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This study proposes a new method for the rapid and non‐contact measurement of cable forces in cable‐stayed bridges, including a cable force calculation method based on measured cable shapes and a batch acquisition method for the true shape of cables. First, a nonlinear regression model for estimating cable forces based on measured cable shapes is established, and a Gauss–Newton‐based cable force solving method is proposed. Furthermore, terrestrial laser scanning technology is used to collect geometric data of the cables. Meanwhile, automatic segmentation, noise reduction, and centerline extraction algorithms for the cable point cloud are proposed to accurately and efficiently obtain the cable shape. The correctness of the proposed cable force calculation method is verified in a well‐designed experiment, with the measurement error of cable forces for 15 test samples being less than 1%. Finally, the proposed point cloud automation processing algorithm and cable force measurement method are fully tested on a cable‐stayed bridge with a span of 460 m. The measurement accuracy of the proposed method for actual bridge cable tension is comparable to that of the frequency method, but the detection efficiency on site is nine times higher than that of the traditional frequency method. Overall, this study provides a new measurement method for construction control, health monitoring, intelligent detection, and other fields of cable‐stayed bridges. [ABSTRACT FROM AUTHOR]

In bridge structure monitoring and evaluation, deformation data serve as a crucial basis for assessing structural conditions. Different from discrete monitoring points, spatially continuous deformation modes provide a comprehensive understanding of deformation and potential information. Terrestrial laser scanning (TLS) is a three-dimensional deformation monitoring technique that has gained wide attention in recent years, demonstrating its potential in capturing structural deformation models. In this study, a TLS-based bridge deformation mode monitoring method is proposed, and a deformation mode calculation method combining sliding windows and surface fitting is developed, which is called the SWSF method for short. On the basis of the general characteristics of bridge structures, a deformation error model is established for the SWSF method, with a detailed quantitative analysis of each error component. The analysis results show that the deformation monitoring error of the SWSF method consists of four parts, which are related to the selection of the fitting function, the density of point clouds, the noise of point clouds, and the registration accuracy of point clouds. The error caused by point cloud noise is the main error component. Under the condition that the noise level of point clouds is determined, the calculation error of the SWSF method can be significantly reduced by increasing the number of points of point clouds in the sliding window. Then, deformation testing experiments were conducted under different measurement distances, proving that the proposed SWSF method can achieve a deformation monitoring accuracy of up to 0.1 mm. Finally, the proposed deformation mode monitoring method based on TLS and SWSF was tested on a railway bridge with a span of 65 m. The test results showed that in comparison with the commonly used total station method, the proposed method does not require any preset reflective markers, thereby improving the deformation monitoring accuracy from millimeter level to submillimeter level and transforming the discrete measurement point data form into spatially continuous deformation modes. Overall, this study introduces a new method for accurate deformation monitoring of bridges, demonstrating the significant potential for its application in health monitoring and damage diagnosis of bridge structures. [ABSTRACT FROM AUTHOR]

Early warning of existing bridges is now predominated by deterministic methods. However, these methods face challenges in expressing uncertain factors (such as wind load, temperature load, and other variables, etc.). These problems directly impact the timeliness and accuracy of bridge early warning. This study develops an innovative method for bridge dynamic early warning with high versatility and accuracy. Long short-term memory network model (LSTM), expectation maximization (EM) and Gaussian mixture model (GMM) were employed in the proposed method. Firstly, the LSTM model is used to predict the measured monitoring data (such as deflection, strain, cable force, etc.) in real time to obtain the predicted results. Next, the number of clusters for the EM-GMM model is determined using the Calinski-Harabasz (CH) index. The method aims to comprehensively consider the internal cohesion of the clustering, ensuring accurate and reliable clustering results. Then, the EM-GMM model is used to cluster the random influence error and the predicted value, which can get the probabilistic prediction result of each corresponding random influence error. On this basis, the dynamic early warning interval under 95% confidence level is constructed. This facilitates early warning and decision-making for potential structural abnormalities. Finally, the accuracy and practicability of the method are verified by the comparison of engineering applications and existing specifications. The results demonstrate that the probabilistic early warning method considering the uncertain factors in the complex service environment can accurately achieve the dynamic early warning of bridges. [ABSTRACT FROM AUTHOR]

