Tuesdays & Thursdays: 1:00 pm to 4:00 pm
Ziqi Wang is an assistant professor in the Department of Civil and Environmental Engineering. His research focuses on analyzing and understanding the reliability, risk, and resilience of structures and critical infrastructures under hazards. He is interested in computational methods of structural reliability and uncertainty quantification, focusing on interpretable probabilistic analysis methods leveraging domain/problem-specific knowledge. He also develops probabilistic methods to analyze the regional impact of hazards by adapting theories/models from reliability, uncertainty quantification, and statistical physics.
Ph.D., Civil Engineering - Southwest Jiaotong University, China, 2015
B.S., Civil Engineering - Southwest Jiaotong University, China, 2010
Wang's research focuses on analyzing and understanding the reliability, risk, and resilience of structures and critical infrastructures under hazards. He is also interested in applying probabilistic methods to a broader field of science. Here are a few of the research areas Wang is currently working on:
Reliability and Uncertainty Quantification methods leveraging domain/problem-specific knowledge
The hypothesis is that an optimal (e.g., efficient, accurate, interpretable, scalable, general) computational method does not exist if a wide spectrum of problems is considered; the domain knowledge should be injected into the design of computational methods for a particular class of problems.
- [2310.00261] A physics and data co-driven surrogate modeling method for high-dimensional rare event simulation (arxiv.org)
- [2303.13023] Relaxation-based importance sampling for structural reliability analysis (arxiv.org)
- [2304.06252] Adaptive active subspace-based metamodeling for high-dimensional reliability analysis (arxiv.org)
- [2208.08991] Optimized Equivalent Linearization for Random Vibration (arxiv.org)
- [2310.10232] Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation (arxiv.org)
Probabilistic approaches for analyzing the regional-scale impact of natural hazards
Statistical physics and information-theoretic approaches in modeling and analyzing the collective behaviors of networked structures under natural hazards.
- [2310.17798] Maximum entropy-based modeling of community-level hazard responses for civil infrastructures (arxiv.org)
- Determination of Recovery Bridges through Post-earthquake Corridor Identification (PEER Annual Meeting)
Other research activities related to probabilistic methods
Stochastic dynamics of disasters.
CE 193 - Engineering Risk Analysis (Fall 2021, Spring 2023)
This course introduces the basic notions and methods of probability theory, statistics and decision theory through their application to civil engineering problems. The objective is to make the student aware of the many uncertainties that influence engineering decisions, and to provide tools for their modeling and analysis in the context of engineering risk assessment. We will start from the very beginning, but go quite far. No prior background in probability or statistics is needed if you are a graduate student. Emphasis is placed on probabilistic modeling and analysis of civil and environmental engineering problems, Bayesian statistics, risk analysis, and decision under uncertainty. For undergraduate students, this course builds on CE93 and provides a solid base in applied probability and Bayesian statistics as used by engineers, and introduces them to the important topics of risk analysis and decision making. For graduate students, in addition, this course provides a strong background for pursuing more advanced courses using non-deterministic methods, such as CE226, CE229, CE262, ME274, NE275 and many others.
CE 229 - Structural and System Reliability (Spring 2022)
To offer a comprehensive and in-depth coverage of modern methods for structural and system reliability assessment, analysis of uncertainty propagation, component/variable importance measures, and Bayesian inference for reliability analysis. Students will use computer codes to apply the concepts learned to example problems and a term project. Students completing this course will be able to read and understand the large and rapidly growing literature in the field of structural and system reliability and risk analysis. They will also understand the techniques employed in various reliability analysis codes. Methods discussed in this course have broad applicability and can be used in many disciplines where probabilistic analysis is needed.
Ph.D. Students
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Visiting Ph.D. Students
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