# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 1 Introduction  
Offshore structures arrange immense potentials for the progress and utilization of oceanic energy and resources. Offshore industrializations are moving rapidly towards deep seawaters for oil and gas exploration in oceanic environments to meet global energy demands. Typically, these constructed structures are subjected to more severe load conditions than those seen on land. The main source of structural load pressure for soil foundations is the ocean waves affecting the platform. In addition to the conditions and situations that often occur in inland structures, offshore structures have additional complications due to their location in the marine environment, where hydrodynamic interactions and dynamic reactions are essential considerations in their design. An offshore structure based on mobility can be of two types: offshore fixed structures and offshore floating structures. In shallow waters, both fixed types of offshore-based structures (gravity and jacketed platforms) are cost-effective and practical. Using fixed platforms, mainly in deep water (nearly 300 m deep), is vulnerable to technical complications linked to economic issues that increase rapidly as the water depth increases. Contrarily, compatible floating structures such as tension leg platforms and spar platforms provide an economical solution to exploration and production facilities in profound and ultra-deep water [1]. Most floating-on-water-based offshore structures are largely ostentations by certain uncertainties primarily related to steep and nonlinear waves, wind, and currents, which cause the ringing and springing of tension leg platforms (TLPs), slow the drift-yaw motion of floating production, storage, and offloading (FPSO), and significant oscillations in spar platforms due to vortex shedding [2]. Due to non-linearities such as geometry, changeable pretension, and varying immersion, investigating floating offshore constructions such as spar platforms and evaluating their environmental impact is difficult. Spar platforms are the most suitable and economical offshore floating structures suitable for deep-sea waters. In the exploration phase of oil and gas drilling operations, several environmental and associated forces often affect offshore floating structures. An early decision should be made to either delay or stop production based on the prediction of the spar platform's response to several anticipated environmental situations. The finite element method (FEM) can be considered a key practice for dealing with such analysis. Several recent studies have been conducted to forecast and predict the responses of spar types of platforms using several conventional methods [3; 4; 5; 6]. However, utilizing such methodologies to analyze the reaction of spar platforms is time-consuming and difficult. Artificial neural networks (ANNs) are cutting-edge algorithms that can deliver very quick and efficient results in instances where a traditional analysis would be quite difficult. Hence, [PERSON] et al. [7] developed an ANN model to predict the mooring forces and analyze the dynamic behavior of guard tent and FPSO in a tandem configuration. It is of great importance to monitor the integrity of the mooring lines [8] on floating offshore platforms in order to detect any damage or failures. To address the problem, [PERSON] et al. [9] exploited an ANN model for mooring line damage detection based on a proprietary methodology that only uses GPS tracking to detect tiny fluctuations in the long drift period of a moored floating vessel as a signal of mooring line failure. ANN was applied by [PERSON] et al. [10] for the purpose of monitoring the health of a floating offshore structure's mooring line. Two machine learning models, a gradient-boosted regression tree (GBRT) and an artificial neural network (ANN), were used to forecast offshore platform risk assessments [11]. In other work, [PERSON] et al. [12] predicted the top tension response of the spar mooring lines using an ANN for a long-time history.