Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.

With the development of the Chinese transportation industry, the number of bridges has increased significantly, but this results in high pressure of structural maintenance and management. Bridge management system (BMS) is critical for efficient maintenance and ensured safety of bridge structures during long-term operation. Building information modeling (BIM) is an emerging technology with powerful visualization and informatization capability, making it an ideal tool for developing modern management systems. This paper introduces the development of a bridge management system based on the BIM technology. Industry Foundation Classes (IFC) and International Framework for Dictionaries (IFD) standards are studied and extended, and coding rules are proposed for the Chinese bridge industry. Also, a standard structural modeling method is proposed to fast build the bridge BIM model. Web-BIM oriented bridge management is proposed, and portable devices are introduced into the system. Collaborative management is realized for different users. The BIM-based maintenance management system is designed. Finally, a practical BIM-based BMS is established for a long-span cable-stayed bridge in China. This system integrates the BIM with the geographic information system (GIS) and contains information management, inspection management, technical condition evaluation, and enables users to cooperate with each other. Such a BMS could help to improve the management efficiency and ensure its normal operation, providing a useful platform for the maintenance of massive bridges in China.

Complicated temperature fields and crossing high-speed trains offer new challenges for monitoring girder deflections in operating railway bridges. Behavior analyses and early warning of girder deflections are necessary for the diurnal operation of bridge structures. Regarding the Nanjing Dashengguan Yangtze River Bridge, the present paper identifies the temperature-induced/train-induced deflections of the bridge girder from deflection data obtained from the wavelet transform. The principal components of the bridge temperature field are obtained to verify the temperature sensitivity of the girder deflection. The effect of the temperature-induced girder deformation on train-induced girder deflections is analyzed using the train-bridge dynamic model. The statistical laws of the downward and upward of train-induced girder deflections are described using the t location-scale distribution. Based on the mutual updating of monitoring data and numerical model, the behaviors of the temperature-induced/train-induced deflections of the bridge girder can be captured accurately, and the warning threshold of girder deflections under the coupling effects of temperature and trains is determined rapidly. The results demonstrated that the influence of the temperature-induced girder deformation on the train-induced girder deflection is relatively small when compared with the train-induced girder deflection itself, with the increasing amplitude being no more than 3%. A real-time early warning of girder deflections for the long-span railway bridge can be conducted to indicate the abnormal changes in girder deflection less than 10 s, which is likely due to the deterioration of track irregularity on the bridge or damaged bridge components.

The evolution rule of temperature-induced deflection in main girders is an important index to evaluate the service performance of long-span cable-stayed bridges, which directly reflects the coupling effect between the vertical stiffness of the main girder and the tension of multiple cables. However, temperature-induced deflection is caused by the complex temperature field of the main girder, cable tower and cable, while monitoring data have documented a time-lag effect between the temperature and temperature-induced deflection. Hence, it is difficult to accurately describe and model the behavior of the temperature-induced deflection in a long-span cable-stayed bridge in service. To this end, by utilizing the advantage of long short-term memory (LSTM) network for time series prediction, a digital model in minute scale based on monitoring data and deep learning can be developed to predict temperature-induced deflection, and resolve the low precision caused by the single-point input and time-lag effect. Compared with traditional machine learning algorithm and linear regression, a deep learning LSTM network has the best performance. For the cable-stayed bridge in this paper, the mean absolute error of the LSTM model was even less than 0.5 mm, and with the combined hypothesis test, the early warning accuracy for the abnormal change of temperature-induced deflection could achieve a minimum of 0.3%.

The monitoring data makes it feasible to quickly evaluate the cracking of the prestressed concrete box-girder bridge. The live-load strain can accurately quantify the load effect and cracking of bridges due to its explicit datum point of signal. Based on the live-load strain data from bridge monitoring system, this study develops a comprehensive data-driven method of state evaluation and cracking early warning for the prestressed concrete box-girder bridge. The feature of vehicle-induced strain is extracted using the deep learning and classification of long short-term memory network. The vehicle-induced strain features are clustered via Gaussian mixture model, and the cracking early warning of the bridge is conducted according to the reliability of heavy vehicle clustering data. This method can be used as an indicator for the bridge inspection, truck-weight-limit and reinforcement work. The results demonstrate that (1) using the long short-term memory network, a deep learning model can be trained to intelligently classify the non-stationary and stationary sections of vehicle-induced strains, of which the test accuracy of classification surpasses 99%, and (2) according to the Gaussian mixture model probability distribution of data, the vehicle-induced strain features can be clustered by the corresponding Gaussian mixture model crest, which is the premise for reflecting relational mapping between vehicle loading and strain response.