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Related Experiment Video

Updated: Aug 23, 2025

A Testing Platform for Durability Studies of Polymers and Fiber-reinforced Polymer Composites under Concurrent Hygrothermo-mechanical Stimuli

A Testing Platform for Durability Studies of Polymers and Fiber-reinforced Polymer Composites under Concurrent Hygrothermo-mechanical Stimuli

Published on: December 11, 2014

Moisture Content Prediction in Polymer Composites Using Machine Learning Techniques.

Partha Pratim Das^1,2, Monjur Morshed Rabby^1,2, Vamsee Vadlamudi¹

¹Institute for Predictive Performance Methodologies, The University of Texas at Arlington Research Institute, Fort Worth, TX 76118, USA.

|October 27, 2022

Summary

This summary is machine-generated.

This study uses dielectric spectroscopy and machine learning to predict moisture content in FRP composites during hygrothermal aging. The developed models accurately estimate moisture saturation and absorption, aiding in understanding material degradation.

Keywords:

FRP composites dielectric analysis machine learning moisture absorption

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Area of Science:

Materials Science
Non-destructive Testing
Machine Learning

Background:

Fiber Reinforced Polymer (FRP) composites are susceptible to moisture absorption, affecting their structural integrity.
Hygrothermal aging, involving combined heat and moisture exposure, significantly impacts composite performance.
Accurate monitoring of moisture content is crucial for assessing the service life and reliability of FRP structures.

Purpose of the Study:

To develop non-destructive methods for estimating moisture content in FRP composites subjected to hygrothermal aging.
To apply broadband dielectric spectroscopy/impedance spectroscopy combined with machine learning for moisture prediction.
To investigate the influence of hygrothermal aging on the dielectric properties and moisture uptake of FRP composites.

Main Methods:

Utilized non-destructive broadband dielectric spectroscopy/impedance spectroscopy to capture frequency domain dielectric responses.
Developed supervised classification models (QDA, SVM, MLP classifier) to categorize composite moisture saturation states.
Developed supervised regression models (MLR, DTR, MLP regression) to quantitatively estimate relative moisture absorption.

Main Results:

Classification models accurately categorized the moisture saturation states of FRP composites.
Regression models achieved high accuracy (R² > 0.95) in estimating relative moisture absorption from dielectric data.
Analysis of model attributes provided insights into the physics of hygrothermal aging and its influence on dielectric properties.

Conclusions:

Dielectric spectroscopy coupled with machine learning offers a robust, non-destructive approach for monitoring moisture in FRP composites.
The developed models can effectively predict moisture content and saturation levels under hygrothermal aging conditions.
This methodology enhances the understanding of material degradation mechanisms and supports predictive maintenance strategies for composite structures.