Publications

2019

1. Next generation reduced order models for soil-structure interaction Asimaki, D, Garcia-Suarez, J, Kusanovic, D, Nguyen, K, and Seylabi, E Esmaeilzadeh In Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions: Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019, June 17-20, 2019, Rome, Italy 2019 [Abs]

   A number of published solutions have been dedicated to problems of dynamic soil-structure interaction SSI for geotechnical infrastructure over the past 50 years. Some have been formulated using idealized models with a number of severe assumptions based on physical intuition. Others have been developed by fitting large-scale experimental results or back-calculated from field measurements. Their simplified formulation and wide use notwithstanding, reliable predictions of such models are guaranteed so long as the geometry, material properties and loading conditions of the problems at hand match the reference configurations. Over the past decade, we have worked on developing SSI reduced order models ROMs based on rigorous mechanics, intended to expand the range of their applicability to conditions outside the range of numerical and physical experiments used to formulate them. In this paper, we present three examples: a framework based on small parameters, energy and momentum balance to resolve soil-structure on rigid retaining walls; a two-dimensional linear ROM for transient analysis of nonlinear buildings on mat foundations in a homogeneous half-space; and a two-dimensional nonlinear ROM for the transient analysis of pipeline systems subjected to transient ground deformation. We will specifically highlight the physics of each problem as revealed by formalized mechanics solutions and high-fidelity simulations; present reduced order models that capture salient aspects of the physics; and show comparisons of our model predictions to state-of-the art methodologies.

2018

1. System Identification of Soil-structure Interaction Mechanisms for Building Structures Kusanovic, DS, Seylabi, E Esmaeilzadeh, and Asimaki, DM In SMIP18 Seminar On Utilization of Strong-Motion Data 2018 [Abs]

   We quantify the effects of dynamic soil-structure-interaction on building structures using system-identification techniques and finite element simulations. We develop analytic expressions for distributed spring and dashpot elements at the soil-foundation interface in terms of dimensionless variables. A system-identification approach based on Extended-Kalman-Filter is employed to estimate the truesoil impedance as seen from the building-foundation system. The impedances estimated are next used to span the range of applicability of the proposed soil impedance model using nonlinear curve-fitting. We find good-agreement between the proposed flexible-based-model and the full finite element-model in period lengthening, radiation damping, time-history responses and their frequency contents.

2017

1. Untangling the Dynamics of Soil-structure Interaction Using Nonlinear Finite Element Model Updating Ebrahimian, Hamed, Asimaki, Domniki, Kusanovic, Danilo S., and Ghahari, Shahrbanoo In SMIP17 Seminar On Utilization of Strong-Motion Data 2017 [Abs]

   The dynamic response of a building structure to an earthquake excitation is the result of a complex interaction between the structural system and the underlying and surrounding geology. Since modeling the physics of the coupled soil-structure system is a complex undertaking, the state-of-practice has adopted simplified modeling procedures, such as the substructure method. Nevertheless, these procedures are often empirical and/or based on idealized assumptions, such as linear-elasticity. In this study, our objective is to develop a robust model inversion framework that can be utilized to extract information from the real-world building response measurements to back-calculate the model parameters that characterize the structural response and soil-structure interaction effects.

2015

1. Base-Isolated Systems, Reliability-Based Characterization of Jensen, Hector A., and Kusanovic, D. S. 2015 [Abs]

   The recent improvements in isolation system products have led to the design and construction of an increasing number of seismically isolated buildings worldwide (Ceccoli et al. 1999, Chopra 1995, Kelly 1986, Zou et al. 2010). Similarly, seismic isolation has been extensively used for seismic retrofitting of existing buildings (DeLuca et al. 2001, Mokha et al. 1996). In addition, base isolation concepts are utilized for the protection from shock and vibration of sensitive components of critical facilities such as hospitals, nuclear reactors, industrial and data center facilities. One of the difficulties in the analysis and design of base-isolated systems has been the explicit consideration of the non-linear behavior of the isolators. Another challenge has been the efficient prediction of the dynamic response under future ground motions considering their potential variability as well as competing objectives related to the protection of the superstructure and the minimization of base displacement. The objective of this work is to characterize the performance of base-isolated systems from a reliability point of view. In particular, the case of large scale building models is considered here. Isolation systems composed by rubber bearings are used in the present formulation. The non-linear behavior of these devices is characterized by a biaxial hysteretic model which is calibrated with experimental data (Yamamoto et al. 2009). First excursion probabilities are used as measures of the system reliability.

2014

1. Model-reduction techniques for Bayesian finite element model updating using dynamic response data Jensen, H.A., Millas, E., Kusanovic, D., and Papadimitriou, C. Computer Methods in Applied Mechanics and Engineering 2014 [Abs]

   This work presents a strategy for integrating a class of model reduction techniques into a finite element model updating formulation. In particular a Bayesian model updating approach based on a stochastic simulation method is considered in the present formulation. Stochastic simulation techniques require a large number of finite element model re-analyses to be performed over the space of model parameters during the updating process. Substructure coupling techniques for dynamic analysis are proposed to reduce the computational cost involved in the dynamic re-analyses. The effectiveness of the proposed strategy is demonstrated with identification and model updating applications for finite element building models using simulated seismic response data.

