Nonlinear stochastic controllers for semiactive and regenerative structural systems, with guaranteed quadratic performance margins

In many applications of vibration control, the circumstances of the application impose constraints on the energy available for the actuation of control forces. Semiactive dampers (i.e., viscous dampers with controllable coefficients) constitute the simplest example of such actuation in structural control applications. Regenerative Force Actuation (RFA) networks are an extension of semiactive devices, in which mechanical energy is first converted to electrical energy, which is then dissipated in a controllable resistive network. A fairly general class of semiactive and regenerative systems can be characterized by a differential equation which is bilinear (i.e., linear in state, linear in control input, but nonlinear in both). This paper presents a general approach to bilinear feedback control system design for semiactive and regenerative systems, which is analytically guaranteed to out-perform optimal linear viscous damping in stationary stochastic response, under the familiar Quadratic Gaussian performance measure. The design for full-state feedback and for the more practical case of noise-corrupted and incomplete measurements (i.e., output feedback) are separately discussed. Variants of the theory are shown to exist for other quadratic performance measures, including risk-sensitive and multi-objective frameworks. An illustrative application to civil engineering is presented

Reduced order modelling of hysteretic structural response in seismic risk assessment

Modern seismic risk/loss estimation practices require simulation of structural behavior for different levels of earthquake shaking through time-history analysis. This behavior can be strongly inelastic/hysteretic and evaluating it through high-fidelity finite element models introduces a significant computational burden. A reduced order modeling approach is discussed here to alleviate this burden. The reduced order model is developed using data from the original high-fidelity finite element model (FEM). Static condensation is first used to obtain the stiffness matrix and linear equations of motion for the dynamic degrees of freedom. The restoring forces prescribed by the linear stiffness matrix are then substituted with hysteretic ones, calibrated by comparing the reduced order model time-history to the time-history of the initial FEM for a range of different excitations. This is posed as a least squares optimization problem and its efficient solution is facilitated through a sequential approach. The accuracy and the computational savings of the reduced order model are then examined for seismic risk assessment applications by comparing to the FEM predictions. A stochastic ground motion model is used to describe the seismic hazard and the accuracy for different levels of intensity is separately examined.Authors would like to thank Dr. Papakonstantinou for prodiving the codes for generation of synthetic acceleration time-histories using the (Vlachos, et al. 2018) model

Robust Stochastic Design of Linear Controlled Systems for Performance Optimization

This study discusses a robust controller synthesis methodology for linear, time invariant systems, under probabilistic parameter uncertainty. Optimization of probabilistic performance robustness for [script H]_2 and multi-objective [script H]_2 measures is investigated, as well as for performance measures based on first-passage system reliability. The control optimization approaches proposed here exploit recent advances in stochastic simulation techniques. The approach is illustrated for vibration response suppression of a civil structure. The results illustrate that, for problems with probabilistic uncertainty, the explicit optimization of probabilistic performance robustness can result in markedly different optimal feedback laws, as well as enhanced performance robustness, when compared to traditional “worst-case” notions of robust optimal control

Probabilistically-robust nonlinear control of offshore structures

A controller design for offshore structures is discussed in this study. Stochastic simulation is considered for evaluation of the system's performance in the design stage. This way, nonlinear characteristics of the structural response and excitation are explicitly incorporated into the model assumed for the system. Model parameters that have some level of uncertainty are probabilistically described. In this context, the controller is designed for optimal reliability, quantified as the probability, based on the available information, that the performance will not exceed some acceptable bounds. This treatment leads to a robust-to-uncertainty design. The methodology is illustrated in an example involving the control of a Tension Leg Platform in a random sea environment. Multifold nonlinearities are taken into account for the evaluation of the platform's dynamic response and a probabilistic description is adopted for characterizing the random sea environment

Innovation clubs: Mobilizing local creativity for sustainable development and pedagogy

