Bridges are critical components of transportation infrastructure. To ensure the long-term performance of bridges and the safety of the public, regular inspections are required during their service. Structural performance assessments are subject to various conditions. Structural deterioration is caused by complex environmental and operational conditions (EOCs) including temperature changes, truckloads, chemical corrosion, etc. In this study, an in-site structural health monitoring (SHM) system is designed and deployed on the Caohekou Bridge in China with a series of sensors installed, providing continuous real-time data for 4 years. Crack width, vertical deformation, concrete strains, temperature, longitudinal displacement and acceleration are monitored and assessed. Time-history monitoring data are comprehensively analysed to advance our understanding of structural deterioration caused by time and temperature. It is very common to lose data during the monitoring process, especially in the long-term run. To overcome the challenge of missing data package and to realize early warning, methods including an SHM systems face, a back propagation neural network and a long short-term memory are proposed to predict the bridge responses under the change of EOCs. It has been proved that the performance of predicted crack widths is close to that of the measured value, and the trend of change is consistent. Such results indicate that approaches proposed that quantitatively assess in-service structure are promising. Therefore, effective and efficient maintenance decisions can be made to ensure an immediate response, long-term safety and serviceability of bridge structures.

Rocks are natural materials with a heterogeneous microstructure, and the heterogeneity of the microstructure plays a crucial role in the evolution of microcracks during the compression process. A numerical model of a rock with a heterogeneous structure under compression is developed by digital image processing techniques and the discrete element method. On the grain scale, the damage mechanism and microcrack characteristics of a heterogeneous Biotite granite under compression fracture are investigated. First, the process of constructing a digital image-based heterogeneous grain model is described. The microscopic characteristics of geometric heterogeneity, elastic heterogeneity, and contact heterogeneity are all considered in the numerical model. Then, the model is calibrated according to the macroscopic properties of biotite granite obtained in the laboratory, and the numerically simulated microcrack cracking processes and damage modes are obtained with a high degree of agreement compared to the experiments. Numerical simulations have shown the following: (1) Microcracking occurs first at the weak side of the grain boundaries, and the appearance of intragranular shear cracks indicates that the rock has reached its peak strength. (2) The stress concentration caused by the heterogeneity of the microstructure is an essential factor that causes rock cracks and induces rupture. Intragranular cracks occur successively in quartz, feldspar (plagioclase), and biotite, with far more intragranular cracks in quartz and feldspar (plagioclase) than in biotite. (3) Microcracking in quartz occurs as clusters, fork and fracture features, and in feldspar (plagioclase) it tends to cause penetration microcracking, which usually surrounds or terminates at the biotite. (4) As the confining pressure increases, the tensile break between the grains is suppressed and the number of shear cracks increases. At the macro level, the rock failure mode of the numerical model changes from split damage to shear destruction, which is consistent with the law shown in laboratory experiments.