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Applied Sciences
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Uncertainty and Reliability Analysis for Engineering Systems
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Uncertainty and Reliability Analysis for Engineering Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 6361

Special Issue Editors

Dr. Guijie Li

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Dalian University of Technology, Dalian 116024, China
Interests: uncertainty quantification; system reliability design; structural reliability analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Zhang

E-Mail Website
Guest Editor
School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an 710129, China
Interests: uncertainty quantification; aircraft reliability engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaobo Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Hefei University of Technology (HFUT), Hefei, China
Interests: structural reliability analysis; uncertainty-based design optimization; sensitivity analysis

Special Issue Information

Dear Colleagues,

Reliability is one of the core performance indexes of products, which has attracted increasingly more attention in recent years. For complex engineering systems, the uncertainties of geometric dimensions, material properties, and loads are more prominent. To ensure the realization of the high-reliability requirements of engineering systems, the above uncertainties must be fully considered in the design, manufacturing, and experimental process. However, due to the complexity of the problem, there are still a large number of challenging problems that need to be studied.

In this regard, this Special Issue focuses on reporting the recent advances related to uncertainty quantification, high-dimensional reliability analysis, sensitivity analysis, reliability-based multidisciplinary design optimization, reliability-based design optimization, and their practical applications in complex engineering systems.

Topics for potential contributions include but are not limited to the following:

  • Uncertainty quantification;
  • Advanced uncertainty propagation methodologies, g., metamodel methods and artificial intelligence algorithms;
  • Sensitivity analysis techniques and efficient algorithms;
  • Reliability-based design optimization;
  • Reliability-based multidisciplinary design optimization;
  • Reliability assessment for engineering systems;
  • Emerging tools for reliability design analysis and optimization;
  • Practical applications in engineering systems.

Dr. Guijie Li
Dr. Feng Zhang
Dr. Xiaobo Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • uncertainty quantification
  • reliability analysis
  • reliability-based design
  • reliability-based optimization
  • sensitivity analysis

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Published Papers (6 papers)

