Plenary and Semi-Plenary Speakers

Abstract

Randomness associated with structural loads and disaster actions is a common observation in engineering practice. How to describe the random coincidence of load processes, and how to analyse the load combination effect of structures are still basic problems in modern structural design. In the lecture, a principle of load coincidence is first proposed, then the structural reliability under multi-loads and disaster actions is discussed in detail on the basis of the probability density evolution theory.

The principle of load coincidence indicates that the coincidence probability of random load samples is equal to the joint probability density at the realized samples. Using the principle, the extreme probability distribution of combined loads at the given service period of a structure can be quantitatively established. On the basis, the load effect combination of linear or nonlinear structures can be captured by using the probability density evolution method (PDEM). The comparison of linear and nonlinear load combination effects indicated that the probability distribution for the quantity/response of interest exhibits significant difference. Furthermore, the structural reliability under multi-loads and disaster actions can be analysed using the physical synthesis method proposed by author. Several typical engineering applications are presented in the end of the lecture, including a railway bridge under railway loads and earthquake, and a cooling tower of power plant under typhoon and earthquake. 

According to the above investigation, it is believed that the principle of structural load coincidence has laid a scientific foundation to solve the structural load effect combination problem. The study for the combined effects of multiple loads and disaster dynamic effects has opened up a new way for the simulation of life-cycle performance of structures. 

Bio

Jie Li is a Chair Professor in the Structural Engineering at Tongji University, the academician of the Chinese Academy of Sciences, and the honorary director of Shanghai Institute of Disaster Prevention and Relief. He received Ph.D. in Structural Engineering from Tongji University, China in 1988, and received an honorary doctorate in engineering science from Aalborg University, Denmark in 2013. 

Professor Li has been devoted to the theoretical research on structural engineering and stochastic mechanics for over 40 years. His contributions are distributed in the areas of stochastic dynamics, damage mechanics and engineering reliability. Prof. Li is the author of six monographs and more than 400 peer reviewed journal papers. In 2014, Professor Li was awarded the Alfred M. Freudenthal Medal by ASCE, owing to his academic achievements in the probability density evolution method and in the seismic reliability based design of large-scale infrastructure systems. In 2017-2022, Prof. Li served as the president of the International Association for Structural Safety and Reliability (IASSAR).

Abstract

The evolving complexity of engineering systems demands robust frameworks for monitoring, resilience assessment, and life-cycle management. Structural Health Monitoring (SHM) plays a pivotal role in ensuring the safety and functionality of such systems, yet conventional data-driven methods often struggle with the intricate dynamics and uncertainties inherent to these tasks. This talk overviews an integrative framework that advances learning for dynamics simulations, monitoring, and digital twinning tasks by focusing on the use of appropriate representations.

Central to this approach is the encoding of data into forms that effectively capture system behaviors, leveraging appropriate architectures, and hybridizing physics-based principles with machine learning. We discuss integration of structured representations and formal grammars to enable the characterization of dynamic behaviors and foster learning models that are interpretable, adaptable, and generalizable. This physics-enhanced paradigm enables efficient simulation of complex systems, whether in forward open-loop or closed-loop configurations, accommodating scenarios with or without integrated data. By exploring these representational strategies, this presentation outlines a roadmap toward resilient and self-aware systems.

Bio

Eleni Chatzi is a Full Professor and Chair of Structural Mechanics and Monitoring at the Institute of Structural Engineering of the Department of Civil, Environmental and Geomatic Engineering of ETH Zürich. She currently serves as the President of the European Academy of Wind Energy (EAWE). Her research interests include the fields of Structural Health Monitoring (SHM), hybrid modelling for digital twinning, and data-driven decision support for engineered systems. Her work in the domain of self-aware infrastructure was recognized with a 2016 ERC Starting Grant award, the 2020 ASCE Huber Research prize, the 2020 EASD Junior Research Prize in the area of Computational Structural Dynamics, and the 2024 SHM Person of the Year award.

