Surrogate Model-Based Stochastic FPF Study of Tensile-Loaded Laminated Bamboo Composite

Laminated bamboo composites (LBCs) are becoming popular as an eco-friendly and renewable alternative to conventional materials as they are low cost and have high specific strength. Unidirectional beam boards and cross-laminated boards are commercially available LBC variants used in house construction. However, predicting the performance of these materials can be challenging due to their stochastic material properties, which can lead to cracks forming in various piles when subjected to continuous static loading. This study employs classical laminate theory (CLT) for determining LBCs’ first ply failure (FPF) load under tensile loading using Tsai–Wu and Tsai–Hill failure theories. The maximum allowable normal stress was 529.77 × 106 N/m2 and 366.34 × 106 N/m2, according to the Tsai–Wu and Tsai–Hill theories. Monte Carlo simulation (MCS) was used to analyse the stochastic effects on strength ratio (SR). The MCS yielded SR values within the range of 7.85 × 106 to 8 × 106 N/m for the Tsai–Wu theory and from 5.46 × 106 to 5.53 × 106 N/m for the Tsai–Hill theory. Additionally, an artificial neural network (ANN) model was trained to predict the random FPF load of LBCs, aiding in designing and optimising LBC structures. This approach can be used as a predictive tool in the early stages of design to explore design configurations and material properties, thus reducing the need for costly experimental testing.