Comparative analysis of traditional and transfer learning algorithms for coal spoil classification via close-range imagery

Article


Thiruchittampalam, Sureka, Shanmugalingam, Kuruparan, Banerjee, Bikram P, Glenn, Nancy F. and Raval, Simit. 2024. "Comparative analysis of traditional and transfer learning algorithms for coal spoil classification via close-range imagery." Georisk: assessment and management of risk for engineered systems and geohazards. https://doi.org/10.1080/17499518.2024.2422490
Article Title

Comparative analysis of traditional and transfer learning algorithms for coal spoil classification via close-range imagery

ERA Journal ID4336
Article CategoryArticle
AuthorsThiruchittampalam, Sureka, Shanmugalingam, Kuruparan, Banerjee, Bikram P, Glenn, Nancy F. and Raval, Simit
Journal TitleGeorisk: assessment and management of risk for engineered systems and geohazards
Number of Pages18
Year2024
PublisherTaylor & Francis
Place of PublicationUnited Kingdom
ISSN1749-9518
1749-9526
Digital Object Identifier (DOI)https://doi.org/10.1080/17499518.2024.2422490
Web Address (URL)https://www.tandfonline.com/doi/full/10.1080/17499518.2024.2422490
Abstract

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 sixteen models evaluated in this study, the ResNet18-support vector machine 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.

KeywordsMine waste; close-range images; dump stability; convolutional neural networks; deep hybrid learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020401905. Mining engineering
401304. Photogrammetry and remote sensing
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Byline AffiliationsUniversity of New South Wales
University of Moratuwa, Sri Lanka
School of Surveying and Built Environment
Boise State University, United States
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