ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Abstract This research investigates the application of explainable quantum machine learning (QML) for predictive maintenance in the railroad industry. By utilizing Ground-Penetrating Radar (GPR) data to characterize subsurface track conditions (Ballast Fouling Index, Ballast Thickness Index, Layer Roughness Index, and Moisture Likelihood Index), a quantum neural network (QNN) model was developed to predict track geometry (profile and alignment) defects in a Class 3 railroad track. Shapley Additive Explanations (SHAP) were employed to analyze the feature importance and the model's decision-making processes to ensure model interpretability. The QNN model correctly predicted 42 out of 55 test data points. SHAP analysis identified the ballast fouling index and layer roughness index as the most influential parameters, aligning with engineering expectations.

Dengimowei Alabintei

Nii Attoh-Okine

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering

Hybrid Quantum Neural Network and Shapely Additive Explanations in Railway Track Geometry Modeling

混合量子神经网络与Shapley加性解释在铁路轨道几何建模中的应用

Abstract A sparsity-based optimization approach is presented for determining the equations of motion of stochastically excited nonlinear structural systems. This is done by utilizing measured excitation-response realizations in the formulation of the related optimization problem, and by considering a library of candidate functions for representing the system governing dynamics. Note that a novel aspect of the approach relates to treating, also, systems endowed with fractional derivative elements. Clearly, this is of significant importance to a multitude of diverse applications in engineering mechanics taking into account the enhanced modeling capabilities of fractional calculus. Further, the fundamental theoretical and computational aspects of various representative, state-of-the-art, numerical schemes for solving the derived sparsity-based optimization problem are reviewed and discussed. A Bayesian compressive sampling approach that exhibits the additional advantage of quantifying the uncertainty of the estimates is considered as well. Furthermore, comparisons and a critical assessment of the employed numerical schemes are provided with respect to their efficacy in determining the nonlinear structural system equations of motion. In this regard, two illustrative numerical examples are considered pertaining to a nonlinear tuned-mass-damper-inerter vibration control system, and to a nonlinear electromechanical energy harvester, both endowed with fractional derivative elements.

Georgios Pasparakis

Vasileios Fragkoulis

Ioannis A. Kougioumtzoglou

On the efficacy of sparse representation approaches for determining nonlinear structural system equations of motion

稀疏表示方法在确定非线性结构系统运动方程中的有效性研究

Chen Jiang

Zhen Hu

Alba Sofi

Liang Gao

Haobo Qiu

Byeng Dong Youn

Special Section on Uncertainty-Aware Diagnostics and Prognostics for Health Monitoring and Risk Management of Engineered Systems

工程系统健康监测与风险管理中的不确定性感知诊断与预测特辑

Abstract In this paper, we provide a comprehensive definition and classification of various sources of uncertainty within the fields of Structural Dynamics, System Identification, and Structural Health Monitoring (SHM), with a primary focus on the latter. Utilizing the classical input-output system representation as a main contextual framework, we present a taxonomy of uncertainties, intended for consistent classification of uncertainties in SHM applications: (i) input uncertainty; (ii) model form uncertainty; (iii) model parameter/variable uncertainty; (iv) measurement uncertainty; and (v) inherent variability. We then critically review methods and algorithms that address these uncertainties in the context of key SHM tasks: system identification and model inference, model updating, accounting for environmental and operational variability, virtual sensing, damage identification, and prognostic health management. A benchmark shear frame model with hysteretic links is employed as a running example to illustrate the application of selected methods and algorithmic tools. Finally, we discuss open challenges and future research directions in uncertainty quantification for SHM.

Antonios Kamariotis

Konstantinos Vlachas

Vasileios Ntertimanis

Ioannis Koune

Alice Cicirello

Eleni Chatzi

On the consistent classification and treatment of uncertainties in structural health monitoring applications

结构健康监测应用中不确定性的一致分类与处理

Abstract Additive manufacturing (AM) has been growing rapidly to transform industrial applications. However, the fundamental mechanism of AM has not been fully understood which resulted in low success rate of building. A remedy is to introduce surrogate modeling based on experimental dataset to assist additive design and increase design efficiency. As one of the first papers for predictive modeling of AM especially direct energy deposition (DED), this paper discusses a bidirectional modeling framework and its application to multiple DED benchmark designs including: (1) forward prediction with cross-validation, (2) global sensitivity analyses, (3) backward prediction and optimization, and (4) intelligent data addition. Approximately 1150 mechanical tensile test samples were extracted and tested with input variables from machine parameters, postprocess, and output variables from mechanical, microstructure, and physical properties.

Yiming Zhang

Sreekar Karnati

Soumya Nag

Neil Johnson

Genghis Khan

Brandon Ribic

Accelerating Additive Design With Probabilistic Machine Learning

加速增材设计与概率机器学习

Abstract Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and Functional Principal Component Analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm-assisted orthogonal matching pursuit for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.

Abbreviation
Journal Impact	1.98
Quartiles(Global)	ENGINEERING, MULTIDISCIPLINARY(Q2)

ISSN	2332-9017, 2332-9025
h-index	19

Publication Information	Publisher: The American Society of Mechanical Engineers(ASME)，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2015，Proportion of original research papers: 100.00%，Self Citation Rate:5.60%， Gold OA Rate: 1.64%

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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Hybrid Quantum Neural Network and Shapely Additive Explanations in Railway Track Geometry Modeling

On the efficacy of sparse representation approaches for determining nonlinear structural system equations of motion

Special Section on Uncertainty-Aware Diagnostics and Prognostics for Health Monitoring and Risk Management of Engineered Systems

On the consistent classification and treatment of uncertainties in structural health monitoring applications

Accelerating Additive Design With Probabilistic Machine Learning

Hybrid Quantum Neural Network and Shapely Additive Explanations in Railway Track Geometry Modeling

On the efficacy of sparse representation approaches for determining nonlinear structural system equations of motion

Special Section on Uncertainty-Aware Diagnostics and Prognostics for Health Monitoring and Risk Management of Engineered Systems

On the consistent classification and treatment of uncertainties in structural health monitoring applications

Daily Engine Performance Trending using Common Flight Regime Identification