Abstract

This lecture will present an overview of a digital-twin framework developed to inform and guide the development process of complex, large-scale, multidisciplinary systems throughout their life cycle, while maximizing resources such as schedule and cost. This framework relies on simulation, analysis and uncertainty quantification capabilities that have successfully been applied to space missions such as the James Webb Space Telescope and Mars Sample Return. Specifically, this framework enables: 1) orders-of-magnitude reductions in computational cost through a multi-fidelity simulation approach and appropriate sampling strategies; 2) identification of the main risk drivers through efficient and comprehensive global sensitivity analyses based on optimal transport theory and machine learning, especially powerful on black-box models; 3) effective test planning informed by such sensitivity analyses for uncertainty reduction and model validation with limited resources; 4) data assimilation through a Bayesian approach that reduces system overdesign and provides models with predictive capabilities; 5) probabilistic risk assessment of rare events in the context of mission safety and success. Overall, through this digital-twin framework, system knowledge is increased due to a systematic, rigorous approach to identifying and reducing important uncertainties, bringing noticeable improvements in the decision-making process and ultimately mission success.

Bio

Dr. Giuseppe Cataldo is Assistant Chief for Technology at NASA’s Goddard Space Flight Center, where he is responsible for planning, implementing, directing and evaluating a comprehensive program designed to produce advanced mechanical systems technology for future use in space flight. He is also the NASA Uncertainty Quantification (UQ) Community of Practice co-lead and directs research activities in modeling, simulation, UQ and artificial intelligence across the agency in collaboration with many external partners. Previously, he led the NASA planetary protection efforts for the last mission of the Mars Sample Return program and was the chief engineer of NASA’s EXCLAIM mission and the near-infrared camera NASA contributed to the PRIME telescope. He also worked on multidisciplinary design optimization and UQ for the James Webb Space Telescope and on a variety of NASA missions and technology development projects.

Giuseppe joined NASA in 2009 fully sponsored by the European Space Agency as one of the two European students selected for the NASA Academy, NASA’s premiere leadership program for talented students. He earned his PhD at the Massachusetts Institute of Technology and master’s degrees from the Polytechnic Institutes of Milan and Turin (Italy) and ISAE-SUPAERO (France). He is the recipient of numerous awards, an Associate Fellow of the American Institute of Aeronautics and Astronautics, member of several technical committees and author of more than 70 journal and conference papers. He speaks six languages, plays the violin and piano, loves swimming, skiing, mentoring young students, and helping the poor with his family and friends.

Abstract

Stochastic optimal control of structures presents a compelling approach to enhancing performance and fostering robust design in engineering structures and infrastructure systems. This methodology harnesses control and optimization techniques, accounting for stochastic dynamics inherent in nonstationary and non-Gaussian excitations resulted from natural hazards such as earthquakes and typhoons, with a particular emphasis on ensuring structural safety grounded in reliability metrics. In this lecture, the historical trajectory of stochastic optimal control of structures is first sorted, and the basic formulas of the physically-based stochastic optimal (PSO) control are introduced. Subsequently, the generalized optimal control policy, encompassing the definition of control laws, the design of control parameters, the optimization of device layouts, and the solving of global reliability, for the PSO control is presented. Numerical examples of reliability-based optimal structural control in active, semiactive, passive modalities showcase the advantages of the PSO control in bolstering structural safety and optimizing design expenses.

Bio

Dr Yongbo Peng has been devoted in the area of uncertainty propagation and quantification, structural reliability and resilience analysis, stochastic control and optimization, and adaptive materials and structures. He obtained a PhD degree in Structural Engineering from Tongji University in 2009 after fulfilling a two-year joint PhD program in University of Southern California, USA, and joined Tongji University since then. He became a full Professor in 2018. Currently, he serves as a council member and deputy secretary-general of the Chinese Society for Vibration Engineering, a council member of the Shanghai Society of Theoretical and Applied Mechanics, a member of Joint Committee on Structural Safety (JCSS), and editorial board members of the Journal of Vibration Engineering and Journal of Architecture and Civil Engineering. Dr Peng has published two monographs, and over 140 journal papers. He won the Youth Science and Technology Award of the Chinese Society for Vibration Engineering in 2016, and was named among the ‘Top 2% World Ranking Scientists’ by Stanford/Elsevier in succession.