Two months after the 2010 Haiti earthquake devastated Port-au-Prince, a reconnaissance team of structural engineering researchers from the University of Notre Dame travelled to Léogâne to assess the damage. As their research revealed the underlying problems plaguing the residential construction industry in Léogâne, they realized that recommending externally-devised engineering solutions alone would not bring about significant change. In order to discover more holistic and implementable solutions, they turned to the affected community. By hosting open innovation challenges throughout Léogâne, the research team identified local innovators who demonstrated leadership, problem-solving skills, and creativity. These local innovators, forty-two men and women of various professions representing each of the six zones of Léogâne, were then trained and certified in the Pwosesis pou Innovasyon (Innovation Process) - a Creole-adapted form of Human-Centered Design. These individuals subsequently engaged their neighbors to form six Innovation Clubs. For seven years, undergraduate and graduate engineering students have worked with these Innovation Clubs to facilitate sustainable development research initiatives. This paper describes the framework used to establish these Innovation Clubs, case studies of cooperative projects, the observed benefits of this collaboration, and recommendations for future implementation

Integrated workflow for evaluating sustainability and resiliency of building systems

This study describes the development of a workflow for integrated life-cycle assessment (iLCA) of buildings that is capable of capturing the dependencies between multi-hazard resilience and sustainability using tools native to professional practice. Modules dedicated to hazard characterization, structural response, damage, repair/loss, and environmental impact (embodied and operating energy) are developed using Application Programming Interfaces (APIs) and semantic data perspectives from computer science. A unifying probabilistic framework is utilized to quantify life-cycle performance and a common, versatile, simulation-based approach is adopted for estimation of performance. This approach supports various resilience/sustainability metrics, including monetary losses, downtime, total embodied energy (initial construction and repairs), and operating energy. A case study executed in the Revit environment evaluates the performance of a special reinforced concrete frame located near Los Angeles International Airport (LAX). Two design alternatives are considered to illustrate the impact of design and material decisions, ultimately revealing design choices which best achieve joint resiliency and sustainability.The authors gratefully acknowledge the support of NSF (CMMI-1537652). The first author also recognizes the support of her NSF Graduate Research Fellowship (DGE-1313583) and Deans Fellowship from the University of Notre Dame. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF

Reliability-based design of tuned mass-damper-inerter (TMDI) equipped multi-storey frame buildings under seismic excitation

The reliability based optimal design is considered of tuned mass-damper-inerter (TMDI) equipped linear building frames subject to seismic excitations modeled as stationary colored random processes. The TMDI is a recently introduced generalization of the classical linear tuned mass-damper (TMD) benefitting from the mass amplification property, the so-called inertance, of the inerter device to enhance the vibration suppression capabilities of the TMD. The frequency, damping ratio, and inertance TMDI properties are treated as design variables to minimize out-crossing rates of pre-specified thresholds for building floor accelerations, inter-storey drifts, and TMDI mass displacement. Numerical data pertaining to a 10-storey frame structure equipped with a TMDI arranged in 12 different topologies are furnished indicating the enhanced performance of the TMDI over the classical TMD especially for relatively small additional attached mass.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.Facult

Multi-objective design under uncertainty using a Kriging-based evolutionary optimizer

Μulti-objective design problems with probabilistic objectives estimated through stochastic simulation are examined in this paper. For the efficient solution of such problems a surrogate model based optimization scheme, termed MODU-AIM, was recently developed by the authors. Foundations of MODU-AIM are the formulation of the surrogate model in the augmented input space, composed of both the design variables and the uncertain model parameters, and an iterative implementation that adaptively controls surrogate model accuracy. At each iteration, a new surrogate model is developed, and a new Pareto front is identified using epsilon-constraint numerical optimization scheme. This front is then compared to the previous iterations front to examine convergence. If convergence has not been established, a set of refinement experiments is identified for the surrogate model development and process proceeds to the next iteration. In this paper, integration of multi-objective evolutionary optimizers (MOEA) is considered for MODU-AIM. This integration extends MODU-AIMs applicability and numerical efficiency and requires a number of modifications and enhancements to address the unique traits of MOEA optimizers with respect to the Pareto front identification