Aiming at comprehensively evaluating the status of a bridge monitoring system, an evaluation framework based on the improved Delphi, analytic Hierarchy process, Grey relations analysis and Fuzzy integrated evaluation (DHGF) is selected. Firstly, the evaluation indexes for the bridge monitoring system are determined by an anonymous group discussion and expert questionnaire using the improved Delphi method. Secondly, a comparison matrix of the evaluation indexes is constructed to determine the comprehensive weight via the analytic hierarchy process. Then, based on the gray relations analysis, the albino weight function is constructed, the evaluation gray class is determined, and the single-factor fuzzy evaluation matrix is obtained. Finally, the final evaluation result was obtained by the fuzzy comprehensive evaluation. The evaluation results of a real bridge monitoring system show that the evaluation level of the monitoring system was level II, and the proposed framework could better reflect the construction and operation status of the monitoring system. [ABSTRACT FROM AUTHOR]

Temperature has a significant influence on bridge strain monitoring data. To improve the accuracy of temperature effect separation in strain monitoring data, this paper proposes a temperature effect separation method comprising variational nonlinear chirp mode decomposition (VNCMD), principal component analysis (PCA) and blind source separation. Firstly, VNCMD was used to decompose the monitoring data of strain and temperature, and the intrinsic mode functions (IMF) of strain and temperature signals were obtained. Secondly, PCA was used to reduce the dimension of IMF, and the false components were eliminated to select the optimal components. After reducing the dimension, the components were used as the input of fast independent component analysis model for blind source separation. Finally, the feasibility and accuracy of the method was verified via the signal-to-noise ratio (SNR) in the simulated signal, and the separation results were evaluated using the Pearson correlation coefficient between the strain component and the corresponding temperature component in real bridge monitoring data. The proposed method performed better than the empirical mode decomposition (EMD) method. The signal-to-noise ratio (SNR) of VNCMD improved 51.80% for daily temperature difference effect and 32.41% for annual temperature difference effect in the numerical study, respectively; the correlation coefficients of VNCMD improved 52.90% for daily temperature difference effect and 4.26% for annual temperature difference effect in practical verification, respectively. [ABSTRACT FROM AUTHOR]

In conventional buffeting analyses of flexible civil engineering structures, the differences between the effects of longitudinal (u-) and vertical (w-) turbulence components were usually ignored. This assumption could cause unpredictable errors and needs to be refined. To improve the accuracy of buffeting analyses, this paper proposes a framework combining Computational Fluid Dynamics simulation and wind tunnel test to analyze the buffeting forces considering the differences between turbulence components. First, the Aerodynamic Admittance Functions (AAFs) with respect to u- and w- turbulence are numerically evaluated. Next, the total buffeting forces are experimentally measured. Finally, the numerical and experimental results are combined following a theoretical scheme to separate the effects of u- and w-turbulence. Results show that u- and w- turbulence have different contributions to the buffeting forces, directly indicating the inaccuracy of the conventional assumption. In the turbulence field investigated, the buffeting lift force at zero angle of attack (AoA) are all contributed from the w-turbulence, while the contribution from u-turbulence increase as AoA increases. This numerical-experimental framework overcomes the limitations of the conventional method by quantitatively describing the different contributions of u- and w- turbulence. [ABSTRACT FROM AUTHOR]

