Tuesdays & Thursdays: 1:00 pm to 3:00 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 methods leveraging domain/problem-specific knowledge. He also develops probabilistic methods to analyze the regional impact of hazards by adapting theories 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:
Computational reliability and uncertainty quantification methods leveraging domain-specific knowledge of civil engineering
An optimal (efficient, accurate, interpretable, scalable, general) computational method does not exist when a wide spectrum of problems is considered. Domain knowledge should be injected into the design of computational methods for a particular class of problems. For many civil engineering applications, before pursuing an end-to-end machine learning approach, tuning simplified physical models with self-correcting mechanisms can often outperform many machine learning methods.
- [2506.10569] A composition of simplified physics-based model with neural operator for trajectory-level seismic response predictions of structural systems
- [2402.04582] Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification
- [2405.08006] Physics-based linear regression for high-dimensional forward uncertainty quantification (arxiv.org)
- [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)
- [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)
Statistical physics of civil systems under natural hazards
Complex systems often reveal simple and striking regularities when examined using appropriate methods. Statistical physics provides a formal apparatus to analyze system-level behavior of complex many-body systems emerging from component-level interactions. Statistical physics seeks a global understanding of macroscopic behaviors (phases) of a system by modeling the competition between unstructured entropy forces (fluctuations that destroy order) and structured organizing forces (interaction laws that create order). As urbanization continues to expand the scale and complexity of civil infrastructure systems, statistical physics will become foundational to civil engineering practice and education.
- [2602.16195] Phase Transitions in Collective Damage of Civil Structures under Natural Hazards
- [2603.29282] Social Amplification Dominates Collective Hazard Response
- [2403.11429v2] Long-range Ising model for regional-scale seismic risk analysis (arxiv.org)
- [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
- [2403.00283] Risk Twin: Real-time Risk Visualization and Control for Structural Systems
- [2404.07323] Surrogate modeling for probability distribution estimation:uniform or adaptive design?
- [2502.19549] Recorded Versus Synthetic Spectral-compatible Ground Motions: A Comparative Analysis of Structural Seismic Responses
- [2007.04136] The dynamics of entropy in the COVID-19 outbreaks (arxiv.org)
- (in Chinese) https://www.bilibili.com/video/BV1wY4y1y7aX/?spm_id_from=333.999.0.0
- Dynamics of severe accidents in the oil & gas energy sector derived from the authoritative ENergy-related severe accident database | PLOS ONE
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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