The characterisation of materials is a prerequisite for evaluating and predicting the stability of mining waste dumps. Over the past three decades, the BHP Mitsubishi Alliance Coal framework has been a cornerstone in Australian coal mines for characterising waste dump materials. However, its reliance on subjective human observations has introduced potential inaccuracies and subjectivity into the process. In response to these limitations, this study proposes an innovative approach to classify coal spoil attributes by remotely acquiring images through phones/tablets. Automated image-based classification relies on feature extraction and a substantial amount of data. Nevertheless, the inherent complexity of geological factors contributing to the formation of both rare and dominant materials leads to imbalanced data. Recognising the need for classification mechanisms to overcome these challenges in spoil classification, the study explores and compares the use of convolutional neural networks, hybrid deep learning, and traditional techniques. Among the twelve models evaluated in this study, the ResNet18-k nearest neighbour model emerges as a powerful tool in geotechnical characterisation. However, it is essential to address issues of interpretability and adaptability to diverse datasets. As this study evolves, the field of geotechnical characterisation of spoil can anticipate the development of more robust methods in the future.

翻译：材料表征是评估和预测采矿废石堆稳定性的先决条件。过去三十年间，BHP三菱联盟煤系框架一直是澳大利亚煤矿废石堆材料表征的基石。然而，该框架依赖主观人工观测，导致过程中存在潜在的不准确性和主观性。针对这些局限，本研究提出了一种创新方法，通过手机/平板远程采集图像来分类煤矸石属性。基于图像的自动分类依赖于特征提取和大量数据。然而，地质因素形成稀有与主导材料的固有复杂性导致了数据不平衡。为克服煤矸石分类中的这些挑战，本研究探索并比较了卷积神经网络、混合深度学习及传统技术的应用。在评估的十二个模型中，ResNet18-k近邻模型脱颖而出，成为岩土特性表征的强大工具。但需解决模型可解释性及对多样化数据集的适应性问题。随着本研究的推进，煤矸石岩土特性表征领域有望在未来开发出更稳健的方法。