The damage monitoring and identification of arch bridges provide an important means to ensure the safe operation of arch bridges. At present, many methods have been developed, and the applicability and effectiveness of these methods depend on the damage type, structural configuration and available data. To guide the practical application of these methods, a systematic review is implemented in this paper. Specifically, the damage monitoring and identification methods of arch bridges are divided into the damage monitoring of local diseases and damage identification of overall performance. Firstly, the research on the damage monitoring of the local diseases of arch bridges is reviewed. According to the disease type, it is divided into four categories, including suspender inspection, void monitoring, stress detection and corrosion detection. For each disease, this paper analyzes the principles, advantages and shortcomings of various methods. Then, the damage identification methods of the overall performance of arch bridges are reviewed, including masonry arch bridges, steel arch bridges, reinforced concrete arch bridges and concrete-filled steel tubular arch bridges. And the commonly used damage indexes of damage identification methods are summarized. This review aims to help researchers and practitioners in implementing existing damage detection methods effectively and developing more reliable and practical methods for arch bridges in the future. [ABSTRACT FROM AUTHOR]

A reasonable and efficient loading scheme is needed to guarantee the success of large-scale bridge tests. In this study, an array-type, self-balancing pulley-group loading system was designed based on the world's largest spanning arch bridge using a 1:10 scale model test. Automatic statistics of the required load at each loading point were realized using ANSYS, and a load optimization algorithm for loading points at different construction stages was proposed. Tests were carried out separately for the loading system using a single set of pulley groups and an array-type pulley group. Finite element models of the model bridge and the original bridge were established separately using ANSYS, and the stress results of different components during different construction stages of the main arch ring were compared. The research results show the following: (1) The load magnification factor of the single-pulley-group loading device is approximately 6.6 times, with a mechanical efficiency of 94.26%. (2) In the array-type loading system, the actual load at each loading point can reach the design value. The self-balancing characteristic of this system can eliminate the impact of vertical deformation of the structure on loading accuracy, verifying the reliability of the system. (3) The simulation results of the original bridge and the model bridge coincide well, and the stress of each component during the construction process has the same trend. At key construction stages, the maximum relative errors of the stress results of the rigid steel frame and the concrete inside the pipe of the two bridges are 8.33% and 9.34%, respectively, and the maximum absolute error of the bottom plate concrete is 0.66 MPa, verifying the correctness of the counterweight-optimization method. The loading scheme proposed in this paper can provide a reference for the design of loading systems with the same type of scale model test. [ABSTRACT FROM AUTHOR]

The bridge deflection data measured in field are greatly affected by temperature. In some situations, temperature can be the dominant factor comparing with loads and other factors. Generally, dynamic deflection contains information related to the health condition of bridge. It is wildly used in dynamic deflection warning system. The temperature-induced deflection might lead to the inaccuracy of such system. Therefore, it is important to separate the temperature-induced deflection when implement bridge health condition assessment using dynamic deflection data. However, the accuracy of existing methods of temperature-induced deflection separation is low. This low accuracy is normally caused by mode aliasing and excessive elimination of feature components, etc. To this end, this study proposed an innovative method of temperature-induced deflection separation based on the time-varying filtered empirical mode decomposition (TVFEMD), permutation entropy (PE) and Kullback–Leibler divergence (KLD). The proposed method is used to extract temperature-induced deflection from bridge deflection data. It overcomes the problems of mode aliasing and end-point effects by reconstructing the signals with the same composition. The separating accuracy is also improved by the non-linear feature extracting ability of the proposed method. First, TVFEMD was introduced to decompose the original bridge deflection data into a number of intrinsic mode functions (IMFs). Then, PE was adopted to measure the complexity of each IMF, by which all IMFs can be reconstructed into several new subseries. Furthermore, KLD was employed to calculate the divergence values between these reconstructed subseries and the measured ambient temperatures. The subseries with the smallest divergence value can be considered to be the temperature-induced deflection. Finally, numerical study and locale measurement were utilized to comprehensively validate the effectiveness of the proposed method. The results show that the identification accuracy of the proposed method exceeds 90%. It outperforms the other involved models in terms of separating daily and annual temperature-induced deflection. [ABSTRACT FROM AUTHOR]

