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In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique

机译:使用机器学习技术在系泊链链路中的平面内和面外弯曲的时刻和局部应力

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This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respectively. Two serial Artificial Neural Network (ANN) models based on a deep multi-layer perceptron technique were developed. The first ANN model corresponds to multi-link analyses, where the input neurons were the tension force and angle and the output neurons were the interlink angles. The second ANN model corresponds to the three-link analyses with the input neurons of the tension force, interlink angle, and the local stress positions, and the output neurons of the local stress. The predicted local stresses for the untrained cases were reliable compared to the numerical simulation results.
机译:本文提出了一种基于机器学习技术的有效方法,以预测系泊链环节的局部应力。 采用钢级R4的D107的目标链链路进行三连杆和多链节有限元分析; 分别进行24,000和8000分析。 开发了两个基于深度多层Perceptron技术的串行人工神经网络(ANN)模型。 第一ANN模型对应于多链路分析,其中输入神经元是张力和角度,并且输出神经元是互连角度。 第二个ANN模型对应于具有张力,互连角度和局部应力位置的输入神经元的三连杆分析,以及局部应力的输出神经元。 与数值模拟结果相比,未受伤病例的预测局部应力是可靠的。

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