Abstract

In recent years, Scientific Machine Learning (SciML) has received widespread attention across the scientific landscape. Operator learning is a particular area of SciML that shows promise for improving computational modeling of complex systems and structures under uncertainty. This talk discusses recent advances in operator learning for uncertain systems, with emphasis on neural operators and their comparison with classical surrogate modeling methods such as polynomial chaos expansions (PCEs) and recently developed PCEs for operator learning. We then introduce a new class of spectral stochastic neural operators, affectionately known as Neural Chaos, that combine concepts of spectral stochastic methods (i.e. PCE) with new neural operator architectures. The Neural Chaos model applies a new dictionary learning approach to learn a set of basis functions that are orthogonal with respect to the empirical distribution of the input random variables – thereby building the neural operator as a spectral stochastic expansion. The approach is capable of learning compact (sparse) surrogate models for the complete spatio-temporal solution to highly nonlinear problems with arbitrarily distributed and dependent random inputs. We then apply the Neural Chaos model to structural applications of varying complexity – starting with a simple Euler-Bernoulli beam with random field stiffness and advancing to prediction of the complete time-history response of a six-story shear building subject to stochastic seismic base excitation.  

Bio

Michael D. Shields is an Associate Professor in the Dept. of Civil & Systems Engineering at Johns Hopkins University where he is also the Director of the Center on High-Throughput Materials Discovery for Extremes (HT-MAX), holds a secondary appointment in the Dept. of Materials Science and Engineering, and is a fellow of the Hopkins Extreme Materials Institute. Prof. Shields conducts methodological research in uncertainty quantification (UQ) and probabilistic modeling for problems in structural mechanics, materials science, and physics with applications ranging from multi-scale material modeling to assessing the reliability and safety of large-scale structures. He received his Ph.D. in Civil Engineering and Engineering Mechanics from Columbia University in 2010, after which he was employed as a Research Engineer in applied computational mechanics at Weidlinger Associates, Inc. He joined the faculty at Johns Hopkins in 2013. For his work in UQ, Prof. Shields has been awarded the ONR Young Investigator Award, the NSF CAREER Award, the DOE Early Career Award, and the Johns Hopkins University Catalyst Award. Prof. Shields and his group also develop the open-source UQpy (Uncertainty Quantification with Python) software, which is a general toolbox and development environment for UQ in computational, mathematical, and physical systems.

Abstract

The combination of stochastic mechanics and fractional calculus creates a powerful approach for modeling complex, random systems that exhibit memory effects, non-local interactions, and anomalous diffusion. Stochastic mechanics typically focuses on systems where randomness governs the evolution of particles or fields. Fractional calculus, on the other hand, generalizes differentiation and integration to non-integer orders, enabling the description of systems with non-local or memory-dependent dynamics. 

In this talk the probabilistic characterization of a nonlinear system enforced by white noise in terms of Complex fractional moments is presented. The main advantage in using such quantities, instead of the integer moments, relies on the fact that, through the Complex fractional moments, the probability density function is restituted in the whole domain. In fact, the inverse Mellin transform returns the probability density function by performing integration along the imaginary axis of the Mellin transform, while the real part remains fixed. This ensures that the probability density function is recovered in the whole range with exception of the value in zero, in which singularities appear. It is shown that by using Mellin transform theorem and related concepts, the solution of the Fokker Planck (FPK) equation is obtained in a very easy way by solving a set of linear differential equations. Specifically, the probability density function response of nonlinear systems may be written in discretized form in terms of complex fractional moment not requiring a closure scheme. Results are compared with those of Monte Carlo Simulation showing the robustness of the solution pursued in terms of Complex fractional moments. Furthermore, recent developments have extended the aforementioned method for characterizing probability density functions that are not necessarily symmetric but can take on any form. Finally some perspective on stochastic process which would be at the same time Gaussian, with stationary increments and self-similar, but not necessarily with Hurst index equal to ½, say Fractional Brownian Motion, will be introduced together with the pertinent Fokker-Planck-Kolmogorov equation for drift-free dynamical systems.

