Disaster Resilience of Critical Infrastructure

Bilal M. Ayyub, PhD, PE
Director of the Center for Technology and Systems Management
University of Maryland College Park, USA


Natural disasters in 2011 alone resulted in $366 billion (2011 US$) in direct damages and 29,782 fatalities worldwide. Storms and floods accounted for up to 70% of the 302 natural disasters worldwide, with earthquakes producing the greatest number of fatalities. Managing these risks rationally requires an appropriate definition of resilience and associated metrics. This presentation provides a resilience definition that meets a set of requirements with clear relationships to reliability and risk as key relevant metrics. Such metrics provide a sound basis for the development of effective decision-making tools for multihazard environments. The presentation also examines recovery, with its classifications based on level, spatial, and temporal considerations. Three case studies are developed and used to gain insights to help define recovery profiles.

Short Bio

Dr. Ayyub is a University of Maryland Professor of Civil and Environmental Engineering, Professor of Reliability Engineering, and Professor of Applied Mathematics and Scientific Computation. He is also a chair professor at Tongji University, Shanghai, China. Dr. Ayyub’s main research interests are risk, uncertainty, decisions, and systems applied to civil, mechanical, infrastructure, energy, defense and maritime fields. Dr. Ayyub is a fellow of the American Society of Civil Engineers (ASCE), American Society of Mechanical Engineers (ASME), Society for Risk Analysis and Society of Naval Architects and Marine Engineers. Dr. Ayyub completed several projects for governmental and private entities, such as the National Science Foundation, Office of Naval Research, Air Force Office of Scientific Research, Army Corps of Engineers, Department of Homeland Security, Nuclear Regulatory Commission, ASME, Hartford, Chevron, Bechtel, etc. Dr. Ayyub is the recipient of several awards from ASCE, ASNE, ASME, NAFIPS, the Department of the Army, and the Governor of the State of Maryland. Dr. Ayyub is the author and co-author of more than 600 publications including 8 textbooks and 14 edited books. He is also the Editor-in-Chief of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems.

Reliability Assessment with Limited and Vague Information

Michael Beer
Institute for Risk and Reliability, Leibniz Universität Hannover, Hanover, Germany
Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK
International Joint Research Center for Engineering Reliability and Stochastic Mechanics,
Tongji University, Shanghai, China


Engineering systems and structures are characterized by a rapid growth in scale and complexity. The amount of information needed to model these systems and structures with their complexity is, thus, growing as well. In contrast to this increasing need for information the available information remains almost at the same level. Hence, with increasing scale and complexity the gap between required and available information is growing quickly, so that uncertainties and risks are involved in our models and analyses to a greater extent than ever before. In particular, epistemic uncertainties become involved to a significant extent. There is a clear consensus that these epistemic uncertainties need to be taken into account for a realistic assessment of the performance and reliability of our structures and systems. However, there is no clearly defined procedure to master this challenge. The second challenge is to analyze large structures and systems under consideration of uncertainties efficiently. This keynote lecture addresses these two challenges. An overview is given on approaches to deal with epistemic uncertainties, with focus on imprecise probabilities and in the context of structural and system reliability assessment. Numerically efficient concepts for these analyses are discussed. Engineering examples are presented to demonstrate the capabilities of the approaches and concepts.

Short Bio

Michael Beer is Professor and Head of the Institute for Risk and Reliability, Leibniz Universität Hannover, Germany, since 2015. He is also part time Professor at the University of Liverpool and at Tongji University, Shanghai, China. He obtained a doctoral degree from Technische Universität Dresden and pursued post-doctoral research at Rice University. From 2007 to 2011 Dr. Beer worked as an Assistant Professor at National University of Singapore. In 2011 he joined the University of Liverpool as Chair in Uncertainty in Engineering and Founding Director of the Institute for Risk and Uncertainty and established a large Doctoral Training Center on Quantification and Management of Risk & Uncertainty. Dr. Beer’s research is focused on uncertainty quantification in engineering with emphasis on imprecise probabilities. Dr. Beer is Editor in Chief (jointly) of the Encyclopedia of Earthquake Engineering, Associate Editor of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Associate Editor of the International Journal of Reliability and Safety, and Member of twelve Editorial Boards including Probabilistic Engineering Mechanics, Computers & Structures, Structural Safety, and Mechanical Systems and Signal Processing. He has won several awards including the CADLM PRIZE 2007 – Intelligent Optimal Design. His publications include a book, several monographs and a large number of journal and conference papers. He is a Fellow of the Alexander von Humboldt-Foundation and Member of ASCE (EMI), ASME, IACM, ESRA, EASD, C(PS)2 of the Bernoulli Society and GACM.

