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.

中文翻译：

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结构参数对蒸压加气混凝土压缩性能的影响：模拟和机器学习

蒸压加气混凝土（AAC）因其轻质、隔热、隔音等特点在土木工程中有着广泛的应用。然而，复杂的随机多孔结构与AAC的压缩性能之间的关系复杂，限制了AAC的优化和应用。在本研究中，基于实验结果实施并验证了有限元模拟。模拟结果表明，随着孔隙率从70%降低到40%，平均孔径从1.5 mm降低到0.5 mm，孔隙连通性从50%降低到0.5 mm，AAC的抗压强度分别提高了31.7%、45.8%和134%。分别为 0。然后，基于数值数据集，采用集成机器学习方法快速预测AAC的压缩性能并分析影响压缩性能的主要因素。机器学习结果表明，基于小数据集的模拟结果，CatBoost 模型具有最佳的预测性能，AAC 的抗压强度和模量的平均相对误差分别为 18% 和 7%。组件模量是预测 AAC 抗压强度和模量的最重要特征。