This paper presents an idea of using carbonized electrospun Polyacrylonitrile (PAN) fibers as a sensor material in a structural health monitoring (SHM) system. The electrospun PAN fibers are lightweight, less costly and do not interfere with the functioning of infrastructure. This study deals with the fabrication of PAN-based nanofibers via electrospinning followed by stabilization and carbonization in order to remove all non-carbonaceous material and ensure pure carbon fibers as the resulting material. Electrochemical impedance spectroscopy was used to determine the ionic conductivity of PAN fibers. The X-ray diffraction study showed that the repeated peaks near 42° on the activated nanofiber film were α and β phases, respectively, with crystalline forms. Contact angle, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) were also employed to examine the surface, thermal and chemical properties of the carbonized electrospun PAN fibers. The test results indicated that the carbonized PAN nanofibers have superior physical properties, which may be useful for structural health monitoring (SHM) applications in different industries.

The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al₂O₃-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.