Due to the harsh working conditions, the durability of the seaport wharf structure is poor compared with similar hydraulic structures. According to the structural characteristics of coastal port wharf and the particularity of a high salt and high humidity environment, the stress features of the coastal wharf structure are analyzed, and the health inspection indicators of the wharf structure are proposed. A fiber grating sensor-based structural health monitoring system for coastal high-pile piers is established. A corresponding system for detecting structural health is designed according to the standard structural section of the wharf’s front platform. The corresponding monitoring implementation scheme, sensor selection, and performance parameters are proposed. Finally, the realization technology and related indicators of data acquisition and transmission subsystem are given. The experimental results indicate that the waveform of the structural response to the wave load has a consistent sine wave pattern with the actual wave load. The maximum strain of the berthing pier appears at 4.35 m and 6.14 m, and the elevations reach $4.66 \mathsf{\mu }\mathsf{\epsilon }$ and $5.31 \mathsf{\mu }\mathsf{\epsilon }$, respectively. The strain at other positions also has an obvious change trend. The experimental results provide some help for the research of the wharf health monitoring system.

This paper aims to use deep learning algorithms to identify and study the stability of breakwater armor blocks. It introduces a posture identification model for fender blocks using a Mask Region-based Convolutional Neural Network (R-CNN), which has been enhanced by considering factors affecting breakwater fender blocks. Furthermore, a wave prediction model for breakwaters is developed by integrating Bidirectional Encoder Representations from Transformers (BERTs) with Bidirectional Long Short-Term Memory (BiLSTM). The performance of these models is evaluated. The results show that the accuracy of the Mask R-CNN and its comparison algorithms initially increases and then decreases with higher Intersection Over Union (IOU) thresholds, peaking at 95.16% accuracy at an IOU threshold of 0.5. The BERT-BiLSTM wave prediction model maintains a loss value around 0.01 and an accuracy of approximately 90.00%. These results suggest that the proposed models offer more accurate stability assessments of breakwater armor blocks. By combining the random forest prediction model with BiLSTM, the wave characteristics and fender posture can be predicted better, offering reliable decision support for breakwater engineering.

The erosion and sedimentation of bank slopes are important factors affecting the safety of wharf operations. The essence of bank slope monitoring is to identify the water–soil interface. This paper proposes a model for soil-and-water interface identification and monitoring equipment buried on the bank slope of the wharf, based on the difference in thermodynamic heat transfer between water and soil media, and presents the results of multi-condition numerical simulation. The comparison between numerical simulation results and indoor experimental results shows that the overall patterns are consistent, with an error of less than 11.4%, which is lower than the deviation between theoretical calculation results and indoor experiments. Based on the accuracy of the numerical calculation results, the temperature rise and propagation characteristics of linear heat sources made of iron and PVC in eight types of cohesive soils and six types of non-cohesive soils were studied. The results indicate that there are significant differences in the temperature distribution of linear heat sources made of iron and PVC in both water and soil media. The monitoring equipment model based on the difference in heat transfer between water and soil can be applied in practical engineering. This provides a foundation for the design and application of subsequent monitoring equipment.