Bio

Antonina Pirrotta is a Full Professor in Structural Mechanics at the University of Palermo, Italy. She, graduated in Civil Engineering from Palermo University in 1987. Firstly for 6 years she worked as freelance structural engineer, then has done PhD in Structural Engineering in 1996 and as Post doctoral studies in 1998. In 2000, she became a Researcher, in 2001 Associate Professor and in 2016 Full Professor in Engineering department at the University of Palermo, Italy.

• HEAD of PhD School of University of Palermo from 2023. 

• HEAD of CIDIS from 2021. 

• Board of Governors of ASCE (EMI) 2021-2024 (exceptionally renewed every year). 

Her achievements resulted in many scientific recognitions, which include the appointment as Chair of the Stochastic Mechanics Group of AIMETA and of IDEA Innovative Dynamics Experiments Association (2022), several fellowships, memberships on scientific committees of various key international conferences, a membership on the Advisory Board of the MSCA COFUND Doctoral Programme at the University of Innsbruck, and many invitations as plenary speaker at several international conferences. 

Prof. Pirrotta has organized and co-organized an exceptionally large number of international conferences, the last EMI2023International Conference organized at University of Palermo and for what she received the appreciation award, minisymposia, and summer schools in Italy and all over the world. She is an Associate Editor of the Journal of Engineering Mechanics (ASCE), (first professor with Italian affiliation) and of Meccanica, which are two absolute top journals in the field. Additionally, she is a member of the Editorial Board of various international scientific journals. She was a Visiting Professor at many universities all over the world such as Tongji University, Columbia University, Liverpool University, TU Wien, etc. With over 180 published papers, she is a leading researcher in the field of structural mechanics  and collaborates with renowned institutions worldwide. 

Prof. Antonina Pirrotta 's contributions and leadership have made her a notable figure in Structural Mechanics and a role model for women in science.

Abstract

Accurate prediction of traffic loads is critical for ensuring the safety and long-term serviceability of highway bridges. Traditional probabilistic models rely on data-driven statistical inference. However, these models are primarily descriptive and often lack the explanatory framework needed for scientific understanding and reliable predictions, particularly beyond observed data. This paper presents recent advances in probabilistic modelling that adopt a phenomenological, generative approach to highway bridge traffic loading.

First, a hierarchical Bayesian model is introduced to use traffic load data from multiple bridges within a network to inform the statistical modelling. By capturing shared structural and traffic characteristics, this approach improves predictive accuracy, reduces uncertainty, and reveals latent correlation structures absent in conventional models.

Second, a Bayesian model is proposed to incorporate engineering and physical constraints into probabilistic traffic load models. By imposing realistic constraints on extreme loads, this approach prevents physically impossible predictions and reduces uncertainty in lifetime load estimation.

These developments demonstrate how generative modelling can offer deeper insights into traffic loading processes while improving predictive accuracy. This shift leads to more robust, interpretable probabilistic models, improving bridge design, assessment, and network-wide risk management.

Abstract

Numerical tools to approximate the solution of (sets of) differential equations have become indispensable in the design of engineering components from the micro-scale to complete structures. Thanks to these tools, an engineer is now able to design, test and optimize designs long before a first prototype is built. However, despite the highly detailed numerical predictions that can be obtained, the results of these calculations often show a non-negligible discrepancy with the actual physical behavior of the structure. At the core of this discrepancy lies uncertainty in the description of the model physics, as well as the governing parameters.

