380 input in advance how many metres the width (actual size) of the entire image corresponds to. This function has been shown to greatly accelerate the RQD measurement process, thereby reducing subjective variations among operators when compared to conventional manual visual measurement. It is anticipated that this will position the RQD as a pragmatic instrument for expeditious and precise on-site rock mass evaluation within the domains of geological engineering and civil and construction engineering. Figure 1 RQD measurement using automatic crack detection on an uploaded image. 3. CASE STUDY 3.1. Data preparation The classification task was performed on tuff, mudstone, and other rocks obtained from advanced borehole cores in tunnelling. In this study, the construction of the labelled dataset required for machine learning was performed by the learning module of the developed application. As shown in Figure 2, the user selects the label name of interest and specifies the Region of Interest (ROI) for the corresponding region. This efficient method allows the user to construct training data. In this experiment, three types of rocks (designated Rocks A, B, and C) were designated as classification classes, and a total of 351,006 pixels of hyperspectral data were acquired as training data. The dataset was then divided into three parts: 80% was designated as the training set, 10% as the validation set, and 10% as the test set, for the purpose of evaluating the model.
RkJQdWJsaXNoZXIy MTM0Mzk2