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Sequential ensemble Monte Carlo sampler for on-line Bayesian inference of time-varying parameter In engineering applications

Lye, Adolphus (author), Marino, Luca (author), Cicirello, A. (author), Patelli, Edoardo (author)

Several on-line identification approaches have been proposed to identify parameters and evolution models of engineering systems and structures when sequential datasets are available via Bayesian inference. In this work, a robust and “tune-free” sampler is proposed to extend one of the sequential Monte Carlo implementations for the identification...

journal article 2023

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A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearity

Marino, Luca (author), Cicirello, A. (author)

An approach for the identification of discontinuous and nonsmooth nonlinear forces, as those generated by frictional contacts, in mechanical systems that can be approximated by a single-degree-of-freedom model is presented. To handle the sharp variations and multiple motion regimes introduced by these nonlinearities in the dynamic response, the...

journal article 2023

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Governing Equation Identification of Nonlinear Single Degree-of-Freedom Oscillators With Coulomb Friction Using Explicit Stick and Slip Temporal Constraints

Mahajan, Saurabh (author), Cicirello, A. (author)

The friction force at joints of engineering structures is usually unknown and not directly identifiable. This contribution explores a procedure for obtaining the governing equation of motion and correctly identifying the unknown Coulomb friction force of a mass-springdashpot system. In particular, a single degree-of-freedom system is...

journal article 2023

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Natural Language Processing for systems engineering: Automatic generation of Systems Modelling Language diagrams

Zhong, Shaohong (author), Scarinci, Andrea (author), Cicirello, A. (author)

The design of complex engineering systems is an often long and articulated process that highly relies on engineers’ expertise and professional judgment. As such, the typical pitfalls of activities involving the human factor often manifest themselves in terms of lack of completeness or exhaustiveness of the analysis, inconsistencies across...

journal article 2023

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Bayesian system identification for structures considering spatial and temporal correlation

Koune, I.C. (author), Rózsás, Árpád (author), Slobbe, Arthur (author), Cicirello, A. (author)

The decreasing cost and improved sensor and monitoring system technology (e.g., fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian system identification strategies, the correlation in the model prediction error can become significant. The...

journal article 2023

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Inference and maintenance planning of monitored structures through Markov chain Monte Carlo and deep reinforcement learning

Lathourakis, C.L. (author), Andriotis, C. (author), Cicirello, A. (author)

A key computational challenge in maintenance planning for deteriorating structures is to concurrently secure (i) optimality of decisions over long planning horizons, and (ii) accuracy of realtime parameter updates in high-dimensional stochastic spaces. Both are often encumbered by the presence of discretized continuous-state models that describe...

conference paper 2023

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Special Section on Uncertainty Quantification and Management in Nonlinear Dynamical Systems in Aerospace and Mechanical Engineering

Yuan, Jie (author), Denimal, Enora (author), Bi, Sifeng (author), Feng, Jinglang (author), Hu, Quan (author), Cicirello, A. (author)

contribution to periodical 2023

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Virtual sensing of subsoil strain response in monopile-based offshore wind turbines via Gaussian process latent force models

Zou, Joanna (author), Lourens, E. (author), Cicirello, A. (author)

Virtual sensing techniques have gained traction in applications to the structural health monitoring of monopile-based offshore wind turbines, as the strain response below the mudline, which is a primary indicator of fatigue damage accumulation, is impractical to measure directly with physical instrumentation. The Gaussian process latent force...

journal article 2023

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Spider dynamics under vertical vibration and its implications for biological vibration sensing

Wu, Jun (author), Miller, Thomas E. (author), Cicirello, A. (author), Mortimer, Beth (author)

Often overlooked, vibration transmission through the entire body of an animal is an important factor in understanding vibration sensing in animals. To investigate the role of dynamic properties and vibration transmission through the body, we used a modal test and lumped parameter modelling for a spider. The modal test used laser vibrometry...

journal article 2023

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A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring

Oncescu, Andreea Maria (author), Cicirello, A. (author)

