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In this paper, an approach based on the synergistic use of proper orthogonal decomposition and Kalman filtering is proposed for the online health monitoring of damaged structures. The reduced-order model of a structure is obtained during an (offline) initial training stage of monitoring; afterward, effective estimations of a possible structural damage are provided online by tracking the evolution in time of stiffness parameters and projection bases handled in the model order reduction procedure. Such tracking is accomplished via two Kalman filters: a first (extended) one to deal with the time evolution of a joint state vector, gathering the reduced-order state and the stiffness terms degraded by damage; a second one to deal with the update of the reduced-order model in case of damage evolution. Both filters exploit the information conveyed by measurements of the structural response to the external excitations. Results are reported for a (pseudo-experimental) benchmark test on an eight-story shear building. Capability and performance of the proposed approach are assessed in terms of tracked variation of the stiffness terms of the reduced-order model, identified damage location and speed-up of the whole health monitoring procedure.

In this article, transverse vibration of an Euler-Bernoulli beam carrying a series of traveling masses is analyzed. A semi-analytical approach based on eigenfunction expansion method is employed to achieve the dynamic response of the beam. The inertia of the traveling masses changes the fundamental period of the base beam. Therefore, a comprehensive parametric survey is required to reveal the resonance velocity of the traversing inertial loads. In order to facilitate resonance detection for engineering practitioners, a new simplified formula is proposed to approximate the resonance velocity.

In this paper, joint identification for structural systems, characterized by severe nonlinearities (softening) in the constitutive model, is pursued via the Sigma-Point Kalman Filter (S-PKF) and the Particle Filter (PF). Since a formal proof of the effects of softening in a stochastic structural system on the accuracy and stability of the filters is still missing, we comparatively assess the performances of S-PKF and PF. We show that the PF displays a higher convergence rate towards steady-state model calibrations and the S-PKF is less sensitive to the measurement noise. Both S-PKF and PF are robust, even if they tend to get unstable when a structural failure is triggered. [PUBLICATION ABSTRACT]

In this article, the dynamic responses of a Timoshenko beam subjected to a moving mass, and a moving sprung mass are analyzed. By making recourse to Hamilton's principle, governing differential equations for beam vibration are derived. By using the modal superposition method, the partial differential equations of the system are transformed into a set of Ordinary Differential Equations (ODEs). The resulted set of ODEs is represented in state-space form, and solved by means of a numerical technique. The accuracy of the results has been ascertained through comparing the results of our approach with those available from previous studies; moreover, a reasonable agreement has been obtained. The quantities of the dynamic response of the beam for the case of moving sprung mass are confronted with moving mass and also moving load cases. Through extensive numerical campaign, it is concluded that with respect to the applied values of suspension system properties, the deflections of the beam subjected to moving load case are an upper bound for moving sprung mass results. [PUBLICATION ABSTRACT]

Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for output-only system identification and input estimation of dynamic systems. Using merely sparse output-only measurements, the proposed PINNs architectures furnish a novel approach to input, state, and parameter estimation of linear and nonlinear systems with multiple degrees of freedom. These architectures are comprised of parallel and sequential PINNs that act upon a set of ordinary differential equations (ODEs) obtained from spatial discretization of the partial differential equation (PDE). The performance of this framework for dynamic system identification and input estimation was ascertained by extensive numerical experiments on linear and nonlinear systems. The advantage of the proposed approach, when compared with system identification, lies in its computational efficiency. When compared with traditional Artificial Neural Networks (ANNs), this approach requires substantially smaller training data and does not suffer from generalizability issues. In this regard, the states, inputs, and parameters of dynamic state-space equations of motion were estimated using simulated experiments with “noisy” data. The proposed framework for PINN showed excellent great generalizability for various types of applications. Furthermore, it was found that the proposed architectures significantly outperformed ANNs in generalizability and estimation accuracy.

Microscale uncertainties related to the geometry and morphology of polycrystalline silicon films, constituting the movable structures of micro electro-mechanical systems (MEMS), were investigated through a joint numerical/experimental approach. An on-chip testing device was designed and fabricated to deform a compliant polysilicon beam. In previous studies, we showed that the scattering in the input–output characteristics of the device can be properly described only if statistical features related to the morphology of the columnar polysilicon film and to the etching process adopted to release the movable structure are taken into account. In this work, a high fidelity finite element model of the device was used to feed a transitional Markov chain Monte Carlo (TMCMC) algorithm for the estimation of the unknown parameters governing the aforementioned statistical features. To reduce the computational cost of the stochastic analysis, a synergy of proper orthogonal decomposition (POD) and kriging interpolation was adopted. Results are reported for a batch of nominally identical tested devices, in terms of measurement error-affected probability distributions of the overall Young’s modulus of the polysilicon film and of the overetch depth.

