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The traditional multiscale model static updating method for long-span bridges requires load tests to obtain the correspondence between load and response, which leads to prolonged traffic interruption, with poor timeliness and low efficiency. Therefore, an efficient multiscale model dynamic calibrating framework based on stochastic vehicle-induced responses is proposed in this paper. The multiscale model is calibrated by monitoring data, and the dynamic calibrating efficiency is improved through the substructure–refined model combination modeling. First, the relationship between the structure and its corresponding response statistical characteristics is derived under stochastic traffic loads, and a statistical-based calibrating objective function of the multiscale model is established. Second, the framework for efficient multiscale model dynamic calibrating based on long-term monitoring data is presented, including efficient multiscale model establishment and dynamic calibrating based on stochastic vehicle-induced responses. Finally, the effectiveness of the proposed method is verified by its application to a long-span steel box girder suspension bridge. Comparison with the traditional load test method demonstrates that the proposed method effectively achieves multiscale model dynamic calibrating based on monitoring data during bridge operation, improving calibrating efficiency while ensuring multiscale modeling accuracy. [ABSTRACT FROM AUTHOR]

The identification of damage to concrete bridge surfaces is of great significance to maintaining the durability and reliability of bridges. However, it is difficult to identify small areas of damage, especially in high-resolution images. To conduct damage identification research in a targeted manner, this paper proposes a method of multidamage identification in high-resolution images based on You Only Look Once version 5 (YOLOv5). In this paper, a data set labeled with four types of damage (crack, spallation, hole, and rebar) is used for training, validation, and testing. To ensure that the network adequately learns the damage features, the high-resolution images are cropped into subimages via the autoadaptive window cropping method (AWCM) proposed in this paper. The cropping method can crop images according to the label information and protect the damage features from destruction during the cropping process. To avoid overfitting, it is necessary to balance the volume of different types of damage. After balancing, the count for each category is as follows: crack (4,980), spallation (5,225), hole (5,211) and rebar (5,020). The balanced data set can be used to train the deep learning network and construct the multidamage identification model. After identification, the subimages, and the prediction boxes of damages in them are restored to their original high-resolution images. The results show that the mean average precision (mAP) of all classes is 94.2%, and the values for cracks, spallation, holes, and rebars are 86.9%, 98.1%, 92.3%, and 99.4%, respectively, which indicates that the proposed method outperforms the other three methods (inputting original images directly, the sliding window cropping method, and the random centroid cropping method). [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for the detection and assessment of internal corrosion in concrete bridge decks. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper analyzes some common abnormal data from actual bridges GPR data and proposes corresponding automatic algorithms for anomaly correction to enhance assessment accuracy. The automatic algorithm focuses on two main aspects: correcting anomalies in direct coupling wave amplitudes based on data statistics and mitigating the impact of abnormal data due to incorrectly picked rebar on depth correction using density clustering. The specific process of the automatic method can be divided into four steps. First, automatic rebar picking is performed based on the preprocessed GPR data. Next, data statistics analysis is implemented on the extracted rebar data to identify and correct abnormal amplitude data. Then, the true rebar data are identified for depth correction based on density clustering. Finally, the bridge deck corrosion map is generated based on the corrected rebar reflection amplitudes and rebar positions. The feasibility of this method was verified through a case study with GPR data from two in-service bridges. The results show that this method can effectively and automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by the proposed method is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. Practical Applications: Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for concrete bridge deck corrosion detection and assessment. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper proposes a set of automatic data processing procedures for anomaly correction to improve the corrosion assessment accuracy. The feasibility of the proposed algorithms was validated through a case study with GPR data from two in-service bridges. The results show that these algorithms can effectively automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by these algorithms is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. [ABSTRACT FROM AUTHOR]

