PMC-MS-00: General Submissions

We welcome abstracts relevant to the PMC 2016 conference. Topics of interest include stochastic mechanics, stochastic dynamics, random processes, stochastic damage modeling, system identification, health monitoring, uncertainty quantification, verification & validation, reliability and risk analysis, optimization, epistemic uncertainty, and risk management. If you can identify a suitable MS# for your abstract from the list below, please submit your abstract to that MS directly. Otherwise, please submit your abstract to this MS, and the Chair will assign a suitable MS for your abstract.

PMC-MS-01: Advanced simulation-based approaches to uncertainty quantification and reliability analysis

Michael D. Shields, Johns Hopkins University
Siu-Kui (Ivan) Au, Centre for Engineering Dynamics and Institute for Risk and Uncertainty, University of Liverpool

Simulation of physical processes forms the backbone of computational uncertainty and reliability analyses. Monte Carlo simulation is the most robust simulation-based approach for such problems and serves as a benchmark against new methods. In recent years, rapid advances that improve upon classical Monte Carlo simulation, coupled with improvements in computational resources have begun to usher in a new era of simulation-based uncertainty analysis such that modern challenges, e.g., uncertainty quantification for very large and complex systems or inverse problems, are becoming increasingly tractable. Examples of new methods include Markov Chain Monte Carlo (MCMC) approaches, stochastic collocation, sparse-grid sampling methods, Bayesian nested sampling, variance reduction techniques (i.e. Latin hypercube), and new adaptive Monte Carlo methods. This symposium aims at exploring the latest developments in simulation-based approaches for uncertainty quantification and reliability analysis of physical systems, with the potential of establishing new benchmarks from which the next generation of approaches will evolve.

PMC-MS-02: Probabilistic methods for fatigue damage monitoring, diagnosis and prognosis

Eric Hernandez, University of Vermont
Yongming Liu, Arizona State University
Shankar Sankararaman, NASA

This mini-symposium will serve as a platform for exchange of ideas and knowledge dissemination concerning the latest developments in the field of probabilistic fatigue damage monitoring and diagnosis. Applications in mechanical, civil, electrical, aerospace and biomechanical engineering are welcomed.

Topics of interest include: probabilistic damage and crack propagation models, fatigue life estimation, fatigue damage prognosis, stress and strain monitoring, load monitoring, multi-scale models, model-data fusion, uncertainty quantification, crack detection, Bayesian methods, stochastic simulation methods for damage prognosis, parameter estimation, design and analysis of fatigue experiments, model verification, validation, calibration, and case studies.

PMC-MS-03: Uncertainty Modeling & Propagation Techniques In Stochastic Dynamics

Ioannis A. Kougioumtzoglou, Columbia University
Antonina Pirrotta, University of Palermo, Italy
Pol D. Spanos, Rice University
Mario Di Paola, University of Palermo, Italy

The field of uncertainty quantification that includes the characterization and propagation of uncertainties associated with complex systems has received considerable interest in recent years. A major portion of the engineering dynamics/mechanics community has focused, with considerable success, on problems with stochastic media properties, random excitations and uncertain initial/boundary conditions. Nevertheless, the development of novel mathematical tools and of potent signal processing techniques, the ever-increasing available computational capabilities, and advanced experimental setups offer a unique novel tool for addressing complex problems for the first time and even posing new questions. Specifically, researchers and engineers are faced with the challenge of interpreting and translating measured data at multiple scales into pertinent stochastic models. In this regard, there is a need for developing robust multi-scale statistical descriptors and stochastic models capable of capturing complex uncertainty relationships. Further, there is a need for developing analytical/numerical methodologies for solving nonlinear high-dimensional stochastic (partial) differential equations efficiently, and for propagating uncertainty across various scales in the time and space domains. The objective of this MS is to present recent advances and emerging cross-disciplinary approaches in the broad field of stochastic engineering dynamics/mechanics with a focus on uncertainty modeling, and propagation. Further, this MS intends to provide a forum for a fruitful exchange of ideas and interaction among diverse technical and scientific disciplines. Specific contributions related both to fundamental research and to engineering applications of computational stochastic dynamics/mechanics and signal processing methodologies are welcome. A non-exhaustive list includes joint time/space-frequency analysis tools, spectral analysis/estimation subject to highly incomplete/sparse data, efficient high-dimensional functional representation and identification, stochastic/fractional calculus modeling and applications, nonlinear stochastic dynamics, stochastic stability and control theory, multiscale/multi-physics stochastic modeling and analysis, stochastic model/dimension reduction techniques, Monte Carlo simulation methods, and risk/reliability assessment applications.

