# Research on Predicting the Mechanical Characteristics of Deep-Sea Mining Transportation Pipelines
## 5 Conclusions  
Based on the analysis of the mechanical theory of deep-sea mining transportation pipelines, this paper establishes a simulation model for deep-sea mining systems and conducts static and dynamic analyses of the deep-sea mining transportation pipelines. It innovatively combines 64 sets of working condition test schemes through orthogonal experiments and establishes, for the first time, a pipeline mechanical characteristic prediction model based on the BP neural network. The main work and conclusions of this paper are as follows:  
1. In the OrcaFlex 11.0 software, an overall interactive simulation analysis model of the deep-sea mining system is established. A static analysis of the lifting hard pipe and hose is conducted to obtain key parameters of the pipeline's mechanical characteristics. Using an orthogonal experimental design, a total of 64 sets of experimental conditions are combined to analyze the dynamic simulation of the deep-sea mining transportation pipeline system. A total of 32,064 sets of simulated sensor data are obtained for training and testing the mechanical prediction model of the pipeline. 2. A predictive model is constructed based on the BP neural network for the effective tension at the top of the lifting hard pipe, the effective tension at the position of 107.5 m along the hose, the bending radius at the position of 132.5 m along the hose, and the bending moment. The impact of ocean current speed and angle on the mechanical parameters of the hose is studied, and the mechanical parameters of each node of the hose are predicted. The trained BP neural network model has average absolute error (MAE) and root mean square error (RMSE) values close to 0, with correlation coefficients close to 1. There is a strong linear positive correlation between the predicted values and the simulated values. The trained neural network model can serve as a predictive model for the mechanical characteristics of the transportation pipeline. By inputting sensor data, it can predict the changes in the mechanical parameters of critical points in the transportation pipeline, ensuring that the tension of the lifting hard pipe, hose tension, bending radius of the hose, and bending moment remain within safe limits. In the event of safety risks, adjustments to the mining operation plan can be made promptly to avoid pipeline failure, breakage, or excessive bending deformation. Currently, in actual deep-sea mining operations, the sensor parameters related to the mechanical properties of the conveying pipelines that can be monitored include wind speed, wave period, significant wave height, current speed, current angle, and the strain of the pipeline. This paper innovatively takes the dynamic simulation results of multiple working conditions from the earlier orthogonal experiment combinations as simulation sensor data. By employing machine learning methods, a predictive model of pipeline mechanical properties is constructed for the first time, with the monitorable sensor parameters serving as input for model training. This model predicts mechanical parameters that are difficult to monitor in actual deep-sea mining operations. It analyzes the variation patterns of mechanical parameters of the conveying pipeline under different environmental loads to ensure that the pipeline operates within the mechanical parameter range designed for the pipeline. This study can ensure the safety of deep-sea mining operations by predicting the performance of pipelines in extreme marine environments, identifying and addressing potential structural weaknesses in advance, and helping to extend the service life of pipeline systems. In addition, predicting mechanical properties can promote an efficient exploitation of deep-sea resources and support the goal of sustainable development. Conceptualization, Q.H. and Y.Q. ; methodology, Y.Q. ; software, J.Z. and M.Z. ; validation, Q.H., J.H. and M.Z. ; formal analysis, Y.Q. ; investigation, Y.Q., J.Z. and J.H. ; resources, Q.H. ; data curation, Y.Q. ; writing--original draft preparation, Y.Q.