Evolutionary artificial intelligence approach for performance prediction of bio-composites

Giving the high amount of carbon and energy emission from the use of traditional building materials, the use of bio-composites made from industrial crops especially hemp has caught attention from researchers in recent years. These bio-composites not only enhance the thermal performance of buildings but also promote sustainable development due to their eco-friendly nature. Due to their highly heterogeneous nature, however, most of the existing studies on the bio-composites have only focused on experimental investigations, while mathematical modeling of physical, thermal and mechanical properties of biocomposite remains a challenge for the researchers. In this paper, an artificial intelligence (AI) based gene expression programming (GEP) technique is used to develop the mathematical models for predicting the dry density, compressive strength and thermal conductivity of hemp-based bio-composites. A large amount of database was established based on past studies and the most influential parameters were identified by several trial analyses. The proposed mathematical models showed a high correlation with the experimental results. All the models passed the statistical and performance index checks showing strong predictability, generalization capability and high accuracy of GEP-AI models. Comparison of results with the regression analysis techniques further proved the superiority of GEP-AI models over the traditional methods. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper utilizes artificial intelligence (AI) based gene expression programming (GEP) technique to develop mathematical models for predicting the dry density, compressive strength, and thermal conductivity of hemp-based bio-composites. The proposed mathematical models show high correlation with experimental results, passing statistical and performance index checks, demonstrating strong predictability, generalization capability, and high accuracy of GEP-AI models. Comparison with regression analysis techniques confirms the superiority of GEP-AI models over traditional methods.