Faculty Publications

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Human-in-the-Loop Data Analytics for Classifying Fatal Mining Accident Causes Using Natural Language Processing and Machine Learning Techniques
(Springer Science and Business Media Deutschland GmbH, 2025) Sharma, A.; Kumar, A.; Vardhan, H.; Mangalpady, A.; Mandal, B.B.; Senapati, A.; Akhil, A.; Saini, S.
Mining remains one of the most hazardous industries globally, marked by frequent fatalities resulting from complex operational risks. While accident investigation reports hold valuable insights for improving safety practices, the manual coding of fatality narratives remains labor-intensive, inconsistent, and impractical for large datasets. Although natural language processing (NLP) and machine learning (ML) techniques have gained traction for automating the analysis of safety narratives in other high-risk industries, their application to mining accident data, particularly within the Indian context, remains limited. Addressing this gap, the present study proposes a ML framework for the semi-automated classification of fatal accident causes from unstructured text narratives reported by the Directorate General of Mines Safety (DGMS) between 2016 and 2022. A total of 401 fatal accident descriptions were pre-processed and vectorized using Bag-of-Words, TF-IDF, and Word2Vec techniques, followed by model evaluation across multiple algorithms. A semi-automated classification scheme was developed to balance efficiency with expert oversight, where high-confidence predictions were assigned automatically and uncertain cases were flagged for manual review. Logistic regression combined with TF-IDF unigram features achieved the highest performance, with an F1 score of 0.78 and an accuracy of 0.81. Overall, the developed framework successfully auto-coded 68.75% of cases with 94% accuracy, 0.93 recall, and 0.91 precision. Word cloud visualizations were also employed to capture dominant words associated with different cause categories. The proposed framework offers a practical and operationally feasible solution for assigning fatality causes in the mining sector, contributing to active safety management, surveillance, and policy formulation. © Society for Mining, Metallurgy & Exploration Inc. 2025.
Root reinforcement of herbaceous vegetation for stabilization of coal mine overburden dump slopes
(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, A.; Nainegali, L.; Das, S.K.; Reddy, K.R.
Slope instability of coal mine overburden dumps poses significant challenges to mining safety and environmental sustainability. This study investigates the potential for root reinforcement offered by herbaceous vegetation (Dendrocalamus strictus and Cymbopogon citratus) for enhanced slope stability. A series of pot experiments were conducted to grow grasses with the coal mine overburden material. The survival and growth of grasses in the nutrient-devoid overburden are critical because they directly impact the effectiveness of root reinforcement. Therefore, the effect of amendment quantity on plant growth was assessed. A direct shear box test was conducted on the bare and rooted samples using a fabricated internal shear test assembly to determine the strength. The higher peak shear stress and dilatancy angle observed for the rooted specimens were due to the high root tensile strength mobilizing the shear stresses. The results of shear tests were subsequently employed in limit equilibrium slope stability analyses where material heterogeneity was considered to account for uncertainties linked to material properties. The deterministic analysis provided insights into the expected improvements in slope stability due to root reinforcement, offering a baseline for comparison. Meanwhile, the probabilistic analysis considered the variability in material properties, thus providing a more comprehensive understanding of the uncertainty associated with the slope stability assessment regarding the reliability index and probability of failure. By combining experimental investigations with rigorous analytical approaches, this study enhances our understanding of how grassroots reinforcement can enhance the stability of coal mine overburden dumps. © The Author(s), under exclusive license to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
An uncertainty-aware decision support system: Integrating text narratives and conformal prediction for trustworthy accident code classification
(Institution of Chemical Engineers, 2025) Kumar, A.; Senapati, A.; Upadhyay, R.; Chatterjee, S.; Bhattacherjee, A.; Samanta, B.
It is imperative to assign accident classification codes to the Mine Safety and Health Administration (MSHA) accident data for effective data analysis and risk assessment. Although trained personnel are capable of performing this task, the manual process is both time-consuming and resource-intensive. Automating the classification process with machine learning (ML) algorithms promises to expedite code assignment. However, ML predictions typically lack uncertainty metrics. This study proposes an uncertainty-aware hierarchical classification framework that assists human experts in efficiently and accurately assigning accident codes. Several text representation techniques combined with different ML algorithms were employed within a hierarchical architecture to assign classification codes. Low-frequency codes were consolidated into a single category, with a primary classifier distinguishing between these and a secondary classifier further classifying the grouped categories. Regularized Adaptive Prediction Sets (RAPS) was integrated to quantify uncertainty. Highly confident predictions yielding single-class sets were automatically classified, whereas multi-class sets were flagged for manual review. Primary Classifier with XGBoost with word2vec text representation achieved the best performance, with 95.12 % coverage, 37.02 % single-class prediction sets at 96.11 % accuracy, and an average prediction set size of 2.39. Whereas the secondary classifier, a logistic regression model with TF-IDF representation, yielded 96.19 % coverage, an average set size of 1.80, and 53.66 % single-class prediction sets with 98.90 % accuracy. Additionally, sensitivity analysis determined that a 95 % coverage guarantee offers the best trade-off between prediction set size and coverage. The framework effectively integrates conformal prediction to quantify uncertainty and aid human experts in improving the decision-making process in safety management. Although the framework is broadly applicable across different sectors, it needs to be retrained on domain-specific data for effective use. © 2025 The Institution of Chemical Engineers

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