Artificial neural network based modeling of the coupled effect of sulphate and temperature on the strength of cemented paste backfill

Among the different options for mine waste management, cemented paste backfills (CPB) have become important in mining operations around the world due to their environmental and economic benefits. The key design parameter of a CPB structure is its mechanical stability, which is commonly evaluated by the uniaxial compressive strength (UCS) of the CPB material. Experimental studies have shown that the sulphate present within the CPB and the curing temperatures can significantly affect the strength of CPBs. The increasing use of CPBs in underground mine operations as well as the subjection of CPBs to a large variability of thermal (curing temperature) and chemical (sulphate content) loads, make it necessary to model and quantify the coupled effect of sulphate and curing temperature on the strength (key design parameter) of CPBs. Therefore, the main objective of this study is to develop a methodological approach and a mathematical model based on an artificial neural network (ANN) to analyze and predict the effect of different amounts of sulphate on the strength of mature CPBs cured at various temperatures. Based on the experimental results of UCS tests from previous studies on various CPBs, the authors have developed an ANN model by using an ANN methodology implemented through MATLAB (TM). The developed model is validated with experimental data that is not used for the model development. The validation shows good agreement between the predicted and experimental data. The results from the ANN model of this study show that the coupled effect of curing temperature and sulphate significantly affects the strength of CPBs. This effect can be positive (strength increase) or negative (strength decrease) depending on the initial amount of sulphate content, the curing temperature, and type of binder. Furthermore, this study demonstrates that ANN can be used as a valuable tool to evaluate the coupled influence of sulphate and temperature on the strength of CPBs, i.e., it is a suitable tool for the optimization of a CPB mixture.