applsci-logo

Journal Browser

Journal Browser

Reliability and Risk Analysis of Structures and Applications to Design and Optimization, 2nd Edition

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 562

Special Issue Editors

School of Architecture, Syracuse University, Syracuse, NY 13244, USA
Interests: structural reliability engineering; reliability-based design and topology optimization; random vibration analysis; structural and architectural design integration
Special Issues, Collections and Topics in MDPI journals
Centre for Infrastructure Engineering, School of Engineering, Design & Built Environment, Western Sydney University, Penrith, NSW 2751, Australia
Interests: structural system reliability; reliability based design code calibration; system-level stochastic damage detection; disaster risk management; probabilistic strength models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue entitled “Reliability and Risk Analysis of Structures and Applications to Design and Optimization”.

This Special Issue focuses on recent advancements in structural reliability and risk analysis and their applications in design and optimization. Research developments in structural reliability and risk analysis have accompanied growing attention to sustainability, resilience, and integrity of engineering systems, including structures and infrastructure under uncertainties in research communities and practice. Increasing computational power and efficiency have facilitated the development of diverse methods and simulation techniques for the reliability and risk analysis of complex and complicated systems. This issue is devoted to the latest theoretical and numerical developments in reliability assessment and analyzing risks associated with structures and structural systems.

Reliability analysis and uncertainty quantification techniques are increasingly incorporated in practice, with the integration of multi-objective, multi-scale formulations, high-performance gradient-based/heuristic optimization algorithms, machine learning, and parallel computing for reliable and robust structural design and optimization. These advancements are instrumental in ensuring the reliability and robustness of structural design and optimization. The utilization of these tools in engineering challenges has been beneficial for both academic researchers and practicing engineers. Thus, this Special Issue will highlight the latest research developments in reliability-based design, engineering, and optimization, all geared toward enhancing the safety and reliability of structures.

The scope and topics of this issue include, but are not limited to structural system reliability; methods of reliability and risk assessment of a structure or structural system; reliability modeling and prediction; time-invariant and time-variant reliability and risk analysis; random vibration; machine learning, sensitivity analysis; algorithmic developments in reliability/robust based design optimization; novel applications of structural reliability methods and risk analysis in diverse areas such as structural mechanics, construction material, and design; data-driven uncertainty quantification and risk analysis.

We welcome original research papers, and review articles with new insights and perspectives on pioneering developments and their applications, including industrial case studies. All papers submitted for consideration in this Special Issue will undergo a rigorous peer-review process to ensure the wide dissemination of research findings, technological advancements, and the introduction of novel methodologies and their practical applications. We warmly encourage experts in these fields to share their contributions with us for publication in this Special Issue.

Dr. Junho Chun
Dr. Won-Hee Kang
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

  • structural reliability
  • risk analysis
  • reliability-based design and optimization
  • system reliability
  • complex systems
  • machine learning
  • data-driven uncertainty quantification

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 3056 KiB  
Article
Adam Bayesian Gaussian Process Regression with Combined Kernel-Function-Based Monte Carlo Reliability Analysis of Non-Circular Deep Soft Rock Tunnel
by Jiancong Xu, Ziteng Yan and Yongshuai Wang
Appl. Sci. 2024, 14(17), 7886; https://doi.org/10.3390/app14177886 - 5 Sep 2024
Viewed by 322
Abstract
Evaluating the reliability of deep soft rock tunnels is a very important issue to be solved. In this study, we propose a Monte Carlo simulation reliability analysis method (MCS–RAM) integrating the adaptive momentum stochastic optimization algorithm (Adam), Bayesian inference theory and Gaussian process [...] Read more.
Evaluating the reliability of deep soft rock tunnels is a very important issue to be solved. In this study, we propose a Monte Carlo simulation reliability analysis method (MCS–RAM) integrating the adaptive momentum stochastic optimization algorithm (Adam), Bayesian inference theory and Gaussian process regression (GPR) with combined kernel function, and we developed it in Python. The proposed method used the Latin hypercube sampling method to generate a dataset sample of geo-mechanical parameters, constructed combined kernel functions of GPR and used GPR to establish a surrogate model of the nonlinear mapping relationship between displacements and mechanical parameters of the surrounding rock. Adam was used to optimize the hyperparameters of the surrogate model. The Bayesian inference algorithm was used to obtain the probability distribution of geotechnical parameters and the optimal surrounding rock mechanical parameters. Finally, the failure probability was computed using MCS–RAM based on the optimized surrogate model. Through the application of an engineering case, the results indicate that the proposed method has fewer prediction errors and stronger prediction ability than Kriging or XGBoost, and it can significantly save computational time compared with the traditional polynomial response surface method. The proposed method can be used in the reliability analysis of all shapes of tunnels. Full article
Show Figures

Figure 1

Back to TopTop