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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.

In this paper, the mechanical characteristics of stabilizing piles embedded in layered bedrocks are studied both experimentally and numerically. The influence of soft and hard interbedded layers in the structure of the bedrock on the mechanical characteristics of stabilizing piles is particularly investigated. The discrete element method is used to numerically investigate the response of the stabilizing piles embedded in composite and inclined bedrocks. The simulation results and comparison with experimental data are presented to demonstrate the effectiveness and accuracy of the discrete element model. As the dip angle of the soft/hard interbedded bedrock layers increases from 0° to 45°, it is observed that the displacement of the embedded section of the stabilizing pile increases and reaches the maximum displacement at 45°. In the range of 45° to 75°, the influence of the dip angle of the layered bedrock on the displacement of the embedded section of the pile is gradually reduced.

In this study, a nonlinear prediction model of antislide pile top displacement is proposed. Based on the quantitative analysis of the rock mass structure characteristics of the soft and hard interbedded sliding bed in the Jurassic strata, the post-thrust force and geometric characteristics of the top of antislide pile displacement, and bending moment, the main controlling factors affecting the displacement of the top of antislide pile were determined by maximal information coefficient (MIC). Through orthogonal experiment design and 3DEC numerical experiment, a database of main controlling factors (sliding bedrock inclination, thrust size, embedded depth, and pile section size) of pile top displacement was established and a nonlinear prediction model of the displacement of the top of antislide pile based on the main controlling factors was proposed. Finally, two engineering examples were used to validate the performance of this model, with the comparisons of four prediction methods (SVR, MIC-SVR, LSTM, and ELMAN). The results show that the MIC-SVR model has a practical reference value for the prediction of the displacement of the top of an antislide pile in the Jurassic landslide in the Three Gorges Reservoir Area.