Forecasting system for engineered bamboo design properties

Abstract

The present technology discloses the development of a trained learning model for forecasting system using artificial neural networks (ANNs) along with genetic algorithms (GA) to predict the compressive and flexural strength of engineered bamboo products. The model processes mechanical attributes including binder properties, fiber content, and curing duration that is to be compares the results with the database of the system. Due to the significant cons of manual testing in engineered bamboo products, including being time-consuming, resource-intensive, and limited scalability, the utility model provides an efficient and cost-effective alternative for determining material strength. Furthermore, the optimized testing parameters generated by the model can be stored and applied in subsequent testing of engineered bamboo products.