Uncertainties are for instance commonly encountered in the context of structural dynamics, where for instance the effect natural phenomena such as earthquakes or wind loads on structures has to be considered. Indeed, due to the sheer complexity of the underlying physics, the corresponding dynamical loads that act on the system often cannot be described in a crisp way. Stochastic processes provide a rigorous framework to deal with the uncertainties and space/time correlations of uncertain loads by resorting to the well-documented framework of probability theory. However, in practice, the analyst is often confronted with limited, incomplete or conflicting sources of data (i.e., epistemic uncertainty) due to limitations in time, budget and/or measurement resolution. In this case, there might be simply not enough information to warrant the computational cost of performing extremely detailed reliability analyses with very small failure probabilities. This begs the question: where should we invest our resources – in making our reliability analyses more accurate or in making them more robust?

In this talk, I will present some philosophical, theoretical and very practical numerical calculation schemes that are aimed at contributing to answering this question. The presentation will draw from recent work we performed in the field of reliability sensitivity and imprecise reliability analysis, highlighting recent advances in surrogate modelling (functional dimensionality reduction, Bayesian active learning, polynomial chaos expansions), decoupling schemes (operator norm theory) and efficient sampling schemes (multi-domain line sampling, directional importance sampling).  

Bio

Matthias Faes became a full Professor in Reliability Engineering at TU Dortmund at the age of 30, since February 2022. Before, he was a post-doctoral fellow of the Research Foundation Flanders (FWO) working at the Department of Mechanical Engineering of KU Leuven and was also affiliated to the Institute for Risk and Reliability at the University of Hannover as an Alexander von Humboldt Fellow. He graduated summa cum laude as Master of Science in Engineering Technology in 2013 and obtained his PhD in Engineering Technology from KU Leuven in 2017. Since then, he works on theoretical and numerical methods to perform efficient uncertainty quantification and reliability analysis under scarce data and information, including inverse and data-driven methods, surrogate modeling schemes, stochastic fields and imprecise probabilities.

Matthias Faes is a Laureate of the 2017 PhD award of the Belgian National Committee for Applied and Theoretical Mechanics, winner of the 2017 ECCOMAS European PhD award for best PhD thesis in 2017 on computational methods in applied sciences and engineering in Europe, winner of the 2019 ISIPTA - IJAR Young Researcher Award for outstanding contributions to research on imprecise probabilities and the 2023 EASD Junior Research Prize for his contribution to the development of methodologies for structural dynamics, among other awards. He is Associate Editor at Mechanical Systems and Signal Processing, and Associate Managing Editor of the ASCE-ASME Journal of Risk and Uncertainty in Engineering system parts A and B, among other journals. Matthias Faes is author of more than 90 journal papers and more than 90 conference contributions and he has a Google Scholar H-index of 26 (2400+ citations) since 2016. 

Abstract

The ultimate objective of community resilience modeling is to enable decision-makers the ability to quantitatively examine “what if” scenarios, or alterative policies or actions, to improve their resilience before a hazard strikes. Models can no longer focus on a single building, network, or the economy in isolation and must be comprehensive representing a complex endeavor that yields both risks and rewards for the analyst and community alike. The ability to model a community necessitates combining models from different disciplines including their interfaces, the propagation of uncertainty, and ultimately the measurement of resilience metrics across physical systems, households, social institutions, and the economy.  This presentation will begin with a brief history of a US-based federally-funded ten-year 14-university effort to develop an open-source platform for researchers to study community resilience. Then, moving on to systematically examine the facets of resilience within a community, beginning with the need for resilient buildings, maintaining population and economic stability, and maintaining and restoring physical services such as electric power in the context of fully integrated community models to inform policy changes. A focus on wind, earthquake, and climate adaptation examples will be presented to illustrate the concepts, closing by elucidating remaining gaps in community resilience modeling worldwide.  

