Abstract
During iron ore process, a substantial amount of iron ore tailings (IOT) are generated, which can be caused environmental challenges. To mitigate this issue, the stabilised IOT can be repurposed as road material. The unconfined compressive strength (UCS) parameter is typically used to assess the quality control and mix designing of stabilised materials, which its measurement is time-consuming due to the required curing time (CT). Consequently, implementing machine learning techniques to determine and predict UCS values can significantly streamline the process and reduce mix design as well as quality control costs. This research aims to evaluate various machine learning models to predict the UCS of cement-stabilised IOT. Four input variables including cement percentage, CT, compaction moisture content (MC) and compaction energy were considered for UCS modelling. A comparison of the statistical results from the developed models revealed that the artificial neural network (ANN) method exhibited superior accuracy for both training and testing data, with R 2 values of 0.96 and 0.97, respectively. Moreover, a sensitivity analysis of the ANN model demonstrated that cement percentage had the most significant impact on the UCS, while compaction MC had the least. Lastly, a parametric study was conducted to evaluate the influence of various variables on the UCS.
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