A self-supervised classification algorithm is proposed for detecting and isolating sensor faults of health monitoring devices. This is achieved by automatically extracting information from failure investigations. This approach uses (i) failure reports for extracting comprehensive failure labels; (ii) recorded data of a faulty monitoring...

conference paper 2023

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Cyclical Variational Bayes Monte Carlo for efficient multi-modal posterior distributions evaluation

Igea, Felipe (author), Cicirello, A. (author)

Multi-modal distributions of some physics-based model parameters are often encountered in engineering due to different situations such as a change in some environmental conditions, and the presence of some types of damage and non-linearity. In statistical model updating, for locally identifiable parameters, it can be anticipated that multi...

journal article 2023

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Machine learning based optimization for interval uncertainty propagation

Cicirello, A. (author), Giunta, F. (author)

Two non-intrusive uncertainty propagation approaches are proposed for the performance analysis of engineering systems described by expensive-to-evaluate deterministic computer models with parameters defined as interval variables. These approaches employ a machine learning based optimization strategy, the so-called Bayesian optimization, for...

journal article 2022

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A comparative study between Amontons-Coulomb and Dieterich-Ruina friction laws for the cyclic response of a single degree of freedom system

Cabboi, A. (author), Marino, Luca (author), Cicirello, A. (author)

This study aims at assessing the predictive performance of the Amontons–Coulomb law to reliably predict the cyclic response, inclusive of stick–slip, of a single degree of freedom system in contact with the ground through two versions (steady-state and rate-and-state) of a regularized Dieterich–Ruina law. The assessment is carried out by...

journal article 2022

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On the Combination of Random Matrix Theory With Measurements on a Single Structure

Igea, Felipe (author), Chatzis, Manolis N. (author), Cicirello, A. (author)

An approach is proposed for the evaluation of the probability density functions (PDFs) of the modal parameters for an ensemble of nominally identical structures when there is only access to a single structure and the dispersion parameter is known. The approach combines the Eigensystem realization algorithm on sets of dynamic data, with an...

journal article 2022

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Coulomb friction effect on the forced vibration of damped mass-spring systems

Marino, Luca (author), Cicirello, A. (author)

This paper aims at assessing the effect of dry friction on the dynamic behaviour of a damped mechanical system subject to harmonic forcing. Previous work on friction damped systems highlighted that not including other forms of damping in the dynamic analysis can lead to unrealistic results such as the presence of infinite resonances. In this...

journal article 2022

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An efficient and robust sampler for Bayesian inference: Transitional Ensemble Markov Chain Monte Carlo

Lye, Adolphus (author), Cicirello, A. (author), Patelli, Edoardo (author)

Bayesian inference is a popular approach towards parameter identification in engineering problems. Such technique would involve iterative sampling methods which are often robust. However, these sampling methods often require significant computational resources and also the tuning of a large number of parameters. This motivates the development...

journal article 2022

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Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine

Zou, J. (author), Cicirello, A. (author), Iliopoulos, Alexandros (author), Lourens, E. (author)

Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model...

conference paper 2022

document

Sensor fault label identification for robust structural health monitoring

Oncescu, Andreea Maria (author), Cicirello, A. (author)

Health Monitoring strategies rely on tracking the health status of critical engineering structures (Structural Health Monitoring) and of people (monitoring of medical conditions) to detect anomalies in the measurements and make inferences on the health condition for supporting decisions on preventive actions to be implemented to restore...

conference paper 2021

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On the investigation of utility functions on optimal sensor locations

Igea, Felipe (author), Chatzis, Manolis N. (author), Cicirello, A. (author)

Structural Health Monitoring uses data collected from sensors placed on structures to determine their operating condition and whether maintenance is required. Often, optimal sensor placement strategies are used to find the optimal locations for the identification of their modal properties, structural parameters and/or abnormal behaviours under...

conference paper 2021

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Sampling methods for solving Bayesian model updating problems: A tutorial

Lye, Adolphus (author), Cicirello, A. (author), Patelli, Edoardo (author)

This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the context of Bayesian model updating for engineering applications. Markov Chain Monte Carlo, Transitional Markov Chain Monte Carlo, and Sequential Monte Carlo methods are introduced, applied to different case studies and finally their performance is compared....

journal article 2021