Slope failures pose significant threats to human safety and vital infrastructure. The urgent need for the accurate prediction of these geotechnical events is driven by two main goals: advancing our understanding of the underlying geophysical mechanisms and establishing efficient evacuation protocols. Although traditional physics-based models offer in-depth insights, their reliance on numerous assumptions and parameters limits their practical usability. In our study, we constructed an experimental artificial slope and monitored it until failure, generating an in-depth displacement dataset. Leveraging this dataset, we developed and compared prediction models rooted in both statistical and machine learning paradigms. Furthermore, to bridge the gap between generic evaluation metrics and the specific needs of slope failure prediction, we introduced a bespoke performance. Our results indicate that while the statistical approach did not effectively provide early warnings, the machine learning models, when assessed with our bespoke performance metric, showed significant promise as reliable early warning systems. These findings hold potential to fortify disaster prevention measures and prioritize human safety.

Data-driven fault diagnosis has received significant attention in the era of big data. Most data-driven methods have been developed under the assumption that both training and test data come from identical data distributions. However, in real-world industrial scenarios, data distribution often changes due to varying operating conditions, leading to a degradation of diagnostic performance. Although several domain adaptation methods have shown their feasibility, existing methods have overlooked metadata from the manufacturing process and treated all domains uniformly. To address these limitations, this article proposes a weighted domain adaptation method using a graph-structured dataset representation. Our framework involves encoding a collection of datasets into the proposed graph structure, which captures relations between datasets based on metadata and raw data simultaneously. Then, transferability scores of candidate source datasets for a target are estimated using the constructed graph and a graph embedding model. Finally, the fault diagnosis model is established with a voting ensemble of the base classifiers trained on candidate source datasets and their estimated transferability scores. For validation, two case studies on rotor machinery, specifically tool wear and bearing fault detection, were conducted. The experimental results demonstrate the effectiveness and superiority of the proposed method over other existing domain adaptation methods.

Slope failure is a severe natural disaster that can cause property damage and human costs. In order to develop a warning system for slope failure, various studies have been conducted, including research based on both physics-based models and machine learning-based models. While machine learning-based approaches have shown promise due to their ability to automatically extract hidden patterns in data, conventional machine learning models have their limitations. Specifically, while they can always provide a prediction value, they fail to provide information about the uncertainty of the prediction results. In this study, we developed a machine learning model that can predict the slope failure by training trends in time-series data. Our proposed model addresses the limitations of the conventional machine learning models by incorporating the Monte Carlo dropout to calculate the uncertainty during the prediction stage. The experimental results demonstrated that the proposed model significantly outperforms the conventional machine learning models in terms of both its prediction accuracy and the ability to estimate uncertainty. Furthermore, the model proposed in this study can support decision-makers by providing more accurate information than the conventional models.