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Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video. [ABSTRACT FROM AUTHOR]

Recently, the demand for high-precision balancing of rotors has increased in the automobile industry, as more rotors are designed to rotate at ever-higher speeds to maximize energy efficiency. The accumulation of measurement uncertainty in the balancing process decreases the accuracy of the unbalanced mass estimation, which is the ultimate goal of balancing. Here, the problem of uncertainty is shown through a Monte Carlo simulation of signals acquired from an actual production line. To reduce the effect of measurement uncertainty in the balancing procedures, a signal-processing technique that increases the dynamic reliability of the signal is proposed. The suggested method is based on density-based spatial clustering of applications with noise (DBSCAN) with the use of the orthogonality-based averaging method. Specifically, by adjusting radius values while clustering samples through the use of the DBSCAN method, the outliers that arise due to uncertainty are successfully removed. In this work, our proposed automatic-adaptive DBSCAN (AA-DBSCAN) method is validated by applying it to a balancing machine used for blower rotors in fuel cell electric vehicles. The results show that the deviation of the extracted influence coefficients is up to 0.0050, whereas the proposed method reduced it to less than 0.0037. In addition, the suggested procedure reduced the deviations of the unbalanced mass phase estimation by 35.2% as compared to the results found by the conventional method. Consequently, through the validation test, the suggested method was found to have the largest vibration decrease of any method considered in the study. [ABSTRACT FROM AUTHOR]

Summary: Estimating modal parameters requires significant user interaction, especially when parametric system identification methods are used and the physical modes are selected in the stabilization diagram. In this paper, a fast density peaks clustering algorithm combined with the covariance‐driven stochastic subspace identification method is used to automatically identify modal parameters. Before the automatic identification process, the spurious modes from the stochastic subspace identification method were eliminated by a two‐stage method, including using the soft and hard verification criteria to remove spurious modes in the first stage and the removal of spurious modes based on the stability of physical modes in the second stage; thus, a better stabilization diagram was obtained for the subsequent automatic identification. Furthermore, fast density peaks clustering algorithm was applied to select the appropriate structure modes from the stabilization diagram. In the entire identification process, no user participation was required. The proposed method was demonstrated on a 4‐degree of freedom (DOF) numerical model and a benchmark frame structure, and the results indicated that the modal parameters can be identified accurately even with the noise effects using the default user‐defined parameters. This method showed higher efficiency and universality than the existing methods. Finally, the applicability and robustness of the proposed method in automated operational mode tracking were verified on a real cable‐stayed bridge. [ABSTRACT FROM AUTHOR]

Summary: The identification of traffic loads, including the axle weight (AW) and the gross vehicle weight (GVW) of vehicles, plays an important role in bridge design refinement, safety evaluation, and maintenance strategies. Bridge weigh in motion (BWIM) is a promising technique to weigh vehicles passing through bridges. Though the state‐of‐the‐art BWIM can accurately identify the GVW, unacceptable weighing errors are reported when identifying the AW of vehicles, particularly for those with closely spaced axles. To address the axle weighing problem, this paper aims to improve the performance of BWIM in weighing individual axle loads apart from the gross vehicle weight. The work first theoretically analyzes the possible sources of errors of existing BWIM algorithms, which are observational errors residing in the BWIM equation, no constraint imposed on individual axle loads, and ill‐conditioned nature. Accordingly, three measures are taken to establish a novel robust BWIM algorithm, which is based on regularized total least squares, as well as imposing constraints on the relationship between axles. To validate the proposed algorithm, a series of weighing experiments are carried out on a high‐fidelity vehicle‐bridge scale model. The corresponding results indicate that the proposed BWIM algorithm significantly outperforms other existing BWIM algorithms in terms of the accuracy and robustness of identifying individual axle weight, while retaining satisfactory identification of the gross vehicle weight as well. [ABSTRACT FROM AUTHOR]

Summary: In this paper, different variants of Kalman filter based algorithms extended Kalman filter (EKF), two‐stage EKF, and unscented Kalman filter (UKF) are used to estimate and compare the performance of these algorithms in identifying the state and parameters of nonlinear hysteretic model, simulating unbonded fibre reinforced elastomeric isolator (U‐FREI). A two‐story masonry building supported on U‐FREI is utilized for the present study. The model is excited on a shake table using recorded excitations and measured responses are considered for the identification study. Since the hysteretic behavior of U‐FREI is different from that of conventional steel reinforced elastomeric isolator, U‐FREI is modeled using extended Bouc–Wen model. The considered model comprises of mainly six parameters and the identified parameters as obtained from various considered algorithms as well for different input excitations are observed to be different. In view of this, percentage error index (PEI) has been introduced to compare the performance of these algorithms. PEI is evaluated using the parameter weights obtained from parameter sensitivity analysis in the form of spider diagram. The state and parameters of the U‐FREI are also identified using noise corrupted responses from a numerically simulated model of the same considered two‐story masonry building. The responses are contaminated with varying level of artificially added Gaussian white noise. It is observed that the performances of these algorithms are quite stable and are unaffected due to artificially added noise. Further, studies have also been carried out for proper selection of measurement noise covariance and a significant influence on the performance of these algorithms are observed. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a novel distance‐based slicing (DBS) algorithm is proposed for the visualization of inclusion defects within a thick solid structure. The algorithm is based on the propagation distance traveled by the ultrasonic wave signals and was integrated with the generation laser scanning ultrasonic propagation imaging (UPI) system in which the fixed sensor was positioned to detect the induced through‐the‐thickness ultrasonic signals. The advantages provided by the UPI system allow for an in‐situ non‐destructive evaluation for real world applications. An investigation of an isotropic solid specimen of large thickness with multiple internal inclusion defects of different shapes and depth location was performed using the proposed algorithm and system. The results in the form of ultrasonic propagation freeze frames show that the algorithm is much more effective in detecting each individual defect and is particularly useful for a thick‐walled specimen. Additionally, a relationship was determined between the visualized result of each defect and the source position during data acquisition which enabled us to estimate the depth location of the defect within the specimen. [ABSTRACT FROM AUTHOR]

