Effect of structural parameters on compression performance of autoclaved aerated concrete: Simulation and machine learning

Autoclaved aerated concrete (AAC) has broad applications in civil engineering due to its lightweight, thermal and sound insulation. However, the relation between the complex random porous structures and the compression performances of AAC is complex, limiting the optimization and application of AAC. In this study, finite element simulation was implemented and validated based on the experimental results. The simulation results showed that the compressive strength of AAC increased by 31.7, 45.8, and 134% with the porosity decreasing from 70% to 40%, the average pore diameter decreasing from 1.5 to 0.5 mm, and the pore connectivity decreasing from 50% to 0, respectively. Then, based on the numerical dataset, integrated machine learning methods were implemented to rapidly predict the compressive properties of AAC and analyze the main factors affecting the compressive properties. The ML results showed that the CatBoost model had the best predictive performance based on the small dataset of simulation results, with an average relative error of 18% and 7% for compressive strength and modulus of AAC, respectively. The component modulus was the most important feature for predicting the compressive strength and modulus of AAC.