The Effect of Elevated Temperature on Bond Performance of Alkali-activated GGBFS Paste

The main reaction products were investigated by analysis of microstructure of alkali-activated ground granulated blast furnace slag （GGBFS） paste. An experimental research was performed on bond performance of alkali-activated GGBFS paste as a construction adhesive after exposure to 20-500℃. Through XRD analysis, a few calcium silicate hydrate, hydrotalcite and tetracalcium aluminate hydrate were determined as end products, and they were filled and packed each other at room temperature. In addition, akermanite dramatically increased at 800 ~C and above. The two key parameters, the ultimate load Pu.T and effective bond length Le, were determined using test data of carbon fiber-reinforced polymer （CFRP）-to-concrete bonded joints at elevated temperature. The experimental results indicate that the ultimate load Pu.T remains relatively stable initially and then decreases with increasing temperature. The effective bond length Le increases with increasing temperature except at 300℃. The proposed temperature-dependent effective bond length formula is shown to closely represent the test data.

A machine learning model to predict unconfined compressive strength of alkali-activated slag-based cemented paste backfill

The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the relationships between a single design parameter and the UCS,independently of each other.Although machine learning(ML)methods have proven efficient in understanding relationships between multiple parameters and the UCS of ordinary Portland cement(OPC)-based CPB,there is a lack of ML research on AAS-based CPB.In this study,two ensemble ML methods,comprising gradient boosting regression(GBR)and random forest(RF),were built on a dataset collected from literature alongside two other single ML methods,support vector regression(SVR)and artificial neural network(ANN).The results revealed that the ensemble learning methods outperformed the single learning methods in predicting the UCS of AAS-based CPB.Relative importance analysis based on the bestperforming model(GBR)indicated that curing time and water-to-binder ratio were the most critical input parameters in the model.Finally,the GBR model with the highest accuracy was proposed for the UCS predictions of AAS-based CPB.