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Reliability and Risk Assessment for Sustainable Development: Engineering and Science

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1696

Special Issue Editors

School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: spatial variability of geotechnical parameters; random field and stochastic finite element; constitutive model of frozen soil; stochastic analysis for geotechnical engineering

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Guest Editor
School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China
Interests: offshore geotechnical engineering; tunnelling and underground space technology; marine civil engineering construction; artificial ground freezing technology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Civil Engineering, Yantai University, Yantai 264005, China
Interests: environmental geotechnics; artificial ground freezing; cryogenic barrier

Special Issue Information

Dear Colleagues,

Engineering risk assessment is of great significance to sustainable development. Environmental impact assessment is a key part of project risk assessment, which aims to identify and predict the potential impact of a project on the environment, so as to provide a basis for environmental protection and sustainable development. Through evaluation, it is possible to ensure that engineering projects meet economic and social development without causing irreversible damage to the environment, thus promoting sustainable development. Project risk assessment also needs to identify and prevent risks that may occur in the process of project implementation, including technical risks, market risks, policy risks, natural disaster risks, etc. This will help ensure the smooth progress of the project, reduce the negative impact on the environment, and ensure the harmonious coexistence of the project and the environment. The main contents of engineering risk assessment include risk identification and classification, risk probability and consequence assessment, risk impact analysis, risk control measure assessment, risk management and monitoring, risk communication and participation, and risk assessment tools and methods.

This Special Issue will publish high-quality, original research papers in the following overlapping fields:

  • Case histories;
  • Engineering risk assessment;
  • Engineering design;
  • Geo-investigation;
  • Geomechanics analysis;
  • Design and modeling
  • Construction and monitoring;
  • Frozen soil environment;
  • Reliability analysis;
  • Urban engineering geology;
  • Slope instability;
  • Freeze–thaw disaster;
  • underground engineering;
  • Deformation and failure;
  • Multi-field coupling;
  • Uncertainty analysis;
  • Probability method;
  • Risk assessment;
  • Disaster warning.

Dr. Tao Wang
Prof. Dr. Jun Hu
Prof. Dr. Zhiqiang Ji
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. Sustainability 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

  • engineering disaster
  • reliability analysis
  • risk assessment
  • sustainable development
  • multi-field coupling
  • interaction

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Published Papers (1 paper)

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Research

20 pages, 3182 KiB  
Article
Stochastic Risk Assessment Framework of Deep Shale Reservoirs by a Deep Learning Method and Random Field Theory
by Tao Wang, Shuangjian Li, Jian Gao, Xuepeng Zhang and Miao Chen
Sustainability 2024, 16(23), 10645; https://doi.org/10.3390/su162310645 - 4 Dec 2024
Viewed by 849
Abstract
Risk assessment of deep shale reservoirs is very important for subsurface energy development. However, due to complex geological environments and physicochemical interactions, shale reservoir fabric parameters exhibit variability. Moreover, the actual investigation and testing information is very limited, which is a typical small-sample [...] Read more.
Risk assessment of deep shale reservoirs is very important for subsurface energy development. However, due to complex geological environments and physicochemical interactions, shale reservoir fabric parameters exhibit variability. Moreover, the actual investigation and testing information is very limited, which is a typical small-sample problem. In this paper, the heterogeneity and statistical characteristics of deep shale reservoirs are clarified by the measured mechanical parameters. A deep learning method for deep shale reservoirs with limited survey data information is proposed. The variability of deep shale reservoirs is characterized by random field theory. A variable stiffness method and stochastic analysis method are developed to evaluate the risk of deep shale reservoirs. The detailed workflow of the stochastic risk assessment framework is presented. The frequency distribution and failure risk of deep shale reservoirs are calculated and analyzed. The risk assessment of deep shale reservoirs under different model parameters is discussed. The results show that a stochastic risk assessment framework of deep shale reservoirs, using a deep learning method and random field theory, is scientifically reasonable. The scatter plots of the elasticity modulus (EM), cohesive force (CF), and Poisson ratio (PR) distribute along the 45-degree line. The different distributed variables of EM, CF, and PR have a positive correlation. The statistical properties of the measurement data and deep learning data are approximately the same. The principal stress of deep shale follows the normal distribution with significance level 0.1. Under positive copula conditions, the maximum failure probability is 5.99%. Under negative copula conditions, the maximum failure probability is 4.58%. Different copula functions under positive and negative copula conditions have different failure probabilities. For the exponential correlation structure, the minimum failure probability is 3.46%, while the maximum failure probability is 6.19%. The mean failure probability of the EM, CF, and PR of deep shale reservoirs is 4.85%. Different random field-related structures and parameters have different influences on the failure risk. Full article
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