate, thus enabling more accurate damage identification. Numerical simulations validate the method’s robustness under varying conditions, including the vehicle speed, wavelet scale factors, the number of bridge spans, and pavement roughness. The results demonstrate that variational modal decomposition eliminates signal artifacts, producing smooth variational modal decomposition–continuous wavelet transform curves for accurate damage detection. In this study, we offer a robust and practical solution for bridge health monitoring using the vehicle scanning method. Full article

Bridges play a key and controlling role in transportation systems. Steel bridges are favored for their high strength, good seismic performance, and convenient construction. As important node connectors of steel bridges, high-strength bolts are extremely susceptible to damage such as corrosion and loosening. Therefore, accurate identification of bolt loosening is crucial. First, a new type of adhesive piezoelectric sensor is designed and prepared using PMN-PT piezoelectric single-crystal materials. The PMN-PT sensor and polyvinylidene fluoride (PVDF) sensor are subjected to steel plate fixed frequency load and swept frequency load tests to test the performance of the two sensors. Then, a steel plate component connected by high-strength bolts is designed. By applying exciter square wave load to the structure, the vibration response characteristics of the structure are analyzed to identify the loosening of the bolts. In addition, a piezoelectric smart washer sensor is designed to make up for the shortcomings of the adhesive piezoelectric sensor, and the effectiveness of the piezoelectric smart washer sensor is verified. Finally, a bolt loosening index is proposed to quantitatively evaluate the looseness of the bolt. The results show that the sensitivity of the PMN-PT sensor is 21 times that of the PVDF sensor. Compared with the peak stress change, the natural frequency change is used to identify the bolt loosening more effectively. Piezoelectric smart washer sensor and bolt loosening indicator can be used for bolt loosening identification. Full article

This study presents an innovative approach to processing vibration signals in bridge structures, with a focus on enhancing the accuracy of dynamic response measurements and structural health assessments. It addresses key challenges in signal processing, particularly the uncertainties in selecting filtering parameters for isolating dynamic components from static displacements. A novel method for adaptive filter parameter selection is proposed, which considers variations in resonant frequencies and the non-linearity of quasi-static displacements caused by moving loads. This approach significantly reduces errors in determining forced and natural vibration parameters, leading to more accurate assessments of the bridge’s mechanical characteristics. The study introduces an optimized algorithm for processing acceleration and velocity signals, improving the resolution of natural frequency identification. This method combines traditional Fast Fourier Transform (FFT) techniques with an innovative spectral analysis approach, enabling precise identification of resonant frequencies and damping coefficients. A comprehensive evaluation framework is developed, integrating vibration amplitude, frequency, and damping ratio analyses. This framework enhances structural health assessments, improving the detection and characterization of potential defects and changes in load-bearing capacity. The practical significance of this research lies in its real-world application to bridge diagnostics. The study provides guidelines for sensor selection and configuration, adapted for various bridge types and sizes. The proposed methods demonstrate notable improvements in dynamic coefficient determination and overall structural assessments, offering the potential to reduce maintenance costs and enhance bridge safety. Full article

The integration of digital twin technology with structural health monitoring (SHM) is revolutionizing the assessment and maintenance of critical infrastructure, particularly bridges. Digital twins—virtual, data-driven replicas of physical structures—enable real-time monitoring by continuously synchronizing sensor data with computational models. This study presents the development of a real-time digital twin for a three-span steel railway bridge, utilizing a high-fidelity finite element (FE) model built using OpenSeesPy v 3.5 and instrumented with 18 strategically placed accelerometers. The dynamic properties of the bridge are extracted using Stochastic Subspace Identification (SSI), enabling an accurate estimation of modal parameters. To enhance the fidelity of the digital twin, a genetic algorithm-based model-updating strategy is implemented, optimizing the steel elastic modulus to minimize discrepancies between measured and simulated frequencies and mode shapes. The results demonstrate a remarkable reduction in frequency errors (below 5%) and a significant improvement in modal shape correlation (MAC > 0.93 post-calibration), confirming the model’s ability to reflect the bridge’s true condition. This work underscores the potential of digital twins in predictive maintenance, early damage detection, and life-cycle management of bridge infrastructure, offering a scalable framework for real-time SHM in complex structural systems. Full article