PMC-MS-04: Structural Identification and Damage Detection

THIS IS A MINI-SYMPOSIA FOR BOTH CONFERENCES
EMI Dynamics Committee
EMI Structrual Control and Health Monitoring Committee

Eleni Chatzi, ETH-Zurich
Costas Papadimitriou, University of Thessaly
Siu-Kui Au, University of Liverpool

The mini-symposium deals with structural identification methods and applications, as well as structural health monitoring algorithms for damage detection and reliability prognosis. It covers theoretical and computational issues, applications in structural dynamics, earthquake engineering, mechanical and aerospace engineering, as well as other related engineering disciplines. Topics relevant to the session include: theoretical and experimental modal identification, operational modal analysis, linear and nonlinear system identification, statistical system identification methods (maximum-likelihood, Bayesian inference) for parameter and state estimation, model updating/validation and correlation, uncertainty quantification in model selection and parameter estimation, stochastic simulation techniques for state estimation and model class selection, structural health monitoring and fault detection techniques, optimal strategies for experimental design, optimal sensor and actuator location methods, structural prognosis techniques, updating response and reliability predictions using data. Papers dealing with experimental investigation and verification of theories are especially welcomed.

PMC-MS-05: Uncertainty Quantification and Stochastic/Non-probability Design Optimization of High-Dimensional Complex Systems

Guangyong Sun, Hunan University, China
Yujiang Xiang, University of Alaska Fairbanks, USA
Xuchun Ren*, Georgia Southern University, USA

This mini-symposia will accommodate a forum for PMC 2016 to discuss recent advances and explore the future prospects in the area of uncertainty quantification, as well as stochastic design optimization, for high dimensional complex system. Interested researchers are encouraged to submit abstracts on topics which include, but are not limited to: (a) uncertainty quantification, (b) robust design optimization, (c) reliability-based design optimization, (d) dimension-reduction method, (e) dimension decomposition method, (f) methods for stochastic/interval/fuzzy analysis of high-dimensional engineering problems, (g) methods for stochastic/non-probability design optimization of high-dimensional engineering problems. If you know your MS#, please send your abstract to that MS. Otherwise, please submit it here and the Chair will assist you to find an appropriate MS for your submission.

PMC-MS-06: Model Uncertainty in Multidisciplinary Analyses

AIAA Non-Deterministic Approaches Technical Committee

Benjamin P. Smarslok, U.S. Air Force Research Laboratory

Quantification of model uncertainty in multidisciplinary analyses can be extremely challenging and computationally intensive for coupled, time-dependent, multi-physics, multi-scale models. Not only does each model component have natural variability in model inputs (e.g., material properties and loading), but there is also model-form error associated with each quantity of interest (QoI) in the multidisciplinary system. In addition, model calibration and validation is often impeded due to high experimental costs (e.g., wind tunnel tests or microstructural material characterization), as well as limited data from the inability to concurrently measure all of the multidisciplinary quantities of interest (e.g., fluid-thermal-structural interaction). This mini-symposium is intended to address fundamental research challenges for multidisciplinary analyses associated with quantifying model uncertainty (e.g., Bayesian model calibration), identifying significant uncertainty sources in coupled model outputs (e.g., sensitivity analysis), reducing model uncertainty through data collection (e.g., experimental design), and assessing models for spatial and temporal validation.