Rapid and accurate identification of moving load is crucial for bridge operation management and early warning of overload events. However, it is hard to obtain them rapidly via traditional machine learning methods, due to their massive model parameters and complex network structure. To this end, this paper proposes a novel method to perform moving loads identification using MobileNetV2 and transfer learning. Specifically, the dynamic responses of a vehicle–bridge interaction system are firstly transformed into a two-dimensional time-frequency image by continuous wavelet transform to construct the database. Secondly, a pre-trained MobileNetV2 model based on ImageNet is transferred to the moving load identification task by transfer learning strategy for describing the mapping relationship between structural response and these specified moving loads. Then, load identification can be performed through inputting bridge responses into the established relationship. Finally, the effectiveness of the method is verified by numerical simulation. The results show that it can accurately identify the vehicle weight, vehicle speed information, and presents excellent strong robustness. In addition, MobileNetV2 has faster identification speed and requires less computational resources than several traditional deep convolutional neural network models in moving load identification, which can provide a novel idea for the rapid identification of moving loads. [ABSTRACT FROM AUTHOR]

Paper [[14]] proposed a response surface method to further improve the accuracy of alignment prediction for large-span steel-concrete composite beam track cable-stayed bridges. Paper [[5]] investigated the effect of pile foundation cutting and under-pinning processes on the stability of bridge substructures during shield tunneling. They utilized numerical simulations and on-site monitoring to study how the active underpinning process of shield tunneling pile foundations affected bridge substructure deformations. [Extracted from the article]

In the present study, multiple-fan active control wind tunnel tests are conducted to investigate the aerodynamic forces on a 5:1 rectangular cylinder in sinusoidal streamwise winds with different angles of attack (AoA). The effects of the frequency, amplitude, and AoA of the sinusoidal flow on the statistical parameters, spectral characteristics, and spanwise distributions of drag, lift, and moment coefficients are analyzed. Results show that each force has two components: the one induced by the approaching velocity oscillation and the one induced by wake vortex-shedding—this is quite different from that in the smooth flows, where the fluctuating forces are totally due to wake vortex-shedding. For each force, changes of the two components and their relationship with the frequency, amplitude, and AoA are presented. The drag fluctuations are generally dominated by the approaching velocity oscillations, whereas the lift and moment are more sensitive to wake vortex-shedding. Therefore, the drag force has better spanwise correlations than the lift and moment forces. Meanwhile, at a non-zero AoA, the inflow amplitude has different effects on the vortex-shedding-induced component as that at a zero AoA. The differences of spanwise distributions between the sinusoidal flow cases and the smooth flow cases are analyzed. [ABSTRACT FROM AUTHOR]

A stress concentration at a crack tip may cause fracture initiation even under low-stress conditions. The maximum axial stress theory meets the challenges of explaining the fracture propagation of a non-closed fracture of cracked concretes under compressional loading. Uniaxial loading tests of single-crack concrete specimens were carried out and a numerical simulation of fracture propagation under uniaxial compression was performed. The radial shear stress criterion for a mode-II fracture is proposed to examine the stress intensity factor (SIF) of the pre-crack specimens under compressional loading. When the maximum radial shear stress at the crack tip is larger than the maximum axial tensile stress, and the maximum dimensionless SIFs can satisfy frθmax/fθmax > 1 and frθmax/fθmax > KIIC/KIC ( f θ max = K Ie / σ y π a and f r θ max = K II e / σ y π a are maximum dimensionless mode-I and mode-II SIFs, respectively), the crack will extend along the direction of the maximum radial shear stress. The influence of the single-crack angle and width on the mechanical properties of the specimens was examined. The experimental and numerical results indicate that the existence of cracks can considerably weaken the strength of the specimen. The distribution and width of the cracks had a significant effect on the specimen strength. The strength of the concrete specimen initially decreased and then increased with increasing fracture angle. The failure mechanism and rupture angle of pre-crack brittle material while considering crack width will be discovered. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) has the potential to estimate the thickness and density of asphalt pavement during compaction. However, the surface moisture sprayed by the compactor interferes with the accuracy of data collection significantly. This study proposed an approach based on the extended common midpoint (XCMP) method to minimize the effect of surface moisture. Both the numerical simulation of finite-difference time-domain (FDTD) and laboratory experiments were carried out to study the effect of the surface moisture on the GPR signal. Then, three FDTD models with different incident angles of GPR signal were established, and the difference of time intervals obtained from dry and moisture pavements with each model was studied to propose a proper antennas installation mode. Finally, the thickness and density estimated using the proposed method and surface reflection method were compared to validate the accuracy of the proposed approach. The results show that: 1) FDTD models were verified to simulate the interaction of GPR signal with moisture pavement effectively; 2) the time interval of the GPR signal between the surface and bottom of AC layer increased as the thin wet layer dielectric constant grew, and remained unaffected by the electric conductivity of the thin wet layer; 3) the average error of thickness and density predicted utilizing the proposed method were less than 1.3% and 2.4%, respectively, undercomplicated compaction conditions. This study notes that compaction monitoring in real time could benefit from the proposed method. [ABSTRACT FROM AUTHOR]

