The fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to predict the compressive strength of the fly ash-slag geopolymer. In this paper, data from the published literature were collected as the training set and the experimental results from laboratory experiments were used as the test set. Eight input parameters were determined, as follows: the percentage of fly ash, the percentage of slag, the water–cement ratio, the curing age, the modulus of alkali activator, the mass ratio of NaOH to Na2SiO3 and the moles of Na2O and SiO2 in the alkali activator. Three multilayer artificial neural network models were constructed using the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms to compare the prediction accuracy of the compressive strength of the fly ash-slag geopolymer paste at different ages (3, 7, and 28 d). It was concluded that the training set error of the BR–BP neural network was the smallest. Ultimately, the hyperparameter optimization of the BR–BP neural network was carried out to compare the training set and the test set errors before and after the optimization, and the results show that the BR–BP neural network model with hyperparameter optimization had the highest prediction accuracy.
In this paper, the mass loss test, relative dynamic elasticity modulus test, compressive strength test, thermogravimetric analysis (TGA-DTG), and mercury intrusion porosimetry (MIP) were used to study the performance of cement-based materials with low water-binder ratio after sulfate
attack, and the influence of microsilica dosage and erosion period on the performance was also investigated. The results indicate that the microsilica mixed can improve the macro-properties of cement-based material with low water-binder ratio after sulfate attack, and as the microsilica dosage
is increased, the improvement effect tends to decrease after an increase, while the microsilica dosage is 15%, all performance indexes are the best. This study also demonstrates that the microsilica dosage could affect the mass percent and pore structure distribution of the hydration products
of cement-based materials with low water-binder ratio after sulfate attack, and different pore structure distribution may have impact on compressive strength of cement-based materials. Besides, we established relation models for the influence of capillary pores’ and gel pores’
proportion on the compressive strength of cement-based material after sulfate attack, and concluded that there was no interaction between the impacts of two pore types on compressive strength.
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