Special Issue "Multi-Hazard Risk and Resilience"
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".
Deadline for manuscript submissions: 31 January 2022.
Special Issue Editors
Interests: soil–structure interaction; smart seismic design, materials, and devices; performance-based earthquake engineering; statistical and machine learning; risk, loss, and resilience assessment under multiple hazards
Interests: risk and resilience; structural engineering; lifecycle engineering; climate change; sustainability
Special Issues and Collections in MDPI journals
Special Issue Information
Dear Colleagues,
The staggering development of buildings and infrastructures worldwide is coupled with their increased exposures to multiple hazards, including earthquakes, tsunamis, riverine flooding, hurricanes, strong winds, wildfires, landslides, ageing and deterioration, etc. Recent extreme events (e.g., the 2011 Tohoku earthquake and 2017 Hurricane Harvey) have also resulted in the multi-hazard vulnerability of civil infrastructure, causing not only physical damage but also cascading socioeconomic impacts on affected regions.
This Special Issue, “Multi-Hazard Risk and Resilience”, aims to bring together cutting-edge research advances in assessing, designing, and retrofitting individual structures or spatially distributed structural portfolios to protect them against independent, concurrent, or cascading hazards. The methodological framework for multi-hazard risk and resilience convolves probabilistic hazard analysis, structural exposure models, hazard vulnerability assessment, and subsequent risk and resilience quantification. Moreover, recent developments in statistical and machine learning can be leveraged to tackle related problems that are challenging or impossible to solve using traditional methods. Given this, this Special Issue welcomes original contributions containing fundamental research, case studies, opinion papers, or review articles on the following research topics:
- Probabilistic hazard analysis;
- Structural response simulation;
- Optimal hazard intensity measures;
- Probabilistic hazard demand and capacity models;
- Hazard fragility, risk, and resilience assessment;
- Lifecycle analysis under climate change;
- Performance-based design, retrofit, and rehabilitation;
- Statistical and machine learning;
- Infrastructure interdependencies;
- Community resilience.
Prof. Dr. Yazhou (Tim) Xie
Prof. Dr. You Dong
Prof. Dr. Changhai Zhai
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 papers will be 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. Buildings is an international peer-reviewed open access monthly 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 1600 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
- hazard analysis
- fragility model
- risk and resilience assessment
- lifecycle analysis
- climate change
- performance-based design
- statistical inference
- machine learning
- interdependency
- community resilience