Autonomous particle-shape-based classification and identification of calcareous soils through machine learning

Recent geotechnical and geological applications have highlighted numerous challenges associated with conventional particle-size-based soil classification, particularly for the special calcareous soils widely used in marine and offshore constructions. Calcareous soils are always characterized by complex microstructures in terms of loose fabric, irregular particle shapes and abundant intra-particle pores. Therefore, the aim of this study is to establish autonomous particle shape categorization criteria for calcareous soils, and further facilitate their intelligent particle identification and classification using image-based machine learning (ML) techniques. First, an extensive collection of images of individual calcareous sand particles were acquired through microscopic photography to establish the datasets for ML training. Four key shape parameters – circularity, roundness, aspect ratio and convexity – were measured based on image processing and analysis. Subsequently, an unsupervised ML algorithm, K-means clustering, was applied to these shape parameters to autonomously determine optimal particle shape categories for calcareous soils. Finally, based on the obtained particle shape classes, two supervised convolutional neural network algorithms, AlexNet and YOLO-v3 (You Only Look Once version 3), were trained and applied to automatically identify and classify individual particles or particle assemblies of calcareous soils from various raw image types. The predictive results demonstrate the high accuracy and efficiency of ML models in identifying and classifying the particle shapes of calcareous soils.