Proof of concept of the structural health monitoring of framed structures by a novel combined experimental and theoretical approach

Isidori, Daniela; Concettoni, Enrico; Cristalli, Cristina; Soria, Leonardo; Lenci, Stefano

doi:10.1002/stc.1811

Cited by 19 publications

(9 citation statements)

References 49 publications

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“…Prior to the application of observability techniques, a FEM of the structure should be established based on the geometry of the structure to be identified, which is a common preliminary step in many identification methods . With this FEM and the stiffness matrix method, the equilibrium equations together with strength of materials theory might be written in terms of nodal displacements and nodal forces as presented in Equation .…”

Section: Structural System Identification By Observability Techniquesmentioning

confidence: 99%

Analysis of measurement and simulation errors in structural system identification by observability techniques

Lei

Galant

Nogal

et al. 2016

Struct. Control Health Monit.

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This is the peer reviewed version of the following article: [Lei, J., Lozano-Galant, J. A., Nogal, M., Xu, D., and Turmo, J. (2017) Analysis of measurement and simulation errors in structural system identification by observability techniques. Struct. Control Health Monit., 24: . doi: 10.1002/stc.1923.], which has been published in final form at http://onlinelibrary.wiley.com/wol1/doi/10.1002/stc.1923/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.During the process of structural system identification, errors are unavoidable. This paper analyzes the effects of measurement and simulation errors in structural system identification based on observability techniques. To illustrate the symbolic approach of this method a simply supported beam is analyzed step-by-step. This analysis provides, for the very first time in the literature, the parametric equations of the estimated parameters. The effects of several factors, such as errors in a particular measurement or in the whole measurement set, load location, measurement location or sign of the errors, on the accuracy of the identification results are also investigated. It is found that error in a particular measurement increases the errors of individual estimations, and this effect can be significantly mitigated by introducing random errors in the whole measurement set. The propagation of simulation errors when using observability techniques is illustrated by two structures with different measurement sets and loading cases. A fluctuation of the observed parameters around the real values is proved to be a characteristic of this method. Also, it is suggested that a sufficient combination of different load cases should be utilized to avoid the inaccurate estimation at the location of low curvature zones.Peer ReviewedPostprint (author's final draft

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Section: Structural System Identification By Observability Techniquesmentioning

confidence: 99%

Analysis of measurement and simulation errors in structural system identification by observability techniques

Lei

Galant

Nogal

et al. 2016

Struct. Control Health Monit.

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“…These conditions affect the structures causing degradation, aging, and damage [42]; they are also the cause of possible false detections in SHM systems, due to the sensitivity of the methods to operational and environmental variables (EOVs) [43,44]. For that reason, the influence of these variables must be considered in the development of a reliable SHM system [45].…”

Section: Operational and Environmental Conditionsmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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“…In this context, Structural Health Monitoring (SHM) systems can improve the safety and reliability of structures, reduce maintenance costs, predict failures, and extend service life. [8][9][10] For these reasons, SHM, [2,11] which is defined as detecting changes to the material or geometric properties of structures, is crucial for building safety, [12,13] being one of the key components of engineering.…”

Section: Introductionmentioning

confidence: 99%

“…According to previous studies, SHM makes it possible to identify the presence of damage and measure damage evolution. It has received considerable attention and has successfully replaced traditional visual inspection for damage detection and maintenance of structures subjected to various loadings .…”

Section: Introductionmentioning

confidence: 99%

An integrated structural health monitoring system for determining local/global responses of historic masonry buildings

Blanco

Boffill

Lombillo

et al. 2018

Struct Control Health Monit

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Summary In this study, an integrated structural health monitoring system was developed and applied to a masonry heritage building under rehabilitation, where diverse structural changes were made. The late 19th century building had over the years undergone significant deterioration, which brought about the appearance of serious pathological processes affecting the building's stability. In 2012, concerns over the severe cracking phenomena affecting the structure prompted the decision to monitor the structure and to undertake strengthening interventions. A global structural monitoring system was applied to the church to identify the occurrence of possible damage mechanisms and monitoring the intervention processes. The monitoring system was based on the integration of subsystems, which were implemented as and when necessary. Traditional sensors consisting of crackmeters, servo‐inclinometers, cable extension sensors, displacement transducers, wind vanes, and temperature sensors (thermo‐hygrometer and thermocouples) were installed on the building. Moreover, novel measuring approaches using stress sensors and pressure cells were used. Furthermore, manual monitoring elements were installed, which provided measurements for contrasting with those of the continuous sensors, as well as a greater number of monitoring points. Thus, the structural response was measured using 67 monitoring elements. The long‐term (5 years) monitoring results indicated that the interventions were carried out without the occurrence of any irregularity. A good relation was confirmed between the manual and continuous measurements. Moreover, the results have shown the capability of the proposed integrated system to evaluate the structural behaviour during all the phases of the interventions.

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Proof of concept of the structural health monitoring of framed structures by a novel combined experimental and theoretical approach

Cited by 19 publications

References 49 publications

Analysis of measurement and simulation errors in structural system identification by observability techniques

Analysis of measurement and simulation errors in structural system identification by observability techniques

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

An integrated structural health monitoring system for determining local/global responses of historic masonry buildings

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