Bio

Dr. John W. van de Lindt is the Harold H. Short Chaired Professor in the Department of Civil and Environmental Engineering at Colorado State University. Over the last several decades van de Lindt’s research program has focused on performance-based engineering and test bed applications of buildings and other systems for hurricanes, tsunamis, earthquakes, tornadoes and floods.  He has led more than 50 projects focused on risk, reliability, and resilience over the last 25 years. Professor van de Lindt is the Co-director of the National Institute of Standards and Technology-funded Center of Excellence (COE) for Risk-Based Community Resilience Planning headquartered at Colorado State University in its tenth year.  A major portion of the COE was to develop a computational platform IN-CORE to enable communities to measure their resilience to natural hazards.  He serves as the Past Chair of the Executive Committee for the American Society of Civil Engineer’s (ASCE) Infrastructure Resilience Division and has published more than 500 technical articles and reports including more than 250 journal publications.  He currently serves on a number of journal editorial boards worldwide including as the Editor-in-Chief for the ASCE Journal of Structural Engineering.

Abstract

The P–Δ effect is well known, in which an overturning moment emerges due to geometric nonlinearity when a gravity-loaded structure undergoes a large deformation. Similarly, when a column undergoes a large horizontal displacement, a torsional torque emerges to preserve the state of equilibrium. The speaker named this effect “Q–Δ effect.” Although the effect has not been considered in structural design, he has shown that periodical nonlinear restoring torque of the Q–Δ effect can produce a non-negligible torsional response in a structure during earthquakes owing to its internal resonance, named “Q–Δ resonance.” This talk will first explain the theory of the Q–Δ effect and the mechanism of the Q–Δ resonance. Then, he will present the results of shaking-table experiments using a reduced specimen and finite element analysis of a full-scale model of high-rise building under long period ground motions. This resonance phenomenon is a blind spot in seismic design, and given the mechanism of its occurrence, it may need to be considered in wind-resistant design as well. Researchers and engineers are expected to listen to this talk to widely promote proactive countermeasures.

Bio 

Masayuki Kohiyama is Professor of Keio University, Japan since 2016. He obtained master and doctoral degrees from Kyoto University. He pursued research at Kajima Corporation (1995–1999), Earthquake Disaster Mitigation Research Center, RIKEN (1999–2001), and Institute of Industrial Science, The University of Tokyo (2001–2004). From 2004 to 2005, he conducted research at Stanford University as JSPS Overseas Research Fellow and Visiting Associate Professor. In 2005 he joined Keio University as Assistant Professor and has been at the university to the present. He was the Editor in Chief of the Journal of Japan Association for Earthquake Engineering (JAEE). He also served on the Board of Directors of JAEE and will serve as an auditor of the Board of Directors from June, 2025. He has served as secretary and chair of numerous committees on reliability engineering, applied mechanics, and building vibration control at the Architectural Institute of Japan. His research is focused on performance-based design of building structures, disaster reduction systems including structural health monitoring, and disaster-resilient housing and communities.

Abstract

Within the context of rapid environmental changes, increasing demands, and limited resources, it is necessary to rethink how infrastructure is planned, executed, and managed. This task first requires a new understanding of infrastructure as a dynamic system. In other words, it should be modeled as a process that evolves continuously, can change and adjust to new circumstances as they materialize, and is interconnected with other systems and the socioeconomic context. Secondly, given the continuous change of infrastructure and its surrounding environment, there is also a need to change the focus of traditional performance evaluations toward strategies to manage the uncertain future better as it unfolds. This talk discusses the importance of designing optimal infrastructure management policies to efficiently respond to new and unplanned events as they unravel throughout their lifetime to keep them safe and operational. It presents a discussion on both existing policy optimization strategies and future challenges, including flexible designs and adaptation.

Bio

Professor Sánchez-Silva’s area of expertise is risk analysis and stochastic modeling to support the decision-making process in engineering under uncertain conditions. He works extensively on Probabilistic Risk Analysis (PRA), stochastic modeling, and approximate measures to estimate potential risks for infrastructure, network systems, and industrial facilities.  He has been involved in projects where socioeconomic and environmental contexts play a significant role and, therefore, traditional risk modeling and engineering methods can only be applied partially. Some of his current areas of research include the design and management of infrastructure systems (structures and lifelines); stochastic modeling of deteriorating systems and structures; flexibility, adaptability, and resilience of infrastructure; Sustainable infrastructure management and decision-making for resource allocation and cost-effectiveness of investments in the design and operation of various types of facilities.