Advanced Tools for Uncertainty Modeling and
Propagation in Engineering Dynamics

Ioannis A. Kougioumtzoglou
Dept. of Civil Engineering & Engineering Mechanics
Columbia University, USA


Since the dawn of the field of stochastic engineering dynamics, the objective of uncertainty modeling has led to the development of methodologies for analyzing measured/available data, and for estimating pertinent stochastic models, i.e., quantifying the underlying stochastic process/field statistics. Current challenges, however, relate to the fact that, most often, measured/available data are limited and/or incomplete, while at the same time their statistics and/or frequency content exhibit a time/space-varying behavior. Clearly, standard/traditional tools, such as Fourier analysis, are not equipped to address the above cases. In the first part of the talk, exploiting the “sparseness” of the data and relying on appropriate -norm () minimization algorithms in conjunction with (adaptive) wavelet bases, recently developed spectral analysis methodologies subject to vastly sparse/incomplete data will be presented. Further, ever-increasing available computational capabilities, development of potent signal processing tools, as well as advanced experimental setups have contributed to a highly sophisticated modeling of engineering systems and related excitations. As a result, the form of the governing equations has become highly complex from a mathematics perspective. Examples include complex nonlinearities, joint time/space-frequency representations, as well as generalized/fractional calculus. In many cases even the deterministic solution of such equations is an open issue and an active research topic. Clearly, solving the stochastic counterparts of these equations becomes orders of magnitude more challenging. In the second part of the talk, based on the machinery of functional integrals, a recently developed methodology for efficient response determination of stochastic dynamical systems will be presented. Significant novelties and advantages include: (i) The methodology can account for non-Gaussian, non-white, and non-stationary processes, complex nonlinearity forms, as well as unconventional modeling such as generalized/fractional calculus; (ii) The original stochastic problem transforms into a deterministic problem, which may be solved via standard numerical approaches such as finite elements; (iii) The “local” feature of the methodology can be exploited in conjunction with highly sparse representations for the system response to drastically decrease the involved computational cost.

Short Bio

Dr. Ioannis A. Kougioumtzoglou received his five-year Diploma in Civil Engineering from the N.T.U.A. in Greece (2007), and his M.Sc. (2009) and Ph.D. (2011) degrees from the Dept. of Civil and Env/ntal Engineering at Rice University, USA. He is currently an Assistant Professor at the Dept. of Civil Engineering & Engineering Mechanics at Columbia University, USA. Dr. Kougioumtzoglou’s primary research interests focus on the general area of mathematical modeling and dynamics of complex structural/mechanical systems with emphasis on uncertainty quantification aspects. Specifically, the development of numerical and/or analytical techniques for nonlinear stochastic dynamics, computational stochastic mechanics, and signal processing applications constitutes one of the main research themes. Dr. Kougioumtzoglou has published more than 70 technical papers in peer-reviewed International Journals and Conference Proceedings. He is the 2014 European Association of Structural Dynamics (EASD) Junior Research Prize recipient “… for his innovative influence on the field of nonlinear stochastic dynamics”, has co-chaired the 13th International Probabilistic Workshop (IPW 2015), and is a co-Editor of the Encyclopedia of Earthquake Engineering (Springer). Dr. Kougioumtzoglou is an Editorial Board member for the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, and has served as a Guest Editor for several Special Issues in International Journals. He is a member of the Engineering Mechanics Institute (EMI) and the American Society of Civil Engineers (ASCE), a Registered (Licensed/Chartered) Professional Civil Engineer in Greece, and a Fellow of the Higher Education Academy (FHEA) in the UK.

Reducing uncertainty through closer investigation of fundamentals

Robert E. Melchers
Centre for Infrastructure Performance and Reliability
The University of Newcastle, Australia


Too often phenomena are labelled as uncertain or representable only by probabilistic description(s) when a little more careful analysis of the fundamental issues involved would be very helpful. This includes understanding fully the scientific principles involved and the conditions under which they have validity, thinking through parallel situations or cases and, perhaps, conducting new, well formulated, experimental work. The aim should be to gain better understanding of the phenomena of interest. This can reduce the degree to which reliance must be placed on probabilistic representations and can do much to produce better overall models. In turn, this can lead to more informed decisions, such as for infrastructure safety and remaining life. None of this is to suggest that probabilistic modelling is not appropriate, but only that it should be used where no other avenues for representation are available. The theme is illustrated with examples drawn from the emerging field of modelling of structural deterioration resulting from exposure to natural phenomena such as marine environments. The first example is that of the chloride concentration threshold, for many years considered crucial to formulating the likelihood of the initiation of corrosion of reinforcement in concrete structures. The variability in the reported chloride concentrations is extremely high, and many efforts exist to represent this as a random variable. But even a cursory overview suggests that such high variability simply suggests there is a lack of knowledge of the critical processes involved, or a lack of consideration of other variables. Recent research outcomes completely change the understanding for the processes involved. This opens up new ways of looking at the problem and the previously unrecognized variables involved and highlights those variabilities that should be examined. The second example is that of unprotected steel objects such as pipes buried in soils. There are very many potential variables although after more than 60 years of effort by many researchers, no sensible correlations with expected corrosion exist. In the analysis presented it is shown using application of fundamental principles, that many variables are negligible and some others that are important have not had sufficient attention in the past.