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Research

22 pages, 7239 KiB  
Article
A Reliability-Oriented Framework for the Preservation of Historical Railway Assets Under Regulatory and Material Uncertainty
by Thomas Wailes, Muhammad Khan and Feiyang He
Appl. Sci. 2025, 15(9), 4705; https://doi.org/10.3390/app15094705 - 24 Apr 2025
Viewed by 275
Abstract
Preserving historical railway assets presents a complex systems challenge, in which uncertainties in material performance, structural degradation, and regulatory requirements directly impact long-term reliability and operational continuity. Traditional maintenance practices often limit the use of modern materials, introducing inefficiencies, increased lifecycle costs, and [...] Read more.
Preserving historical railway assets presents a complex systems challenge, in which uncertainties in material performance, structural degradation, and regulatory requirements directly impact long-term reliability and operational continuity. Traditional maintenance practices often limit the use of modern materials, introducing inefficiencies, increased lifecycle costs, and higher failure risk due to material ageing and environmental exposure. This study proposes a reliability-informed preservation framework that supports the integration of contemporary materials into historical railway infrastructure while accounting for legal, material, and procedural uncertainties. The framework is validated through two industrial case studies, each reflecting different regulatory and operational constraints. The first case demonstrates the successful substitution of timber with certified PVC cladding on a non-listed signal box, achieving improved durability, reduced maintenance intervals, and enhanced system reliability. The second case explores an unsuccessful attempt to replace decayed timber gables with aluminium, in which late-stage planning misalignment, underestimated risks, and uncertainty in approval outcomes led to a significant cost increase and reduced reliability regarding delivery. By systematically applying and evaluating the framework under real-world conditions, this research contributes to engineering asset management by introducing a structured method for mitigating regulatory and material uncertainties. Full article
(This article belongs to the Special Issue Uncertainty and Reliability Analysis for Engineering Systems)
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11 pages, 1224 KiB  
Communication
Measurement Uncertainty and Compliance Evaluation Applied to Natural Gas Moisture
by Rosana Medeiros Moreira, Cesar Luís Biazon and Elcio Cruz de Oliveira
Appl. Sci. 2025, 15(5), 2482; https://doi.org/10.3390/app15052482 - 25 Feb 2025
Viewed by 544
Abstract
The reliability of natural gas moisture measurements is crucial in preventing corrosion in pipelines and equipment, ensuring burning efficiency and mitigating operational risks, and is often mandated by standards and regulations. An important quality parameter that aids in conformity assessment, particularly in risk [...] Read more.
The reliability of natural gas moisture measurements is crucial in preventing corrosion in pipelines and equipment, ensuring burning efficiency and mitigating operational risks, and is often mandated by standards and regulations. An important quality parameter that aids in conformity assessment, particularly in risk assessment, is measurement uncertainty. The assessment of measurement uncertainty, based on the Law of Uncertainty Propagation, depends on the mathematical model used to calculate this physicochemical property. This study aimed to compare different algorithms for calculating moisture content in natural gas, estimate and validate the measurement uncertainty based on the algorithms implemented in the Portable Moisture Analyzer (PM880) equipment, a portable hygrometer manufactured by Panametrics in Wilmington, NC, USA, and evaluate compliance with Brazilian legislation using guard bands as a decision rule. The moisture results in natural gas varied by a maximum of 1% among the three approaches presented. Furthermore, based on the dew point and pressure results, the expanded uncertainties of moisture were about 20%, which did not compromise the risk assessment for the consumer, as the moisture results were well below the specification value. Consequently, the upper tolerance limit of 58.4 ppmv H2O was established. Full article
(This article belongs to the Special Issue Uncertainty and Reliability Analysis for Engineering Systems)
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14 pages, 3425 KiB  
Article
Prediction of Wind Turbine Blade Stiffness Degradation Based on Improved Neural Basis Expansion Analysis
by Shuai Yang, Jianxiong Gao, Yiping Yuan, Jianxing Zhou and Lingchao Meng
Appl. Sci. 2025, 15(4), 1884; https://doi.org/10.3390/app15041884 - 12 Feb 2025
Viewed by 583
Abstract
To reduce the significant time and cost associated with wind turbine blade fatigue testing, the applicability of the deep learning model Neural Basis Expansion Analysis (N-BEATS) for modeling the stiffness degradation of wind turbine blades was investigated. First, on the basis of a [...] Read more.
To reduce the significant time and cost associated with wind turbine blade fatigue testing, the applicability of the deep learning model Neural Basis Expansion Analysis (N-BEATS) for modeling the stiffness degradation of wind turbine blades was investigated. First, on the basis of a traditional blade stiffness degradation model, the stiffness data were expanded to meet the data volume requirements of N-BEATS. Second, the basic block structure of N-BEATS was improved (by treating the sequence-to-sequence prediction problem as a nonlinear multivariate regression problem) to meet the specific prediction requirements of this task, and the Pinball Mean Absolute Percentage Error (Pinball-MAPE) loss function was adopted to further reduce bias during the prediction process. Additionally, two data augmentation methods—time series combination and random noise injection—were applied to mitigate the risk of model overfitting and improve prediction accuracy. Experimental results demonstrated that the model can effectively learn underlying patterns in the stiffness data and successfully predict the remaining stiffness. Full article
(This article belongs to the Special Issue Uncertainty and Reliability Analysis for Engineering Systems)
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20 pages, 6801 KiB  
Article
Uncertainty Quantification in Shear Wave Velocity Predictions: Integrating Explainable Machine Learning and Bayesian Inference
by Ayele Tesema Chala and Richard Ray
Appl. Sci. 2025, 15(3), 1409; https://doi.org/10.3390/app15031409 - 30 Jan 2025
Cited by 1 | Viewed by 819
Abstract
The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized [...] Read more.
The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized linear model (GLM) to enhance both predictive accuracy and uncertainty quantification in Vs prediction. The study utilizes an Extreme Gradient Boosting (XGBoost) algorithm coupled with Shapley Additive Explanations (SHAPs) and partial dependency analysis to identify key geotechnical parameters influencing Vs predictions. Additionally, a Bayesian GLM is developed to explicitly account for uncertainties arising from geotechnical variability. The effectiveness and predictive performance of the proposed models were validated through comparison with real case scenarios. The results highlight the unique advantages of each model. The XGBoost model demonstrates good predictive performance, achieving high coefficient of determination (R2), index of agreement (IA), Kling–Gupta efficiency (KGE) values, and low error values while effectively explaining the impact of input parameters on Vs. In contrast, the Bayesian GLM provides probabilistic predictions with 95% credible intervals, capturing the uncertainty associated with the predictions. The integration of these two approaches creates a comprehensive framework that combines the strengths of high-accuracy ML predictions with the uncertainty quantification of Bayesian inference. This hybrid methodology offers a powerful and interpretable tool for Vs prediction, providing engineers with the confidence to make informed decisions. Full article
(This article belongs to the Special Issue Uncertainty and Reliability Analysis for Engineering Systems)
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12 pages, 3407 KiB  
Article
Research on the Effect of Working Memory Training on the Prevention of Situation Awareness Failure in Shearer Monitoring Operations
by Xiaofang Yuan, Ruyi Song and Linhui Sun
Appl. Sci. 2024, 14(24), 11876; https://doi.org/10.3390/app142411876 - 19 Dec 2024
Viewed by 704
Abstract
The digitization of the instrument control system in monitoring operations makes the problem of the operator’s situational awareness failure more prominent. In order to better prevent this occurrence, this paper explores the failure of situational awareness from the perspective of cognitive function. The [...] Read more.
The digitization of the instrument control system in monitoring operations makes the problem of the operator’s situational awareness failure more prominent. In order to better prevent this occurrence, this paper explores the failure of situational awareness from the perspective of cognitive function. The subjects were randomly divided into two groups: a working memory training group and a control group. Working memory measurements and coal mining machine monitoring simulation system operation tasks were performed before and after training, and the task performance, situational awareness scale, and EEG index data were recorded. The results showed that, after the training, there was a significant improvement in the task performance of the monitoring operation and the scores of the situational awareness scale, and there were different degrees of activation in the θ, α2, and β1 frequency bands. It was demonstrated that working memory training could help to improve the rapid reaction and decision-making abilities of operators in complex or emergency situations, thus preventing the failure of situational awareness. This study provides a new direction for research on the prevention of situational awareness failure. Full article
(This article belongs to the Special Issue Uncertainty and Reliability Analysis for Engineering Systems)
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13 pages, 1785 KiB  
Article
Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive AdaBoost Algorithm
by Feng Zhang, Zijie Qiao, Yuxiang Tian, Mingying Wu and Xiayu Xu
Appl. Sci. 2024, 14(22), 10098; https://doi.org/10.3390/app142210098 - 5 Nov 2024
Cited by 2 | Viewed by 2032
Abstract
A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an [...] Read more.
A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the AdaBoost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method’s iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs. Full article
(This article belongs to the Special Issue Uncertainty and Reliability Analysis for Engineering Systems)
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