PMC-MS-07: Uncertainty Quantification and Model Verification and Validation in Multiscale Simulation

Yan Wang, Georgia Tech
Laura Swiler, Sandia National Laboratories

The importance of model form and parameter uncertainty has been recognized in various modeling, simulation, and analysis applications, where assumptions and simplifications affect the accuracy of model predictions for physical phenomena. Different methods such as sensitivity analysis and Bayesian approaches can be applied to quantify uncertainty associated with mathematical models. Yet model form uncertainty is typically confounded with variability of input data, which makes its quantification challenging. For various engineering applications of multiscale simulation in mechanics, dynamics, and others, model form uncertainty needs to be differentiated from the stochasticity of model inputs and data errors to support more robust risk analysis and decision making. Additional challenge of applying multiscale simulations for reliable prediction is how to verify and validate models of different length and time scales, given that the lack of verifiable data or capabilities for direct observations at very small (short) or large (long) scales is common. This minisymposium intends to focus on the state-of-the-art of methods to quantify model form uncertainty and their applications in model verification and validation for simulations at multiple scales. Better understanding of both probabilistic and non-probabilistic approaches to assess and improve the confidence of simulation-based predictions is the goal.

PMC-MS-08: Earthquake hazards and beyond: Opportunities for integrating geosciences and engineering

Ting Lin, Marquette University

This mini-symposium invites contributions in the interface between geosciences and engineering, with a focus on hazards. In particular, advances in seismology, geodesy, and geology facilitate characterization of earthquake hazards for engineering analyses and decisions across time scales. Submissions covering various aspects of earthquake hazards and their impact on critical infrastructure are welcome. Potential time scales of study could span from pre-event hazard analysis for long-term planning, real-time or near real-time forecasting, to post-event response. Other hazards of interest include but are not limited to tsunami, volcano, wind, storm surge, and sea-level rise. Quantification of these hazards can benefit from the synergy between geosciences and engineering that potentially embraces physics-based models and empirical data. This mini-symposium provides a cross-cutting forum to address the challenges and opportunities in advancing understanding of earthquake and other hazards for risk reduction.

PMC-MS-09: Critical Infrastructure Systems Modeling: Risk, Reliability, and Resilience

Infrastructure Resilience Committee

Hiba Baroud, Vanderbilt University
Bilal Ayyub, University of Maryland

Critical infrastructure systems have recently garnered significant attention due to their interconnectedness leading to greater vulnerability to large scale disruptions with severe impacts. As a result, research in this area is focused on identifying the risk today’s critical infrastructures face and developing tools that define and measure their reliability and resilience. The objective of this striving research area is to enhance and rehabilitate the current state of the infrastructure as well as to improve the design of future systems. This mini-symposium is intended to provide a platform for discussion and exchange of the recent theoretical, methodological, and applied research advances in the fields of risk, reliability, and resilience of critical infrastructure systems. Topics of interest include but are not limited to risk analysis, risk-informed decision making, resource allocations, recovery planning, resilience metrics, risk management, data-driven methods, interdependency modeling, reliability engineering, civil infrastructures resilience, probabilistic risk assessment, statistical analysis of network systems, uncertainty modeling, etc.

PMC-MS-10: Community Resilience in China

Jie Li, Tongji University, China
Jianjun Qin, Tongji University, China

Over the last decades, more and more large-scale engineering communities have been realized all over China, together with the large-scale urbanization. Several megacities with a total population in excess of ten million people and hundreds of big cities with over one million residents are formulated. The large-scale engineering communities interact with social, financial and other networks to constitute complex systems. Meanwhile, it is well recognized the engineering communities in China are always threatened by disaster events, which resulted in severe consequences. For example, the 1998 Yangtze floods reported over 5 million houses collapsed and 13.2 million people homeless. More recently, Wenchuan earthquake in 2008 caused great paralysis of railway, power, communication, water supply and other infrastructure systems to interrupt the relief and normal activities and around 69,000 fatalities were reported.

In China, there are not only experiences and lessons from disasters for engineering communities, but the research achievements on the aspect of community resilience also. Date back to the end of last century, the framework of dynamic assessment of the performance of lifeline systems before, during and after the seismic hazards, for example, is established. Now, it would be recognized that a big step has been moved forward on the respect of both modeling and numerical analysis of the performance of different types of engineering communities since then. The aim of this symposia is to illustrate the latest developments on this topic in China. The scholars from all over the world are welcome to discuss the needs and challenges of the community resilience in the country. Also, the potential actions of such research in China would be investigated.

