Prediction and Optimization of Mechanical Properties of Water Hyacinth Fiber‐Reinforced Polymer Composites Using Spiking Neural Networks

ABSTRACT The research examines a spike neural networks (SNNs) model that uses trust region policy optimization (TRPO) to predict the mechanical properties of water hyacinth fiber‐reinforced composites, focusing specifically on their tensile and compressive strength values. The lightweight and widespread availability of biodegradable water hyacinth makes it an attractive sustainable material for use as a reinforcing component. We collected two datasets from the existing experimental literature. Database 1 contains 420 compressive strength samples (SNN1) and Database 2 contains 185 tensile strength samples (SNN2). The input properties we considered were fiber volume fraction, matrix composition, and curing conditions. Our methodology included normalizing the data and using a diffusion matrix to analyze feature relationships and we used TRPO for training purposes to achieve better stability in convergence. Our regression analysis revealed an impressive fit, with R 2 values soaring above 0.97. We observed slopes ranging from 0.96 to 0.97 for SNN1, and between 0.85 and 0.88 for SNN2, along with prediction errors staying below 0.5 MPa and 0.01 MPa, respectively. The SNN‐TRPO system has proven to be a powerful tool for predictive modeling and optimizing parameters, paving the way for the production of eco‐friendly and good‐performance composite material.