Faculty Publications
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Item Universal Grey Number Systems for Uncertainty Quantification(Springer Science and Business Media Deutschland GmbH, 2023) Kumar, A.; Balu, A.S.In the recent past, modelling and analysis of structures with uncertain parameters have evoked significant interest.Physical imperfections, model flaws and system complexities can all be sources of uncertainty.In addition, the action loads (live, wind and earthquake) applied to a structure during its lifetime are not deterministic, hence for the proper performance assessment of the structural system, it is essential to properly account for these uncertainties.Uncertainties are usually described by probabilistic and non-probabilistic approaches.The growing interest in the non-probabilistic approach developed due to the incredibility of the probabilistic approach when data is insufficient.For estimating the ranges of the structural system’s response, the interval finite element approach looks to be acceptable, whose input parameters are defined in the ranges.However, the range of values predicted by the interval analysis suffers dependency problem.This can cause the computed findings to be overestimated.Although, the use of numerical truncation technique, parameterization of intervals and subinterval technique suggested by several researchers to avoid the dependency problem caused by general interval arithmetic.The physical rules (distributive law) are not violated by a universal grey numbers are a form of grey number and predict accurate results when compared with the interval approach.The universal grey number system is one such approach where computational efficiency and accuracy can be achieved when the input parameters are available in the ranges/interval. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Epistemic uncertainty quantification in structural systems using improved universal grey theory(Elsevier Ltd, 2023) Kumar, A.; Balu, A.S.It is important to account for uncertainties in structures during the analysis and design. Based on the source and nature, the uncertainties can be classified as aleatory and epistemic. Aleatory uncertainties arise due to the intrinsic randomness nature of physical system, whereas epistemic uncertainties realize on account of insufficient knowledge. When the information about the system is grey (i.e., partially available as range or interval), methods such as combinatorial approach, interval methods (IM) and universal grey theory (UGT) are generally adopted. The combinatorial optimization becomes computationally expensive when the dimension of uncertain system is large. Interval analysis leads to overestimation due to violations of the physical law and dependency problem. The satisfaction of the physical law (distributive law) that arises out of defining the arithmetic relations, contributes to the UGT free from dependency problem, and makes the approach more efficient. The traditional UGT is ineffective in certain conditions, when either one or both the bounds are negative with the absolute value of the upper bound being smaller i.e.,x¯⩽x̲. Therefore, this paper proposes a necessary modification in arithmetic operations to overcome the incapability of traditional UGT. The efficiency of proposed method is demonstrated through three numerical examples. Comparisons have been made with the conventional techniques to substantiate the proposed methodology, and the results obtained show that the proposed method is computationally efficient in terms of efforts and accuracy. © 2023 Institution of Structural EngineersItem 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.Item 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