PMC-MS-11: Objective Resilience in Engineering Mechanics

Objective Resilience Committee

Mohammed Ettouney, Weidlinger Associates

Objective Resilience concerns itself with formalizing issues pertaining to resilience of assets and communities. There are several ways to explore the subject of resilience. 1-Resilience logistical components which comprise robustness, resourcefulness, recovery and redundancy (so called 4Rs). 2-Resilience management components which comprise assessment, acceptance, treatment, monitoring, and communications. 3-Resilience as a subset of risk, which comprise quality of operations and time to recovery. We need to consider all of these viewpoints in order to properly address the subject of resilience in an objective manner. This session will contain presentations which address several aspects of objective resilience and how it relates to engineering mechanics subjects such as robustness, uncertainties, assessment, and monitoring. The session will also address the multidisciplinary aspects of resilience as they relate to engineering mechanics field.

PMC-MS-12: Advances in computational modeling and uncertainty quantification for analysis, design and management of infrastructure systems

Arash Noshadravan, Texas A&M University
Hadi Meidani, University of Illinois at Urbana-Champaign
Dan Frangopol, Lehigh University

Evaluating and enhancing the long-life performance of critical infrastructure systems require a toolset that maps the system level applications to informed decisions regarding the analysis, design, and management of these systems. Uncertainty quantification and predictive modeling are critical components of such a framework. This PMC2016 mini-symposium will provide the opportunity to discuss recent advances in developing uncertainty-aware computational models for reliable and robust analysis, design, and management of infrastructures systems including, but not limited to, transportation systems, building systems, water and energy networks, and various socio-technical systems. The topics of interest include reliability analysis and optimization, life-cycle performance maintenance and management (structural, environmental, and economical), physics-based models for energy simulation, interdependent infrastructure systems, network modeling and analysis, sustainability and resilience to disasters, decision making under uncertainty and Big Data analytics.

PMC-MC-13: Quantification and Propagation of Uncertainty in Engineering Modeling and Design

Mehdi Modares, Illinois Institute of Technology
Zissimos Mourelatos, Oakland University

In various engineering disciplines, developments of theoretical and experimental methods are often of increased complexity because of high-fidelity computational models, extensive acquisition of measured data and the presence of impreciseness and uncertainty. This session will cover analytical and experimental methodologies and new directions in probabilistic and non-probabilistic quantification and propagation of uncertainty in engineering modeling and design. Topics include, but are not limited to, design under uncertainty using probabilistic and non-probabilistic methods, predictive modeling and inference with limited data, computational techniques for uncertainty quantification and propagation, multi-fidelity and surrogate modeling for design under uncertainty, design of multiscale engineering systems under uncertainty, hybrid computational and experimental methods for design under uncertainty, interval analysis of large-scale finite-element models, and random vibrations of linear and non-linear systems, among others.

PMC-MS-14: Risk/reliability-based and robust structural/topology optimization of civil structures exposed to natural and man-made hazards

Seymour MJ Spence, University of Michigan
Alexandros Taflanidis, University of Notre Dame

The mitigation of the effects of natural and/or man-made hazards on the built environment is one of the core challenges of civil engineering. Over the past decades numerous methodologies have been developed/ formulated to this end, incorporating reliability/risk/performance–based concepts and, more recently, resiliency principles. While these approaches may appear to be founded on different assumptions, they all have in common the rational treatment of uncertainty that inherently affects all aspects of hazard modeling and response prediction. These include the uncertainties characterizing loading environment, mechanical properties of structural materials, boundary conditions, modeling assumptions, construction procedures, as well as other relevant uncertainties. In addition to assessing the performance of structural/infrastructural systems within such a probabilistic setting, there is growing interest in identifying design solutions that explicitly optimize this performance. Recent computational advances have led to new and unprecedented possibilities in this respect with methods such as reliability-based and robust design, topology optimization under uncertainty, life-cycle optimization and risk-informed decision making– seeing theoretical developments and practical applications that would have been unthinkable only a decade ago.

