Prediction of long-term physical properties of low density polyethylene (LDPE) cable insulation materials by artificial neural network modeling approach under environmental constraints

Abstract

This study quantifies long-term physical properties of low density polyethylene (LDPE) cables insulations exposed to environmental constraints such as UV radiation and temperature via both experimental measurements and mathematical modeling approach. For this purpose, tensile test and electrical breakdown test were carried out to determine elongation at break, tensile strength, and dielectric strength of unaged and aged specimens, respectively. Experimental results showed that both UV and temperature exposures affected the LDPE properties, significantly. A supervised artificial neural network (ANN) trained by the Levenb erg-Marquardt algorithm was designed for predicting the long-term characteristics of specimens and also for minimizing the experimental procedures. Modeling work showed that the proposed ANN yielded successful estimations and predictions about the service life of thermoplastic cable insulation materials for maintaining the process.