Talha Sino
The assessment of viscoelastic properties in polymer pipes is crucial for ensuring their reliability and performance in various applications, including water distribution, gas transportation, and industrial fluid handling. Polymer pipes exhibit time-dependent mechanical behavior due to their viscoelastic nature, meaning that they experience both elastic and viscous responses when subjected to stress. Understanding these properties is essential for predicting long-term performance, structural integrity, and potential failure mechanisms. Traditional methods for evaluating viscoelastic parameters often involve mechanical testing, which can be time-consuming and require specialized equipment. However, recent advancements in signal processing and artificial intelligence have paved the way for more efficient and accurate assessment techniques. One such approach involves the use of transient signals and artificial neural networks to extract viscoelastic parameters from polymer pipes in a non-destructive manner. Transient signals, typically generated by pressure waves or mechanical excitations, provide valuable insights into the material properties of polymer pipes. When a transient event occurs, such as a sudden change in pressure or mechanical impact, the resulting wave propagates through the pipe system, interacting with the material’s inherent characteristics
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