The complex design parameters and lack of standard mix design methods generate a difficulty in the high efficiently preparation of alkali-activated foamed geopolymer (AAFG). Therefore, this paper proposes an innovative intelligent design method of AAFG based on machine learning (ML) and Metaheuristic Optimization Algorithms (MOA). The effects of twelve factors on drying density (DD) and compressive strength (CS) of AAFG are revealed. The DD and CS predictive models of AAFG with high accuracy (R = 0.96 and 0.84) and strong generalization are trained through Random Forest (RF). The enlargements of proportions of granulated blast furnace slag (GBFS) and metakaolin (MK) in precursors can improve the CS but are not conducive to reduction of DD. Higher proportion of fly ash (FA) in precursors can efficiently reduce DD but can weaken the CS. Higher or lower NaO content, silicate modulus (Ms) and water to binder (W/B) can reduce DD and CS with the optimal foaming conditions of 20 %, 1.0 and 0.75. More additions of foaming agents and foam stabilizers can reduce DD, while the CS has a remarkable degradation. Higher or lower foaming temperature (FT) is both not beneficial for the reduction of DD and enhancement of CS with the optimal FT of 60 °C. The intelligent design system of AAFG developed by RF and Slime Mould Algorithm (SMA) has easy operability, high efficiency, and good stability. The optimal preparation parameters can be efficiently acquired and the corresponding performances can be quickly and accurately predicted.

中文翻译：

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碱活化泡沫地质聚合物的创新智能设计方法：混合物优化和性能预测

复杂的设计参数和缺乏标准配合比设计方法给碱活化泡沫地质聚合物（AAFG）的高效制备带来了困难。因此，本文提出了一种基于机器学习（ML）和元启发式优化算法（MOA）的AAFG创新智能设计方法。揭示了十二个因素对 AAFG 干燥密度 (DD) 和抗压强度 (CS) 的影响。通过随机森林（RF）训练出具有高精度（R = 0.96和0.84）和强泛化能力的AAFG DD和CS预测模型。前驱体中粒化高炉矿渣（GBFS）和偏高岭土（MK）比例的增大可以提高CS，但不利于DD的降低。前体中较高比例的粉煤灰 (FA) 可以有效降低 DD，但会削弱 CS。较高或较低的NaO含量、硅酸盐模量(Ms)和水/粘结剂(W/B)可以降低DD和CS，最佳发泡条件为20%、1.0和0.75。较多添加发泡剂和稳泡剂可以降低DD，而CS则有显着的降解。较高或较低的发泡温度（FT）都不利于DD的降低和CS的增强，最佳FT为60℃。采用射频和史莱姆模算法（SMA）开发的AAFG智能设计系统，操作简单、效率高、稳定性好。可以有效地获取最佳制备参数，并快速准确地预测相应的性能。