Summary: Modal identification method based on blind source separation (BSS) technique has gained extensive attentions for civil structures. Developing the complex modes estimation method is important in practical applications because the assumption of proportional damping is not always satisfied. Sparse component analysis (SCA) performs well in underdetermined BSS problems. However, SCA is confined to the situation of proportional damping. In this study, a generalized SCA method is proposed to extend the original SCA method to both real and complex modes identification. First, the general formulation of complex modes is extended by the analytic form to eliminate the complex conjugate part in the BSS model. A new single‐source‐point detection method that is available to handle real and complex modes is proposed. Local outlier factor method is adopted to remove the outliers in single source points. Subsequently, complex‐valued modal matrix is calculated by the clustering technique. Then, modal responses are recovered using the complex version of smoothed zero norm method, where modal frequencies and damping ratios can be extracted. Finally, the effectiveness of the proposed method is demonstrated for identification of real and complex modes, close modes, and underdetermined problem. The application to a benchmark structure demonstrates the effectiveness for practical applications. [ABSTRACT FROM AUTHOR]

The structural temperature gradient (STG) is one of the most key factors causing cracking and even damage to bridge structures. However, its real effects on bridge structures are often over- or underestimated in practice. For most operating bridges, the structural health monitoring systems have just been put into use recently, and the monitoring structural temperature data are limited, which always leads to unreasonable STG representative value for a long return period based on such short-term structural temperature data. To solve the problems, this article proposes an STG determination method based on the long-term historical meteorological parameters at bridge sites. First, the main meteorological parameters affecting the STG were determined by correlation analysis. Second, considering the different influence mechanisms of various meteorological conditions on STG, a training sample set construction method is proposed by clustering the meteorological parameters and STG monitoring data. Based on such training data, a correlation model between STG and meteorological parameters can be established to extend the STG dataset based on the historical meteorological data. Finally, the peak over threshold method is applied to analyze the obtained extended STG data and to estimate its representative value. The proposed method was verified through a long-span cable-stayed bridge. The results show that the monitoring dataset of the STG can be effectively extended through the established correlation model. Compared with the short-term monitoring data, more reasonable representative values of the STG can be obtained through the extended dataset of monitoring STG. [ABSTRACT FROM AUTHOR]

Sliding bearings are a key component of bridges, and their normal performance is an important prerequisite to ensure traffic safety. To solve the problem wherein a single type of sensor has difficulty in effectively predicting the wear life of sliding bearings, this paper proposes a method to predict the sliding bearing wear life using the multitype monitoring data of the bridge. This method uses the vertical acceleration data of the girder with high sampling frequency to calculate the bearing cumulative dynamic displacement under vehicle load and then processes the data collected by the longitudinal displacement gauge with low sampling frequency to extract the bearing cumulative static displacement under the effect of temperature. Next, the daily bearing cumulative displacement calculated by adding them is taken as the evaluation index, and the sliding bearing wear life is predicted based on the reliability analysis. Finally, the proposed method is verified by a numerical example of vehicle–bridge interaction and real bridge monitoring data. The obtained results show that the proposed method can be used to estimate the bearing cumulative displacement with high accuracy and can effectively predict the wear life of sliding bearings. The prediction results can provide an important reference for bridge evaluation. [ABSTRACT FROM AUTHOR]

Bridge inspections performed by the response of the moving vehicle have been widely studied because of its mobility and efficiency, whereas mode shapes extracted by the moving vehicle have low order and limited accuracy in existing studies. In this study, experimental studies are conducted in which a stationary shaker is used to excite the steady-state-forced vibration of the bridge, and the bridge vibration is collected by an accelerometer installed on a moving vehicle. Accurate multiorder mode shapes are obtained by extracting the vehicle acceleration envelope. First, a theoretical derivation of the method is provided, and an extraction process for field tests is proposed. Then, a model test of a three-span continuous beam bridge and a field test of a two-span continuous girder bridge are conducted. To verify the accuracy of the mode shapes extracted by the moving vehicle, reference data are obtained by finite-element modal analysis and by using the hammer test. Through a difference analysis of accelerations between the parked vehicle and the bridge deck under the test vehicle, the ability of the designed test vehicle to collect bridge vibration in different frequency bands is evaluated. Finally, the first five mode shapes of the bridge model and the first six mode shapes of the field bridge are extracted by a moving test vehicle, and the effects of vehicle speed and excitation amplitude on mode shape extraction are analyzed. The results show that multiorder mode shapes can be accurately extracted by a moving test vehicle. [ABSTRACT FROM AUTHOR]