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Journal of Polymer & Composites Original Research

Optimizing Mechanical and Durability Properties of Eco-Friendly Composite Materials Using Recycled Fillers and ML Techniques

  1. Brahma. Dutt. Purohit Department of Civil Engineering, Jaypee University, Guna
  2. Nitin Kumar Samaiya Department of Civil Engineering, Jaypee University, Guna
  3. Abhishek Verma Department of Civil Engineering, Jaypee University, Guna
  4. Rupal Purhoit Department of Civil Engineering, Govt. Polytechnic, Bhind

Abstract

The increasing demand for sustainable construction materials has intensified the exploration of recycled fillers as partial or full replacements for natural aggregates in composite materials. This study investigates the mechanical and durability performance of polymer matrix composites incorporating processed recycled fillers derived from construction and demolition (C&D) waste. Three distinct processing methods were employed to prepare the recycled fillers: untreated (URF), single processed (SPRF), and double processed (DPRF), with replacement levels ranging from 0% to 100%. The composite formulations were prepared using the Standard Composite Blending Method (SCBM), and their performance was evaluated through a series of mechanical and durability tests, including compressive strength, tensile strength, curing agent absorption, diffusion coefficient, and workability assessments. The results revealed that increasing recycled filler content led to reductions in strength and workability, primarily due to increased porosity and weak interfacial bonding. However, the double processed recycled filler (DPRF) consistently outperformed the other variants across all tests, demonstrating superior matrix compatibility, reduced permeability, and enhanced mechanical integrity, even at full replacement levels. Notably, the compressive strength increased from 28 to 90 days across all mixtures, indicating continued hydration and strength development. To support experimental findings, machine learning models—Polynomial Regression, Random Forest Regression, and Support Vector Regression (SVR)—were developed to predict mechanical properties based on input parameters such as filler type, replacement ratio, and curing duration. Among these, Polynomial Regression yielded the highest accuracy, with R² values exceeding 0.99 for tensile and flexural strength predictions. These outcomes validate the applicability of data-driven modelling in optimizing sustainable composite formulations. The study concludes that DPRF is a technically and environmentally viable substitute for natural aggregates in composite materials, and that predictive modelling can significantly enhance material design processes by enabling accurate performance estimation.

Keywords

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