Multiscale Surrogate Modeling and Uncertainty Quantification for Periodic Composite Structures

Computational modeling of the structural behavior of continuous fiber composite materials often takes into account the periodicity of the underlying micro-structure. A well established method dealing with the structural behavior of periodic micro-structures is the so- called Asymptotic Expansion Homogenization (AEH). By considering a periodic perturbation of the material displacement, scale bridging functions, also referred to as elastic correctors, can be derived in order to connect the strains at the level of the macro-structure with micro- structural strains. For complicated inhomogeneous micro-structures, the derivation of such functions is usually performed by the numerical solution of a PDE problem - typically with the Finite Element Method. Moreover, when dealing with uncertain micro-structural geometry and material parameters, there is considerable uncertainty introduced in the actual stresses experienced by the materials. Due to the high computational cost of computing the elastic correctors, the choice of a pure Monte-Carlo approach for dealing with the inevitable material and geometric uncertainties is clearly computationally intractable. This problem is even more pronounced when the effect of damage in the micro-scale is considered, where re-evaluation of the micro-structural representative volume element is necessary for every occurring damage. The novelty in this paper is that a non-intrusive surrogate modeling approach is employed with the purpose of directly bridging the macro-scale behavior of the structure with the material behavior in the micro-scale, therefore reducing the number of costly evaluations of corrector functions, allowing for future developments on the incorporation of fatigue or static damage in the analysis of composite structural components.Comment: Appeared in UNCECOMP 201

Structural health monitoring of an innovative timber building

A main focus in timber construction research is the development of innovative, sustainable and reliable structures. In order to determine the long-term structural behaviour of these novel structures, structural health monitoring is a valuable tool. In the past two years an innovative timber-hybrid pilot building has been conceived, designed and realized at ETH Zürich. The building contains four innovative structural systems, a post-tensioned timber frame, two timber-concrete hybrid floor systems using beech LVL, and a biaxial pure timber floor in beech wood. In order to fully understand the combined structural behaviour of these innovative systems an extensive monitoring system was set up. The dense sensor network was implemented along with the construction progress, in order to also quantify the effects of important construction stages on the structural behaviour (addition of significant loads, addition of stiffening elements, extreme changes in environmental climate, etc.). The installed setup includes 16 load cells, measuring the changes in the post-tension force in the frame, absolute deformation measurements, temperature and relative humidity sensors, as well as measurements of the moisture content of timber. The monitoring campaign is planned to be continued for several years beyond the completion of construction, in order to quantify the long-term behaviour during the use phase of the building

Optimal sensor placement through Bayesian experimental design: effect of measurement error and number of sensors

Sensors networks for the health monitoring of structural systems ought to be designed to render both accurate estimations of the relevant mechanical parameters and an affordable experimental setup. Therefore, the number, type and location of the sensors have to be chosen so that the uncertainties related to the estimated health are minimized. Several deterministic methods based on the sensitivity of measures with respect to the parameters to be tuned are widely used. Despite their low computational cost, these methods do not take into account the uncertainties related to the measurement process. In former studies, a method based on the maximization of the information associated with the available measurements has been proposed and the use of approximate solutions has been extensively discussed. Here we propose a robust numerical procedure to solve the optimization problem: in order to reduce the computational cost of the overall procedure, Polynomial Chaos Expansion and a stochastic optimization method are employed. The method is applied to a flexible plate. First of all, we investigate how the information changes with the number of sensors; then we analyze the effect of choosing different types of sensors (with their relevant accuracy) on the information provided by the structural health monitoring system

Cost-Benefit Optimization of Sensor Networks for SHM Applications

Structural health monitoring (SHM) is aimed to obtain information about the structural integrity of a system, e.g., via the estimation of its mechanical properties through observations collected with a network of sensors. In the present work, we provide a method to optimally design sensor networks in terms of spatial configuration, number and accuracy of sensors. The utility of the sensor network is quantified through the expected Shannon information gain of the measurements with respect to the parameters to be estimated. At assigned number of sensors to be deployed over the structure, the optimal sensor placement problem is ruled by the objective function computed and maximized by combining surrogate models and stochastic optimization algorithms. For a general case, two formulations are introduced and compared: (i) the maximization of the information obtained through the measurements, given the appropriate constraints (i.e., identifiability, technological and budgetary ones); (ii) the maximization of the utility efficiency, defined as the ratio between the information provided by the sensor network and its cost. The method is applied to a large-scale structural problem, and the outcomes of the two different approaches are discussed

Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

Quantifying the value of the information extracted from a structural health monitoring (SHM) system is an important step towards convincing decision makers to implement these systems. We quantify this value by adaptation of the Bayesian decision analysis framework. In contrast to previous works, we model in detail the entire process of data generation to processing, model updating and reliability calculation, and investigate it on a deteriorating bridge system. The framework assumes that dynamic response data are obtained in a sequential fashion from deployed accelerometers, subsequently processed by an output-only operational modal analysis scheme for identifying the system's modal characteristics. We employ a classical Bayesian model updating methodology to sequentially learn the deterioration and estimate the structural damage evolution over time. This leads to sequential updating of the structural reliability, which constitutes the basis for a preposterior Bayesian decision analysis. Alternative actions are defined and a heuristic-based approach is employed for the life-cycle optimization. By solving the preposterior Bayesian decision analysis, one is able to quantify the benefit of the availability of long-term SHM vibrational data. Numerical investigations show that this framework can provide quantitative measures on the optimality of an SHM system in a specific decision context

On the constitutive modeling of dual-phase steels at finite strains: a generalized plasticity based approach

In this work we propose a general theoretic framework for the derivation of constitutive equations for dual-phase steels, undergoing continuum finite deformation. The proposed framework is based on the generalized plasticity theory and comprises the following three basic characteristics: 1.A multiplicative decomposition of the deformation gradient into elastic and plastic parts. 2.A hyperelastic constitutive equation 3.A general formulation of the theory which prescribes only the number and the nature of the internal variables, while it leaves their evolution laws unspecified. Due to this generality several different loading functions, flow rules and hardening laws can be analyzed within the proposed framework by leaving its basic structure essentially unaltered. As an application, a rather simple material model, which comprises a von-Mises loading function, an associative flow rule and a non-linear kinematic hardening law, is proposed. The ability of the model in simulating simplified representation of the experimentally observed behaviour is tested by two representative numerical examples

On the constitutive modeling of dual-phase steels at finite strains: a generalized plasticity based approach

In this work we propose a general theoretic framework for the derivation of constitutive equations for dual-phase steels, undergoing continuum finite deformation. The proposed framework is based on the generalized plasticity theory and comprises the following three basic characteristics: 1.A multiplicative decomposition of the deformation gradient into elastic and plastic parts. 2.A hyperelastic constitutive equation 3.A general formulation of the theory which prescribes only the number and the nature of the internal variables, while it leaves their evolution laws unspecified. Due to this generality several different loading functions, flow rules and hardening laws can be analyzed within the proposed framework by leaving its basic structure essentially unaltered. As an application, a rather simple material model, which comprises a von-Mises loading function, an associative flow rule and a non-linear kinematic hardening law, is proposed. The ability of the model in simulating simplified representation of the experimentally observed behaviour is tested by two representative numerical examples