2. On the effect of near-field excitations on the reliability-based performance and design of base-isolated structures Jensen, H.A., and Kusanovic, D.S. Probabilistic Engineering Mechanics 2014 [Abs]

   This work explores the effect of near-field excitations on the reliability-based performance and design of base-isolated systems. In particular base-isolated buildings under uncertain excitation are considered in this work. A probabilistic logic approach is adopted for considering the variability of future excitations. Isolation elements composed by rubber bearings are used in the present formulation. The non-linear behavior of the bearings is characterized by a biaxial hysteretic model which is calibrated with experimental data. The performance of the isolated system is defined in terms of the isolators deformations and the superstructure interstory drifts and absolute accelerations. First excursion probabilities are used as measures of system reliability. Two example problems involving large finite element building models are presented to illustrate the ideas set forth.

2012

1. Compromise design of stochastic dynamical systems: A reliability-based approach Jensen, Hector A., Kusanovic, Danilo S., and Valdebenito, Marcos A. Probabilistic Engineering Mechanics 2012 [Abs]

   This paper presents a procedure for obtaining compromise designs of structural systems under stochastic excitation. In particular, an effective strategy for determining specific Pareto optimal solutions is implemented. The design goals are defined in terms of deterministic performance functions and/or performance functions involving reliability measures. The associated reliability problems are characterized by means of a large number of uncertain parameters (hundreds or thousands). The designs are obtained by formulating a compromise programming problem which is solved by a first-order interior point algorithm. The sensitivity information required by the proposed solution strategy is estimated by an approach that combines an advanced simulation technique with local approximations of some of the quantities associated with structural performance. An efficient Pareto sensitivity analysis with respect to the design variables becomes possible with the proposed formulation. Such information is used for decision making and tradeoff analysis. Numerical validations show that only a moderate number of stochastic analyses (reliability estimations) has to be performed in order to find compromise designs. Two example problems are presented to illustrate the effectiveness of the proposed approach.

2011

1. An Efficient First-Order Scheme for Reliability Based Optimization of Stochastic Systems Jensen, Hector A., Schuëller, Gerhart I., Valdebenito, Marcos A., and Kusanovic, Danilo S. 2011 [Abs]

   This contribution presents a novel approach for solving reliability-based optimization (RBO) problems. In particular, the reliability-based optimization of structural systems under stochastic loading is considered. The approach is based on a descent-feasible direction and a line search strategy, allowing to explore the space of the design variables most efficiently. The proposed method is monotonically convergent, that is, it generates a sequence of steadily improved feasible designs. An efficient simulation technique is used for the purpose of evaluating the reliability constraints. On the other hand, the sensitivity of the reliability constraints with respect to the design variables is estimated by an approach based on the local behavior of the performance functions that define the failure domains. Numerical results show that only a moderate number of reliability estimates has to be performed during the design process. A numerical example showing the efficiency and effectiveness of the approach reported herein is presented.

2010

1. Reliability-based synthesis of non-linear stochastic dynamical systems: a global approximation approach Jensen, Hector A., Ferre, Macarena S., and Kusanovic, Danilo S. International Journal of Reliability and Safety 2010 [Abs]

   The paper presents an efficient methodology to carry out reliability-based structural optimisation of non-linear systems under stochastic loading. The optimisation problem is formulated as the minimisation of an objective function subject to multiple reliability constraints. The reliability constraints are given in terms of first excursion probabilities with large state-space dimensions. A sequential approximate optimisation scheme based on global approximations of the reliability constraints is implemented in the proposed formulation. The approximations of the first excursion probabilities in terms of the design variables are constructed by combining a global mixed linearisation approach with a reliability sensitivity analysis. The sensitivity of the first excursion probabilities are estimated by considering some statistics of an augmented reliability problem. The number of reliability estimations required during the optimal design process is generally very small. Two numerical examples are presented to illustrate the effectiveness of the method.

2009

1. Reliability-based optimization of stochastic systems using line search Jensen, H.A., Valdebenito, M.A., Schuëller, G.I., and Kusanovic, D.S. Computer Methods in Applied Mechanics and Engineering 2009 [Abs]

   This contribution presents an approach for solving reliability-based optimization problems involving structural systems under stochastic loading. The associated reliability problems to be solved during the optimization process are high-dimensional (1000 or more random variables). A standard gradient-based algorithm with line search is used in this work. Subset simulation is adopted for the purpose of estimating the corresponding failure probabilities. The gradients of the failure probability functions are estimated by an approach based on the local behavior of the performance functions that define the failure domains. Numerical results show that only a moderate number of reliability estimates has to be performed during the entire design process. Two numerical examples showing the effectiveness of the approach reported herein are presented.