A comprehensive study of a long-term creep thermo-mechanical fatigue behavior monitoring of BFRP composite pipeline using electrical capacitance sensors and deep learning algorithm

•A multi-physics 3D FEM is established for exciting electrodes of ECS installed around pipeline subjected to LTCTMF.•The LTCTMF behavior is studied at different levels of LTCTMF.•A theoretical model results of LTCTMF compliance over creep time are analyzed using the Modulus degradation Approach.•A deep learning model is designed to predict compliance for various LTCTMF levels not included in previous FEM evaluation.•The proposed method performance is estimated by computing the accuracy rate, regression rate, and F-score indexes. 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 (Sf(t)) 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 Sf(t) over the time of creep for some levels of long-term creep thermo-mechanical fatigue (Rf%) 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 Sf(t) in pipeline for various Rf% 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 Rf%=25%,50%, and 75% from a finite element model and modulus degradation approach, and then is predicted the long-term creep thermo-mechanical fatigue behavior for Rf%=1