Traditional autoregressive (AR) model-based damage identification methods construct structural damage sensitive features by trial and error, which are time-consuming, laborious and may lead to poor recognition effect. This study applies convolutional neural networks (CNNs) to quickly and automatically extract high-dimensional features of autoregressive model coefficients (ARMCs). In this research, AR model was utilized to fit the acceleration time series. The input matrices marked with damage location were produced by ARMCs, and then those matrices were sent to the proposed CNN for training. The trained CNN was employed for damage identification and localization. The effectiveness of the proposed method was verified by the damage identification and localization of a three-storied frame structure. The performance of the proposed CNN was compared with multilayer perception (MLP), random forest, and support vector machine (SVM). Meanwhile, the prediction results from different sample types were also discussed. Furthermore, parametric study in relation to the number of accelerometers and ARMCs used is conducted. These analyses demonstrate that the accuracy of CNN tests results reach 100%, 6.67%, 20%, and 25% higher than that of MLP, random forest, and SVM, respectively. Besides, other metrics calculated in this paper (e.g., precision, recall) further indicate that the proposed CNN performs well. The combination of AR and CNN does show excellent performance in damage identification and localization, which seems to be able to resist external excitation changes and accurately identify the multi-location damage and minor damage using limited accelerometers and ARMCs. [ABSTRACT FROM AUTHOR]

With an extension in service years, bridges inevitably suffer from performance deterioration. Columns are the main components of bridge structures, which support the superstructure. The damage of pier columns is often more harmful to bridges than that of other components. To accurately evaluate the time-varying characteristics of corroded columns, this paper proposes a new model for the bearing capacity evaluation of deteriorated reinforced concrete (RC) eccentric compression columns based on the Hermite interpolation and Fourier function. Firstly, the axial compression point, the pure bending point and the balanced failure point were selected as the basic points, and the deteriorated strength of these basic points was calculated by considering factors such as concrete cracking, reduction of reinforcement area, buckling of the steel bar, bond slip and strength reduction of confined concrete. After that, the interpolation points were generated by a piecewise cubic Hermite interpolating polynomial, and the explicit expression of the interpolation points fitting function was realized by the trigonometric Fourier series model. Finally, comparison studies based on measured data from forty-five corroded RC eccentric compression columns were conducted to investigate the accuracy and efficiency of the proposed method. The results show that: (1) the prediction results for bearing capacity of corroded RC columns are in good agreement with the measured data, with the average ratio of predicted results to test results at 1.06 and the standard deviation at 0.14; (2) the proposed model unifies the three stress states of axial compression, eccentric compression and pure bending, and is consistent with the continuum mechanics characteristics; (3) the decrements of axial load carrying capacity for 10% and 50% of the corrosion rate are 31.4% and 45.2%, while in flexure they are 25.4% and 77.4%, respectively; and (4) the test data of small-scale specimens may overestimate the negative effect of corrosion on the bearing capacity of actual structures. The findings in this paper could lay a solid starting point for structural life prediction technologies based on nondestructive testing. [ABSTRACT FROM AUTHOR]

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology. [ABSTRACT FROM AUTHOR]

The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation. [ABSTRACT FROM AUTHOR]