The aim of this mini-symposium is to provide an opportunity for researchers in the fields of hazard modeling, uncertainty modeling and structural/topology optimization under uncertainty to present their current research efforts as well as future directions. Contributions addressing theoretical and computational developments, numerical algorithms as well as practical applications from different sub-fields of uncertainty modeling in engineering (e.g. risk management and optimization, modeling of hazards and extreme or rare events, stochastic dynamics, modeling of uncertainty with probability theory – Bayesian theory, interval models, fuzzy set theory etc.) and relevant optimization approaches (e.g. robust optimization, topology optimization, risk-based design) are welcome. The mini-symposium will provide an opportunity to bring together researchers, academics, design code developers, and practicing engineers active in these topical areas to share their experience and latest research results.

PMC-MS-15: Surrogate Models for Uncertainty Quantification, Reliability/Risk Assessment and Robust Design

Bruno Sudret, ETH Zürich, Institute of Structural Engineering
Alexandros Taflanidis, University of Notre Dame

Structural reliability methods and more generally, methods that aim at taking into account model- and parameter uncertainty have received much attention in the mechanical, civil, and aerospace engineering communities over the past two decades. Some well-known methods such as FORM/SORM for reliability analysis, spectral methods for stochastic finite element analysis, global sensitivity analysis (Sobol’ indices), Monte Carlo approaches etc. are nowadays applied in an industrial context, e.g. nuclear, aerospace, civil and automotive industries, among others. In parallel, advances in computer and computational science have been supporting the development of advanced, high-fidelity, numerical models that have greatly improved our ability to accurately model (and thus understand) complex engineering systems in the aforementioned fields. However, many of these models are highly computationally intensive, involving, for example, the solution of governing ordinary or partial differential equations over a large spatial and temporal domain using finite difference or finite element methods. A single run of such models may last minutes to hours, even on powerful computers. This creates a challenge for their direct adoption for reliability-analysis and reliability-design optimization (or more generally for problems that involve uncertainty propagation techniques), which require repeated calls to the computational code. It is necessary in this setting to develop a substitute that may be evaluated thousands to millions of times at low cost: these substitutes are referred to as metamodels or surrogate models.

The aim of this mini-symposium is to confront various kinds of meta-modeling techniques in the context of uncertainty propagation including classical response surfaces, polynomial chaos expansions, Kriging, support vector regression, neural networks, sparse grid interpolation, etc. Papers that present new methodology developments as well as applications relevant to the engineering mechanics community that make use of surrogate models are welcome.

PMC-MS-16: Bayesian Methods in Uncertainty Quantification and Probabilistic Engineering Design

Pingfeng Wang, Wichita State University
Zhen Hu, Vanderbilt University

Bayesian methods involve formal combination of prior knowledge and new evidences to yield updated beliefs regarding a quantity of interest, and have been prevalently used for the quantitative incorporation of external evidence into the design, monitoring, and analysis of engineering systems and processes. With the growing capability of computational modeling and computer technology, the Bayesian methods also play an increasing important role in bridging the gap between experimental studies and engineering design analysis using computational simulation models through model validation & verification. This mini-symposium focuses on Bayesian methodologies and their engineering applications in uncertainty quantification, design optimization, and risk management. Topics of interest include but are not limited to the following: inference/calibration/updating, Bayesian Networks (BNs), reliability analysis, failure diagnosis/prognosis, information fusion and data analytics, experimental design, system design optimization, efficient Bayesian computation, and applications in probabilistic engineering analysis and design.

PMC-MS-17: Modeling Resilient Infrastructure

Paolo Gardoni, Center for Risk-Based Community Resilience Planning, University of Illinois at Urbana-Champaign
John van de Lindt, Center for Risk-Based Community Resilience Planning, Colorado University

Modern societies rely on large-scale interdependent networks and systems, including transportation, water and wastewater, electric power, communication and information networks that are critical for economic growth and societal well-being. Such infrastructure are vulnerable to natural hazards, such as earthquakes and tsunamis, hurricanes, tornadoes, floods, and wildfires, as well as anthropogenic hazards from industrial accidents, disease and malevolence.