Strategically important objects, such as dams, tunnels, bridges, and others, require long-term structural health monitoring programs in order to preserve their structural integrity with minimal downtime, financial expenses, and increased safety for civilians. The current study focuses on developing a damage detection methodology that is applicable to the long-term monitoring of such structures. It is based on the identification of resonant frequencies from operational modal analysis, removing the effect of environmental factors on the resonant frequencies through support vector regression with optimized hyperparameters and, finally, classifying the global structural state as either healthy or damaged, utilizing the Mahalanobis distance. The novelty lies in two additional steps that supplement this procedure, namely, the nonlinear estimation of the relative effects of various environmental factors, such as temperature, humidity, and ambient loads on the resonant frequencies, and the selection of the most informative resonant frequency features using a non-parametric neighborhood component analysis algorithm. This methodology is validated on a wooden two-story truss structure with different artificial structural modifications that simulate damage in a non-destructive manner. It is found that, firstly, out of all environmental factors, temperature has a dominating decreasing effect on resonance frequencies, followed by humidity, wind speed, and precipitation. Secondly, the selection of only a handful of the most informative resonance frequency features not only reduces the feature space, but also increases the classification performance, albeit with a trade-off between false alarms and missed damage detection. The proposed approach effectively minimizes false alarms and ensures consistent damage detection under varying environmental conditions, offering tangible benefits for long-term SHM applications. Full article

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA’s effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA’s potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments. Full article

Structural health monitoring commonly uses natural frequency analysis to assess structural conditions, but direct frequency shifts are often insensitive to minor damage and susceptible to environmental influences like temperature variations. Traditional methods, whether based on absolute frequency changes or theoretical models like PCA and GMM, face challenges in robustness and reliance on model selection. These limitations highlight the need for a more adaptive and data-driven approach to capturing the intrinsic nonlinear correlations among multi-order modal frequencies. This study proposes a novel approach that leverages the nonlinear correlations among multi-order natural frequencies, which are more sensitive to structural state changes. A deep learning framework integrating CNN-BiLSTM-Attention is designed to capture the spatiotemporal dependencies of multi-order frequency data, enabling the precise modeling of intrinsic correlations. The model was trained exclusively on healthy-state frequency data and validated on both healthy and damaged conditions. A probabilistic modeling approach, incorporating Gaussian distribution and cumulative probability functions, was used to evaluate the estimation accuracy and detect correlation shifts indicative of structural damage. To enhance the robustness, a moving average smoothing technique was applied to reduce random noise interference, and damage identification rates over extended time segments were calculated to mitigate transient false alarms. Validation experiments on a mass-spring system and the Z24 bridge dataset demonstrated that the proposed method achieved over 95% damage detection accuracy while maintaining a false alarm rate below 5%. The results validate the ability of the CNN-BiLSTM-Attention framework to effectively capture both structural and environmental nonlinearities, reducing the dependency on explicit theoretical models. By leveraging multi-order frequency correlations, the proposed method provides a robust and highly sensitive approach to structural damage identification. These findings confirm the practical applicability of deep learning in damage identification during the operational phase of structures. Full article

In structural health monitoring, effectively eliminating the influence of variable environmental conditions on modal frequencies remains a critical challenge for accurate damage identification. Nonstationary and nonlinear variations in modal frequencies, commonly induced by environmental changes, tend to overshadow the effects caused by structural damage. An improved Gaussian mixture model (GMM) is proposed in this paper to normalize nonlinear and nonstationary frequency data, enabling effective structural damage detection under variable environmental conditions. As the effectiveness of the GMM is highly influenced by the initial parameter values used in the expectation-maximization (EM) algorithm, a subdomain division strategy is first presented to determine the unique initial values of the GMM parameters. Through the application of the EM algorithm, the GMM is constructed simply and efficiently through the determined initial parameters. Next, on the basis of the constructed GMM, the modal frequency data are normalized to extract damage features that remain unaffected by environmental variations. Subsequently, Hotelling’s T2 statistic and its cumulative form are calculated for the damage features and designated as the damage indicators; meanwhile, the corresponding damage thresholds are also calculated according to the kernel density estimation technique. To validate the proposed method, two case studies are conducted: one with a numerical mass-spring system and the other with a real bridge structure. Results show that environmental influences no longer impact the normalized frequency data, and the cumulative statistic demonstrates outstanding accuracy in identifying structural damage. Full article

This study introduces a multi-point optical flow cable force measurement method based on Euler motion amplification to address challenges in accurately measuring cable displacement under small displacement conditions and mitigating background interference in complex environments. The proposed method combines phase-based magnification with an optical flow method to enhance small displacement features and improve SNR (signal-to-noise ratio) in cable displacement tracking. By leveraging magnified motion data and integrating auxiliary feature points, the approach compensates for equipment-induced vibrations and background noise, allowing for precise cable displacement measurement and the identification of vibration modes. The methodology was validated using a scaled model of a cable net structure. The results demonstrate the method’s effectiveness, achieving a significantly higher SNR (e.g., from 7.5 dB to 22.24 dB) compared to traditional optical flow techniques. Vibration frequency errors were reduced from 6.2% to 1.5%, and cable force errors decreased from 11.38% to 3.13%. The multi-point optical flow cable force measurement method based on Euler motion magnification provides a practical and reliable solution for non-contact cable force measurement, offering potential applications in structural health monitoring and the maintenance of bridges and high-altitude structures. Full article