Short Bio

Robert E Melchers is Professor of Civil Engineering at The University of Newcastle, Australia. Until recently he was concurrently an Australian Research Council DORA Research Fellow [2014-16] and prior to that Australian Research Council Professorial Fellow [2004-8 and 2009-13].

He holds a BE and MEngSc from Monash University and a PhD from the University of Cambridge, UK. He is a Fellow of the Australian Academy of Technological Sciences and Engineering and an Honorary Fellow of The Institution of Engineers Australia.

His most recent awards are the 2009 ACA Corrosion Medal, the 2012 Jin S Chung Award (International Society of Offshore and Polar Engineers) and the 2013 John Connell Gold Medal (The Institution of Engineers, Australia). He was the 2014 Eminent Speaker for the College of Structural Engineers, The Institution of Engineers, Australia.

Drilling and stick-slip oscillations

Rubens Sampaio, Ph. D.
Professor of PUC-Rio, Brazil


Drilling is a complex process. The drillstring may have more than 10km and are very flexible. The bottom hole assembly, on the bottom of the drillstring, is short but is under heavy compression and subject to a very complex load. Besides the complexity, the drilling process and structures involved are very uncertain. Specially in the interaction between soil that is being drilled and the drill. The main type of uncertainty is in the stick-slip dynamics that must be mastered in order to control the drilling process and optimize the rate of penetration of the drill. The talk will discuss the drilling process, how the modeling process developed in Brazil, its uncertainties, its stability and reliability, and will concentrate in the stick-slip dynamics and how to model it.

Short Bio

Prof. Sampaio is a PUC-Rio Professor of Mechanics. He is a Ph. D. in Mathematics from Carnegie-Mellon University, a former President of the Brazilian Society for Applied and Computational Mathematics (SBMAC), former Editor of the Journal of Brazilian Association of Mechanical Sciences (ABCM) and of Notas de Matematica Aplicada of SBMAC.

Prof. Sampaio main interests are Continuum Mechanics, Dinamics, Stochastic Mechanics, Scientific Computation, and Applied Mathematics.

Prof. Sampaio is an Alexander von Humboldt Fellow, a Chevalier d’Ordre des Palmès Académique de la République Française, a Comendador da Ordem Nacional de Mérito Científico do Brazil. He has cooperated for more than 40 years with the USA, France, Germany, Portugal, Spain, Chile, Argentine. He is Pesquisador 1A of CNPq, Cientista do Nosso Estado da Faperj for the last 15 years. His main achievement however are the students he helped to train: 25 doctors, 32 masters, and several pos-docs. He is the Secretary of the ABCM-Committee of Stochastic Modeling and Uncertainty Quantification. The publications of Prof. Sampaio can be viewed in ResearchGate.

Recent developments in surrogate modelling for uncertainty quantification

Prof. Bruno Sudret
Chair of Risk, Safety and Uncertainty Quantification
ETH Zurich, Switzerland


Nowadays computational models are used in virtually all fields of applied sciences and engineering to predict the behavior of complex natural or man-made systems. Those computational models (a.k.a simulators) allows the analyst to assess the performance of systems in silico, e.g. to optimize news design or operating and maintenance policies. Such models usually feature dozens of parameters and are expensive to run, even when taking full advantage of the available computer power. In parallel, the more complex the system, the more uncertainty in its governing parameters, environmental and operating conditions. In this respect, uncertainty quantification techniques used to solve reliability, sensitivity or optimal design problems usually require thousands to millions of model runs with different values of the model input parameters, which is not affordable with high-fidelity, costly simulators. This has led to the development of surrogate models in the last decade.

Roughly speaking, a surrogate model is an accurate approximation of the simulator built from a limited number of runs at selected values (the experimental design) and some learning algorithm. In this lecture, an overview of the most efficient surrogate modelling techniques will be given, namely polynomial chaos expansions (including sparse approaches for high-dimensional problems), Kriging (a.k.a. Gaussian process modelling), their combination into PC-Kriging, as well as low-rank tensor approximations. Recent extensions to dynamics problems will be addressed. Various applications in model calibration (Bayesian inversion), sensitivity and reliability analysis in civil and mechanical engineering will be presented as an illustration.

Short Bio

Bruno Sudret is a professor of Risk, Safety and Uncertainty quantification at ETH Zurich since 2012.