Summary: Rapid health assessment of essential buildings such as hospitals, fire stations, and large residential complexes is crucial after damaging earthquakes. The use of advanced technologies such as wireless sensors, learning algorithms, and signal processing methods became more attractive in such fast applications due to their higher reliabilities and efficiencies compared to the conventional visual inspection methods. This paper presents a robust post‐earthquake damage detection framework for predicting the extent and location of damage occurrence in the braced‐frame structures after an earthquake. To do so, features derived from acceleration response of the structure were used along with a classification learner to determine the health condition of the structure. Decision tree classifiers are used for the purpose of damage classification where the Bayesian optimization algorithm is implemented to optimize the architecture of the mentioned classifier. A one‐story chevron steel‐braced frame, a three‐story X‐braced steel‐frame, and a five‐story three‐dimensional building are considered to validate the proposed method. The total number of 3774 and 1887 nonlinear response history analyses were respectively performed for 2D and 3D numerical models under scaled SAC motions, using the OpenSees simulation platform. Furthermore, in order to simulate the field condition, a maximum level of 10% white Gaussian noise is added to the output signals. Results obtained from the three case studies show that the proposed framework is robust and reliable in predicting the extent of damage level in the braced‐frame structures in a short time after an earthquake. [ABSTRACT FROM AUTHOR]

Image-based bridge displacement measurement still suffers from certain limitations in outdoor implementation. Each of these limitations was addressed in this study. (1) The laser spot is difficult to identify visually during the object distance (OD: mm) measurement using a laser rangefinder, which makes the scale factor (SF: mm/pixel) calibration tricky. To overcome this issue, a stereovision-based full-field OD measurement method using only one camera was suggested. (2) Sunlight reflected by the water surface during the measurement causes light spot interference on the captured images, which is not conducive to target tracking. A network for light spot removal based on a generative adversarial network (GAN) is designed. To obtain a better image restoration effect, the edge prior was novelly designed as the input of a shadow mask-based semantic-aware network (S2Net). (3) A coarse-to-fine matching strategy combined with image sparse representation (SR) was developed to balance the subpixel location precision and efficiency. The effectiveness of the above innovations was verified through algorithm evaluation. Finally, the integrated method was applied to the vibration response monitoring of a concrete bridge impacted by the traffic load. The image-based measurement results show good agreement with those of the long-gauge fiber Bragg grating sensors and lower noise than that of the method before improvement. [ABSTRACT FROM AUTHOR]

Summary: The tracking performance of an actuation transfer system in a real‐time hybrid simulation (RTHS) frequently faces accuracy and robustness challenges under constraints and complicated environments with uncertainties. This study proposes a novel control approach based on the deep deterministic policy gradient algorithm in reinforcement learning (RL) combined with feedforward (FF) compensation, which emphasizes the implementation of shaking table control and substructure RTHS. The proposed method first describes the control plant within the RL environment. Then, the agent is trained offline to develop optimized control policies for interaction with the environment. A series of validation tests were conducted to assess the performance of the proposed method, starting with the dynamic testing of underwater shaking table control and then a virtual RTHS benchmark problem. For complex systems, such as controlling the underwater shaking table, the proposed algorithm, FF, and adaptive time series (ATS) compensation methods are compared under various water depths and motions. The results show better performance and wider broadband frequency applicability under different shaking table dynamic‐coupling effects. Next, a controller based on the proposed method was designed by extending the virtual RTHS via the configuration of the control plant and substructure division, as provided in the RTHS benchmark problem. The proposed RL controller also improved the tracking accuracy and robustness of conventional FF compensators against unmodeled dynamics and perturbation uncertainties. This controller can be extended to further advanced control strategies as a component of model‐based control methods. [ABSTRACT FROM AUTHOR]

Summary: In this study, the Q‐learning algorithm is integrated into the binary water strider algorithm to adaptively control the search operators and repair strategies. The proposed algorithm (QWSA) is applied to optimal sensor placement in structures with numerous candidate locations. To handle the constraint related to the number of sensors, an efficient strategy based on the triaxial driving‐point residue (DPR3) coefficient is developed, in addition to the random solution repair strategy. The reinforcement learning algorithm with softmax policy determines which strategy should be adopted for the generated solutions. Since the utilized sensors are triaxial, the tridimensional modal assurance criterion (TMAC) is applied as the objective function. To verify the effectiveness of the proposed method, two large‐scale dome‐shaped trusses are investigated, and the results are compared with the basic water strider algorithm and several other well‐known and modern metaheuristics in the literature. The results demonstrate that placements obtained by QWSA have in average lower than half of the cost value obtained by compared methods. Additionally, average DPR3 (DPR3¯) of the QWSA is higher than the rest of the algorithms. The Wilcoxon test indicates that, at a 95% confidence level, the results of QWSA are superior to all algorithms. Additionally, the proposed method outperforms them in terms of convergence speed. [ABSTRACT FROM AUTHOR]