A method of bridge structure seismic response identification combining signal processing technology and deep learning technology is proposed. The short-time energy method is used to intelligently extract the non-smooth segments in the sensor acquired signals, and the short-time Fourier transform, continuous wavelet transform, and Meier frequency cestrum coefficients are used to analyze the spectrum of the non-smooth segments of the response of the bridge structure, and the response feature matrix is extracted and used to classify sequences or images in the LSTM network and the Resnet50 network. The results show that the signal processing techniques can effectively extract the structural response features and reduce the overfitting phenomenon of neural networks, and the combination of signal processing techniques and deep learning techniques can recognize the seismic response of bridge structures with high accuracy and efficiency. Full article

Cables and tendons are crucial elements in bridge engineering but also are vulnerable structural elements because they are usually subjected to fatigue and corrosion problems. Thus, vibration-based non-destructive techniques have been used for external post-tensioning tendon assessment. Regarding continuous monitoring systems, tendon assessment is carried out through the continuous tracking of its natural frequencies and the subsequent estimation of the tension force, as this parameter is essential for the bridge’s overall structural performance, thus providing useful information about bridge safety. However, for long-term monitoring assessment, two main challenges have to be addressed regarding practical applications: (i) double-peak spectra and other spurious factors that affect the frequency estimation, and (ii) temperature dependency, which needs to be carefully treated since frequency/tension variation may be explained by temperature variation, thus masking potential structural anomalies. On this subject, this paper presents the experimental long-term monitoring of several post-tensioning external tendons in a high-speed railway bridge in which a sectorized weighted peak-picking frequency identification procedure is proposed for frequency estimation, alongside a cascade clustering process, which allows meaningful frequency estimates to be selected. Finally, the selected frequency estimates, which show variations from 1 to 2% for all analyzed frequencies, are used for the long-term assessment of the tension force. Full article

Effective placement and compaction of the concrete mixture within the spans of prestressed bridges are essential for the proper anchoring and prestressing of tendons. The high density of reinforcement and location of the cable ducts present significant challenges, increasing the risk of void formation and structural irregularities, which can lead to failures during the prestressing process. Ground Penetrating Radar (GPR) emerges as a pivotal non-destructive testing method for diagnosing such complex prestressed structures. Utilizing high-frequency electromagnetic waves, GPR accurately detects and maps anomalies within hardened concrete, enabling precise identification of defect locations and their dimensions. The detailed imaging provided by GPR facilitates the development of targeted repair strategies and allows for the exclusion of concrete voids through selective invasive inspections in designated boreholes. This study presents the use of GPR for the investigation of anomalies and damage in prestressing tendons of a newly built concrete bridge. It underscores the critical role of GPR in enhancing the diagnostic and maintenance programs for prestressed bridge structures, thereby improving their overall integrity and longevity. Full article

Effective noise reduction strategies are crucial for improving the precision of Ground-Based Synthetic Aperture Radar (GB-SAR) technology in bridge deflection measurement, particularly in mitigating the signal noise introduced by complex environmental factors, and thereby ensuring reliable structural health assessments. This study presents an innovative gradual de-noising method that integrates an Improved Second-Order Blind Identification (I-SOBI) algorithm with Fast Fourier Transform (FFT) featuring Adaptive Cutoff Frequency Selection (A-CFS) for reducing the complex environmental noises. The novel method is a two-stage process. The first stage employs the proposed I-SOBI to preserve the contribution of effective information in separated signals as much as possible and to recover pure signals from noisy ones that have nonlinear characteristics or are non-Gaussian in distribution. The second stage utilizes the FFT with the A-CFS method to further deal with the residual high-frequency noises still within the signals, which is conducted under a proper cutoff frequency to ensure the quality of de-noised outputs. Through meticulous simulation and practical experiments, the effectiveness of the proposed de-noising method has been comprehensively validated. The experimental results state that the method performs better than the traditional Second-Order Blind Identification (SOBI) method in terms of noises reduction capabilities, achieving a higher accuracy of bridge deflection measurement using GB-SAR. Additionally, the method is particularly effective for de-noising nonlinear time-series signals, making it well-suited for handling complex signal characteristics. It significantly contributes to the provision of reliable bridge dynamic-behavior information for infrastructure assessment. Full article