Dr. Sudret received a master’s of science from the Ecole Polytechnique (France) in 1993. He then obtained a master’s degree and a Ph.D in civil engineering from the Ecole Nationale des Ponts et Chaussées (France) in 1996 and 1999, respectively. Dr. Sudret has been working in probabilistic engineering mechanics and uncertainty quantification for engineering systems since 2000: first as a post-doctoral fellow at the University of Berkeley (California), then as a researcher at EDF R&D (the French world leader in nuclear power generation) where he was the head of a group specialized in probabilistic engineering mechanics (2001-2008). From 2008 to 2011 he has worked as the Director of Research and Strategy at Phimeca Engineering (France).

He is the author and co-author of more than 250 publications in journal and conference proceedings. He currently serves in the editorial board of Reliability Engineering and Systems Safety, Probabilistic Engineering Mechanics, Sustainable and Resilient Infrastructure and the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. He promotes the dissemination of uncertainty quantification techniques through the development of the software UQLab.

Risk Management Framework Based on Monitoring
and Assessment of Infrastructures

Nasim Uddin
Dept. of Civil, Construction, and Environmental Engineering
University of Alabama at Birmingham, USA


To address the challenges of increasing demands on our infrastructure system and reduced revenues to invest in the infrastructure, preserving and enhancing the existing infrastructure in a cost-efficient way is of primary importance. Today, the bulk of funding goes to preserving and enhancing existing facilities. This will become even more the case as existing infrastructures continue to age, natural hazards become more frequent due to climate changes, and the probability of large earthquakes in dense urban areas continue to increase as forecast by seismologists. Most regions in the U.S., for example, are susceptible to one or more natural hazards that can severely damage critical infrastructures and place additional burdens on already strained systems and the communities they serve. Probability based design are based on the assumption of stationarity, i.e., the statistical properties of variables in future will be similar to past time periods. This assumption has been challenged now due to climate, weather and their extremes in future are expected to be different than in the past. Since the uncertainty associated with extreme events is not completely quantifiable, full reliance on a probabilistic risk-based approach has the potential to be questionable and, therefore, new approaches along with considerable engineering judgment will be essential. This talk will propose a risk management framework to ensure that an infrastructure system can be updated over time as conditions change, and will demonstrate with application to a transportation infrastructure. Such a framework would include a monitoring program to evaluate system performance over time and the flexibility needed to make changes. A climate change risk management program can be integrated into strategic and systematic process of operating, maintaining, upgrading and expanding infrastructure effectively throughout their life cycle. Data collected over the life cycle can evaluate the system’s performance under changing conditions, and also inform other stakeholders who are evaluating decisions for similar infrastructure. In the event that the risks and potential costs of a project is too uncertain, and it is not possible to reduce the uncertainty in the timeframe in which action should be taken, the framework will depend on the use of low-regret, adaptive strategies based on the innovative infrastructure monitoring and assessment method in order to make the infrastructure more robust and resilient against future climate and weather extremes; and also to seek alternatives that do well across a range of possible future conditions.

Short Bio

Prof. Uddin, PhD, PE, F.ASCE is a Professor of structural engineering at University of Alabama at Birmingham, USA and a leading researcher in resilient and sustainable infrastructure with the implementation of hazard resistant resilient structures against earthquake, blast, flood and hurricane loading. He completed over 50 research projects with National Science Foundation (NSF) and Department of Transportation (DOT) cumulative research funding exceeding $8.0 million, and is directing research for UAB Sustainable Smart City Research Center. He is currently leading NSF funded international research with European partners on next-generation Bridge Weigh-In-Motion (BWIM) Systems to effectively manage the nation’s aging bridge infrastructure, and just completed a NSF project on developing design methods for high-performance infrastructure against flood, blast, and hurricane. He is serving as the Chair of ASCE Infrastructure Resilience Division (IRD) Award, Publication, and Communication Committees, and served as the Chair of ASCE Council for Disaster Risk Management. He is the Chief Editor of the ASCE Natural Hazards Review Journal and Engineering Editor of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. He organized NSF funded international workshop on Innovation on Wind Storm and Storm Surge Mitigation Construction in Dhaka, Bangladesh. Dr. Uddin was appointed to many technical committees, and delivered many invited talks at leading national and international organizations. He organized and moderated a number of Symposiums on Disaster Risk Management at ASCE National Conferences. He is a full bright scholar, He has authored/co-authored over 180 technical papers, 2 books on high performance infrastructures and 4 books on hazards including Quantitative Risk Assessment for Natural Hazards, and Seismic Hazard Design Issues in the Central U.S. World Bank recently published a story on Dr. Uddin’s research about flood and storm surge mitigation in the World Development Report 2010: Development and Climate Change.