# Research on Predicting the Mechanical Characteristics of Deep-Sea Mining Transportation Pipelines
## 1 Introduction
[PERSON] employed the stochastic phase spectrum method to generate random wave forces and vortex-induced excitations acting onisers in the time domain. He proposed a parametrically excited top-tensioned riser model that was simultaneously subjected to random wave and vortex-induced excitations [26]. In the study of flexible hose mechanics, [PERSON] [27] proposed a method for calculating the spatial shape and stress state of flexible hoses. This mathematical model principle involves dividing the hose into several mass-spring segments to analyze the force and motion relationship of the segments. [PERSON] and [PERSON] [28] conducted a finite difference analysis on submarine cables, combined with the system dynamics characteristics to establish and solve the differential equations of the flexible cable. [PERSON] et al. derived the mathematical three-dimensional equilibrium equations for flexible hoses and utilized the U.L. method to solve the equilibrium equations through the three-dimensional large displacement finite element incremental method [29]. [PERSON] utilized the finite time integration method to analyze the situation where one end of the flexible hose was fixed and the other end was freely falling, under the assumption that the axial and shear effects of the hose were neglected [30]. Based on the static characteristics of the hydraulic conveying pipeline, [PERSON] obtained a three-dimensional spatial model of the hose and conducted numerical simulations on the static displacement of the hydraulic lifting pipeline under steady-state conditions [31]. [PERSON] utilized the OrcaFlex software to simulate the laying of a flexible pipeline with an outer diameter of 352.42 mm on the seabed at a depth of 3000 m. The study investigated the axial tension, bending moment, and stress-strain dynamics of the pipeline during the laying process and analyzed factors influencing the dynamic behavior of the pipeline [32]. Based on fundamental mechanics research, significant progress and breakthroughs have been achieved in recent years in the prediction and estimation of mechanical properties. [PERSON], [PERSON], and others used inverse analysis to diagnose the mechanical properties and structures of materials, which aids in assessing potentially deteriorated material performance and mechanical damage within structures [33]. The combination of \"drilling\" methods with indentation tests and inverse analysis has been utilized to evaluate residual stress and mechanical properties, effectively reducing the additional damage and costs typically associated with conventional diagnostic procedures for structural components [34]. [PERSON] developed a stress prediction model based on synthesized nonlinear coefficients, which can effectively assess the stress state of specimens [35]. [PERSON] proposed a multimodal fusion prediction model that integrates material microstructure information and compositional data, providing a promising solution for the measurement and prediction of material properties [36]. Despite the rich force analysis provided by past research, there remains a significant challenge in force prediction, especially in quickly predicting under conditions of insufficient monitoring data. This paper aims to address this issue by establishing a simulation model of the deep-sea mining system and conducting static and dynamic simulation analyses on lifting hard pipes and hoses, further building a predictive model of pipeline mechanical characteristics based on the BP neural network, thereby providing guidance for the safety and reliability of the deep-sea mining transportation system.