Past disasters have shown that the societal consequences of the damage and failure of infrastructure are often several times the physical damage to such systems. Further, population growth, economic development in regions vulnerable to natural hazards such as coastal regions, and climate change further exacerbate the risks. The impact on society is typically not limited to the immediate aftermath of a hazardous event but can be long lasting as shown by recent disasters, such as Hurricanes Katrina and Sandy, earthquakes in Haiti, Chile, New Zealand and Japan, and Cyclone Haiyan in the Philippines. The long-term impact on society highlights the need to develop new infrastructure that are more resilient to hazards. A resilient infrastructure is not only reliable (i.e., the probability of being damaged or failure is small) but it also has the ability to recover rapidly. It is clear then that resilience is not simply a property of the isolated infrastructure but the infrastructure needs to be considered with its dependencies and interdependences with other physical and socio-economic infrastructure that influence its functionality and recovery.

This symposium will highlight new research to help overcome some of the most critical challenges toward the development of a resilient built environment, among them: 1) the understanding of physical infrastructure dependencies and interdependencies, their interfaces with supporting social, economic and political networks, the role that each system plays in disaster recovery; including how the vulnerability of populations contributes to the extent of damage and disruption, and time to recovery, 2) the development of metrics and tools for assessing resilience; 3) the development of new methods, algorithms and formulations to model and quantify resilience accounting for infrastructural dependencies and interdependences; 4) the development of new design criteria, codes and standards that can improve the resilience of the built infrastructure; and 5) the spatio-temporal optimization of resource allocation for best mitigation and recovery planning.

Examples of infrastructure considered in this mini-symposium include, but are not limited to, transportation, water and wastewater, electric power, communication and information networks.

PMC-MS-18: System reliability effects in infrastructure systems

Cristopher D. Moen, Virginia Polytechnic Institute
Benjamin W. Schafer, Johns Hopkins University
Sanjay R. Arwade, University of Massachusetts, Amherst

System reliability effects are known to occur throughout civil infrastructure systems and the theory of system reliability has been well developed, yet system reliability concepts remain relatively absent from modern design codes, potentially leading to inefficiencies or underpredicted safety of the built environment. This mini-symposium welcomes work related to system reliability modeling, prediction and application from theoretical or application-oriented points of view and from the point of view of code-specification writing bodies that may attempt to incorporate system effects in design codes.

Many of the challenges facing the application of system reliability concepts to design are related to the high dimension of uncertainty associated with civil engineering systems and the high degree of nonlinearity present in models for the capacity determination of those systems. Therefore, particular attention is called to new work that addresses the curse of dimensionality and ameliorates the need for large numbers of solution runs to estimate reliability. Furthermore, approximate engineering models can have great value in allowing the bounding or efficient approximation of system reliability effects and the relatively straightforward assessment of those effects by designers. Finally, contributions directly related to the incorporation of system effects in design codes are welcomed and viewed as particularly important to the supporting theories of system reliability that impact the built environment.

PMC-MS-19: Characterization, simulation, and modeling of random heterogeneous materials

Lori Graham-Brady, Johns Hopkins University
Michael Shields, Johns Hopkins University
Johann Guilleminot, Universite Paris-Est

Dramatic advances in computational capabilities and three-dimensional characterization facilities have enabled multi-scale mechanics models with unprecedented levels of resolution that represent the underlying microstructure and lower-scale mechanisms in materials under a variety of stress states. However, the process is riddled with uncertainty at all stages, including random measurement errors associated with the characterization techniques, natural variations between different material samples of a finite size, and uncertainty in the model parameters. This symposium will bring together researchers studying the role that these uncertainties associated with random material heterogeneities play in multi-scale model predictions. For example, contributions could include efforts to address uncertainty associated with microstructural data collection, stochastic simulation of polycrystalline and/or multi-phase materials, upscaling of material properties via stochastic homogenization, stochastic inverse analysis to infer microstructural parameters from experimental observations, or surrogate/reduced-order models to represent lower-scale micro- or nano-mechanics.

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