arXiv subjects

E. N. Chatzi

Publications and source records attributed to E. N. Chatzi.

3 recordsLinked to original sources

arXiv2021

Attenuation of surface modes in granular media

In this work, an unconsolidated granular medium, made of silica microbeads, is experimentally tested in a laboratory setting. The objective is to investigate the attenuation mechanisms of vertically polarized seismic waves traveling at the surface of unconsolidated substrates that are characterized by power-law rigidity profiles. Both geometric spreading and material damping due to skeletal dissipation are considered. An electromagnetic shaker is employed to excite the granular medium between 300 and 550 Hz, generating linear modes that are localized near the surface. A densely sampled section is recorded at the surface using a laser vibrometer. The explicit solution of the geometric attenuation law of Rayleigh-like waves in layered media is employed to calculate the geometric spreading function of the vertically polarized surface modes within the granular material. In accordance with recent studies, the dynamics of these small-amplitude multi-modal linear waves can be analysed by considering the granular medium as perfectly continuous and elastic. By performing a non-linear regression analysis on particle displacements, extracted from experimental velocity data, we determine the frequency-dependent attenuation coefficients, which account for the material damping. The findings of this work show that laboratory-scale physical models can be used to study the geometric spreading of vertically polarized seismic waves induced by the soil inhomogeneity and characterize the material damping of the medium.

physics.geo-ph↗

arXiv2020

Value of structural health information in partially observable stochastic environments

Efficient integration of uncertain observations with decision-making optimization is key for prescribing informed intervention actions, able to preserve structural safety of deteriorating engineering systems. To this end, it is necessary that scheduling of inspection and monitoring strategies be objectively performed on the basis of their expected value-based gains that, among others, reflect quantitative metrics such as the Value of Information (VoI) and the Value of Structural Health Monitoring (VoSHM). In this work, we introduce and study the theoretical and computational foundations of the above metrics within the context of Partially Observable Markov Decision Processes (POMDPs), thus alluding to a broad class of decision-making problems of partially observable stochastic deteriorating environments that can be modeled as POMDPs. Step-wise and life-cycle VoI and VoSHM definitions are devised and their bounds are analyzed as per the properties stemming from the Bellman equation and the resulting optimal value function. It is shown that a POMDP policy inherently leverages the notion of VoI to guide observational actions in an optimal way at every decision step, and that the permanent or intermittent information provided by SHM or inspection visits, respectively, can only improve the cost of this policy in the long-term, something that is not necessarily true under locally optimal policies, typically adopted in decision-making of structures and infrastructure. POMDP solutions are derived based on point-based value iteration methods, and the various definitions are quantified in stationary and non-stationary deteriorating environments, with both infinite and finite planning horizons, featuring single- or multi-component engineering systems.

cs.LG↗

arXiv2016

Surrogate modelling for stochastic dynamical systems by combining NARX models and polynomial chaos expansions

The application of polynomial chaos expansions (PCEs) to the propagation of uncertainties in stochastic dynamical models is well-known to face challenging issues. The accuracy of PCEs degenerates quickly in time. Thus maintaining a sufficient level of long term accuracy requires the use of high-order polynomials. In numerous cases, it is even infeasible to obtain accurate metamodels with regular PCEs due to the fact that PCEs cannot represent the dynamics. To overcome the problem, an original numerical approach was recently proposed that combines PCEs and non-linear autoregressive with exogenous input (NARX) models, which are a universal tool in the field of system identification. The approach relies on using NARX models to mimic the dynamical behaviour of the system and dealing with the uncertainties using PCEs. The PC-NARX model was built by means of heuristic genetic algorithms. This paper aims at introducing the least angle regression (LAR) technique for computing PC-NARX models, which consists in solving two linear regression problems. The proposed approach is validated with structural mechanics case studies, in which uncertainties arising from both structures and excitations are taken into account. Comparison with Monte Carlo simulation and regular PCEs is also carried out to demonstrate the effectiveness of the proposed approach.

stat.ME↗