Online correction of drift in structural identification using artificial white noise observations and an unscented Kalman Filter

Eleni N. Chatzi,Clemente Fuggini 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.16 No.2

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the “drift effect” in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.

Hybrid evolutionary identification of output-error state-space models

Dertimanis, Vasilis K.,Chatzi, Eleni N.,Spiridonakos, Minas D. Techno-Press 2014 Structural monitoring and maintenance Vol.1 No.4

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

Minimal Invasive Coronary Artery Fistula Ligation

Fotios A Mitropoulos,Andrew Chatzis,Constantinos Contrafouris,Ioanna A. Sofianidou,Achilleas G. Lioulias,Meletios A. Kanakis 대한흉부외과학회 2014 Journal of Chest Surgery (J Chest Surg) Vol.47 No.6

A coronary artery fistula was surgically ligated in a 38-year-old woman via a left anterior mini-thoracotomy without the use of cardiopulmonary bypass. In selected cases, this surgical approach can provide an excellent surgical exposure for coronary artery fistula ligation. It also offers an excellent cosmetic result and shorter hospital stay.

Amplitude-dependent model updating of masonry buildings undergoing demolition

Panagiotis Martakis,Yves Reuland,Eleni Chatzi 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.2

Precise knowledge of dynamic characteristics and data-driven inference of material properties of existing buildings are key for assessing their seismic capacity. While dynamic measurements on existing buildings are typically extracted under ambient conditions, masonry, in particular, exhibits nonlinear behavior at already very low shaking amplitudes. This implies that material properties, inferred via data-driven model updating under ambient conditions, may be inappropriate for predicting behavior under seismic actions. In addition, the relative amount of nonlinearity arising from structural behavior and soilstructure interaction are often unknown. In this work, Bayesian model updating is carried out on field measurements that are representative of increasing levels of shaking, as induced during demolition, on a pre-code masonry building. The results demonstrate that masonry buildings exhibit nonlinear behavior as the elastic modulus drops by up to 18% in the so-called equivalent elastic range, in which the observed frequency drop is reversible, prior to any visible sign of damage. The impact of this effect on the seismic assessment of existing structures is investigated via a nonlinear seismic analysis of the examined case study, calibrated under dynamic recordings of varying response amplitude. While limited to a single building, such changes in the inferred material properties results in a significant reduction of the safety factor, in this case by 14%.

An Empirical Method for Threshold Selection

Stelios Krinidis,Michail Krinidis,Vassilios Chatzis 보안공학연구지원센터(IJSIP) 2012 International Journal of Signal Processing, Image Vol.5 No.2

Effective Heterogeneous Data Fusion procedure via Kalman filtering

Ravizza, Gabriele,Ferrari, Rosalba,Rizzi, Egidio,Chatzi, Eleni N. Techno-Press 2018 Smart Structures and Systems, An International Jou Vol.22 No.5

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

Critical emergency medicine and the resuscitative care unit

Maria Mermiri,Georgios Mavrovounis,Dimitrios Chatzis,Ioannis Mpoutsikos,Aristea Tsaroucha,Maria Dova,Zacharoula Angelopoulou,Dimitrios Ragias,Athanasios Chalkias,Ioannis Pantazopoulos 대한중환자의학회 2021 Acute and Critical Care Vol.36 No.1

Critical emergency medicine is the medical field concerned with management of critically illpatients in the emergency department (ED). Increased ED stay due to intensive care unit (ICU)overcrowding has a negative impact on patient care and outcome. It has been proposed thatimplementation of critical care services in the ED can negate this effect. Two main CriticalEmergency Medicine models have been proposed, the “resource intensivist” and “ED-ICU”models. The resource intensivist model is based on constant presence of an intensivist in thetraditional ED setting, while the ED-ICU model encompasses the notion of a separate EDbasedunit, with monitoring and therapeutic capabilities similar to those of an ICU. Criticalemergency medicine has the potential to improve patient care and outcome; however, establishmentof evidence-based protocols and a multidisciplinary approach in patient managementare of major importance.

Aorto-Right Ventricular Tunnel: An Uncommon Problem with a Common Solution

Fotios Mitropoulos,Meletios A. Kanakis, M.D., Ph.D.,Andrew Chatzis,Maria Kiaffas,Prodromos Azariades,Aphrodite Tzifa 대한흉부외과학회 2016 Journal of Chest Surgery (J Chest Surg) Vol.49 No.4

Aorto-ventricular tunnel is a rare congenital malformation, and aorto-right ventricular tunnel (ARVT) is an even less common entity. Here, we report the case of a 3-month-old female who underwent successful surgical closure of ARVT. The origin of the right coronary artery was proximal to the ostium of the tunnel.

Effective Heterogeneous Data Fusion procedure via Kalman filtering

Gabriele Ravizza,Rosalba Ferrari,Egidio Rizzi,Eleni N. Chatzi 국제구조공학회 2018 Smart Structures and Systems, An International Jou Vol.22 No.5

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

A semi-supervised interpretable machine learning framework for sensor fault detection

Panagiotis Martakis,Artur Movsessian,Yves Reuland,Sai G.S. Pai,Said Quqa,David Garcıa Cava,Dmitri Tcherniak,Eleni Chatzi 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easyto-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.