The composite pipeline is a relatively new and viable alternative pipeline to the more commonly used traditional one due to its good mechanical and fatigue properties and lower production cost. For this purpose, it is critical to assess the mechanical and fatigue performance of composite pipeline material under various working conditions, particularly for monitoring long-term creep thermo-mechanical fatigue behavior. In this paper, a long-term creep thermo-mechanical fatigue behavior in a basalt fiber reinforced polymer laminated composite pipeline is detected through an integrated expert system consisting of the electrical capacitance sensors and a deep learning algorithm. First, a multi-physics finite element model is established for the simulation of a long-term creep thermo-mechanical fatigue behavior in basalt fiber reinforced polymer composite pipelines subjected to long-term fatigue loading of internal pressure and thermal effect. Second, theoretical model results of long-term creep thermo-mechanical fatigue compliance () over the time of creep are analyzed in pipeline material using the modulus degradation approach. Finally, an electrical potential change between electrical capacitance sensors electrodes corresponding to over the time of creep for some levels of long-term creep thermo-mechanical fatigue ( is recorded and then used in these datasets for training of the novel deep neural network based on one of the most widely used of the deep neural network families is the convolutional neural network, to predict in pipeline for various not included in the previous finite element model evaluation (i.e. electrical capacitance sensors technique). In this paper first is detected the long-term creep thermo-mechanical fatigue behavior for , and from a finite element model and modulus degradation approach, and then is predicted the long-term creep thermo-mechanical fatigue behavior for and respectively via deep neural network. The proposed method results are in good agreement with the experimental results available in the literature, thus verifying the accuracy and reliability of the proposed technique and its applicability to other different composite structures.

中文翻译：

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使用电容传感器和深度学习算法监测 BFRP 复合管道长期蠕变热机械疲劳行为的综合研究

复合管道由于其良好的机械性能和疲劳性能以及较低的生产成本，是一种相对较常用的传统管道相对较新且可行的替代管道。为此，评估复合管道材料在各种工作条件下的机械和疲劳性能至关重要，特别是监测长期蠕变热机械疲劳行为。本文通过由电容传感器和深度学习算法组成的集成专家系统来检测玄武岩纤维增强聚合物层压复合材料管道的长期蠕变热机械疲劳行为。首先，建立了多物理场有限元模型，模拟玄武岩纤维增强聚合物复合材料管道在内压和热效应的长期疲劳载荷作用下的长期蠕变热机械疲劳行为。其次，使用模量退化方法分析了管道材料随蠕变时间的长期蠕变热机械疲劳柔量 () 的理论模型结果。最后，记录电容传感器电极之间的电势变化，该变化对应于某些级别的长期蠕变热机械疲劳的蠕变时间，然后在这些数据集中用于训练基于深度神经网络家族中应用最广泛的是卷积神经网络，它可以对之前的有限元模型评估中未包含的各种情况（即电容传感器技术）进行管道预测。本文首先检测长期通过有限元模型和模量退化方法，计算了 、 和 的蠕变热机械疲劳行为，然后通过深度神经网络分别预测 和 的长期蠕变热机械疲劳行为。所提出的方法结果与文献中提供的实验结果，从而验证了所提出技术的准确性和可靠性及其对其他不同复合材料结构的适用性。