Bridge health monitoring is critical for infrastructure safety, especially with the growing deployment of IoT sensors. This work addresses the challenge of securely storing large volumes of sensor data and extracting actionable insights for timely damage detection. We propose BIONIB, a novel framework that combines an unsupervised machine learning approach called the Novelty Index (NI) with a scalable blockchain platform (EOSIO) for secure, real-time monitoring of bridges. BIONIB leverages EOSIO’s smart contracts for efficient, programmable, and secure data management across distributed sensor nodes. Experiments on real-world bridge sensor data under varying loads, climatic conditions, and health states demonstrate BIONIB’s practical effectiveness. Key findings include CPU utilization below 40% across scenarios, a twofold increase in storage efficiency, and acceptable latency degradation, which is not critical in this domain. Our comparative analysis suggests that BIONIB fills a unique niche by coupling NI-based detection with a decentralized architecture, offering real-time alerts and transparent, verifiable records across sensor nodes.

Transfer learning techniques for structural health monitoring in bridge-type structures are investigated, focusing on model generalizability and domain adaptation challenges. Finite element models of bridge-type structures with varying geometry were simulated using the OpenSeesPy platform. Different levels of damage states were introduced at the midspans of these models, and Gaussian-based load time histories were applied at mid-span for dynamic time-history analysis to calculate acceleration data. Then, this acceleration time-history series was transformed into grayscale images, serving as inputs for a Convolutional Neural Network developed to detect and classify structural damage states. Initially, it was trained and tested on datasets derived from a Single-Source Domain structure, achieving perfect accuracy (1.0) in a ten-label multi-class classification task. However, this accuracy significantly decreased when the model was sequentially tested on structures with different geometry without retraining. To address this challenge, it is proposed that transfer learning be employed via feature extraction and joint training. The model showed a reduction in accuracy percentage when adapting from a Single-Source Domain to Multiple-Target Domains, revealing potential issues with non-homogeneous data distribution and catastrophic forgetting. Conversely, joint training, which involves training on all datasets except the specific Target Domain, generated a generalized network that effectively mitigated these issues and maintained high accuracy in predicting unseen class labels. This study highlights the integration of simulation data into the Deep Learning-based SHM framework, demonstrating that a generalized model created via Joint Learning utilizing FEM can potentially reduce the consequences of modeling errors and operational uncertainties unavoidable in real-world applications.

This paper presents a comprehensive literature review on the digital twinning of floating offshore wind turbines (FOWTs). In this study, the digital twin (DT) is defined as a dynamic virtual model that accurately mirrors a physical system throughout its lifecycle, continuously updated with real-time data and use simulations, machine learning, and analytics to support informed decision-making. The recent advancements and major issues have been introduced, which need to be addressed before realizing a FOWT DT that can be effectively used for life extension and operation and maintenance planning. This review synthesizes relevant literature reviews focused on modeling FOWT and its specific components along with the latest research. It specifically focuses on the structural, mechanical, and energy production components of FOWTs within the DT framework. The state of the art DT for FOWT, or large scale operational civil and energy infrastructure, is not yet matured to perform real-time update of digital replicas of these systems. The main barriers include real-time coupled modeling with high fidelity, the design of sensor networks, and optimization methods that synergize the sensor data and simulations to calibrate the model. Based on the literature survey provided in this paper, one of the main barriers is uncertainty associated with the external loads applied to FOWT. In this review paper, a robust method for inverse analysis in the absence of load information has been introduced and validated by using simulated experiments. Furthermore, the regulatory requirements have been provided for FOWT life extension and the potential of DT in achieving that.

When the dimensions of polycrystalline structures become comparable to the average grain size, some reliability issues can be reported for the moving parts of inertial microelectromechanical systems (MEMS). Not only the overall behavior of the device turns out to be affected by a large scattering, but also the sensitivity to imperfections gets enhanced. In this work, through on-chip tests, we experimentally investigate the behavior of thin polysilicon samples using standard electrostatic actuation/sensing. The discrepancy between the target and actual responses of each sample has then been exploited to identify: (i) the overall stiffness of the film and, according to standard continuum elasticity, a morphology-based value of its Young’s modulus; (ii) the relevant over-etch induced by the fabrication process. To properly account for the aforementioned stochastic features at the micro-scale, the identification procedure has been based on particle filtering. A simple analytical reduced-order model of the moving structure has been also developed to account for the nonlinearities in the electrical field, up to pull-in. Results are reported for a set of ten film samples of constant slenderness, and the effects of different actuation mechanisms on the identified micromechanical features are thoroughly discussed.