Structural health continuous monitoring of buildings – A modal parameters identification system

Muttillo, Mirco; Battista, Luca Di; Rubeis, Tullio de; Nardi, Iole

doi:10.23919/splitech.2019.8783051

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…En tal contexto, la detección temprana de daño estructural ayuda al mantenimiento de la estructura y prevención de eventos catastróficos; esto supone un ahorro económico a largo plazo. Con respecto a las aplicaciones de la monitorización de salud estructural, en (Boscato et al, 2016) y (Muttillo et al, 2019) se propone el análisis de edificaciones a través del despliegue de sensores estratégicamente ubicados. Por otro lado, en (Pachón et al, 2018) se realiza el estudio de la salud estructural del puente de Posadas (Córdoba, España) con la metodología OSP (Optimal Sensor Placement) para una eficiente monitorización continua del puente, obteniéndose errores menores al 2%.…”

Section: Introductionunclassified

Análisis de métodos OMA para la extracción de parámetros modales sobre edificios existentes

Galindo

Serrano

León

et al. 2021

rte

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En este artículo se presenta la aplicación de métodos de análisis modal operacional (OMA, Operational Modal Analysis) con el objetivo de caracterizar los parámetros modales (v.g. frecuencias y modos de vibración) de una edificación. El estudio se realiza sobre un escenario real consistente en un edificio esencial. En concreto se emplea: el método de descomposición en el dominio de la frecuencia (FDD, Frequency Domain Decomposition) y su versión mejorada (EFDD, Enhanced-FDD). En una primera etapa, se lleva a cabo una evaluación estructural preliminar del edificio (empleando el método de inspección visual rápida, RVS), un levantamiento de dimensiones y ensayos en campo para la caracterización mecánica de sus componentes, todo ello con el propósito de conseguir un análisis modal convencional lo más fiable posible en términos de sus parámetros modales. Con base en este análisis modal, se diseña un plan de instrumentación con acelerómetros triaxiales de sistemas microelectromecánicos (MEMS); el proceso de instrumentación abarca tres etapas: la adquisición, el control y el almacenamiento de información. La principal contribución de este trabajo consiste en la evaluación de la aplicación de los métodos FDD y EFDD sobre un edificio esencial, con la particularidad del uso de vibraciones de microsismicidad para la identificación de parámetros modales. El análisis de los resultados obtenidos determina una frecuencia fundamental del edificio de 1.43 Hz, y evidencia un comportamiento modal no recomendado.

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Section: Introductionunclassified

Análisis de métodos OMA para la extracción de parámetros modales sobre edificios existentes

Galindo

Serrano

León

et al. 2021

rte

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“…In this work, an enhanced version with a different use of the proposal shown in [ 34 ] is presented. The proposed monitoring system for structural health is based on a microcontroller and two triaxial accelerometric sensors.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring: An IoT Sensor System for Structural Damage Indicator Evaluation

Muttillo

Stornelli

Alaggio

et al. 2020

Sensors

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In the last decades, the applications of structural monitoring are moving toward the field of civil engineering and infrastructures. Nevertheless, if the structures have damages, it does not mean that they have a complete loss of functionality, but rather that the system is no longer in an optimal condition so that, if the damage increases, the structure can collapse. Structural Health Monitoring (SHM), a process for the identification of damage, periodically collects data from suitable sensors that allow to characterize the damage and establishes the health status of the structure. Therefore, this monitoring will provide information on the structure condition, mostly about its integrity, in a short time, and, for infrastructures and civil structures, it is necessary to assess performance and health status. The aim of this work is to design an Internet of Things (IoT) system for Structural Health Monitoring to find possible damages and to see how the structure behaves over time. For this purpose, a customized datalogger and nodes have been designed. The datalogger is able to acquire the data coming from the nodes through RS485 communication and synchronize acquisitions. Furthermore, it has an internal memory to allow for the post-processing of the collected data. The nodes are composed of a digital triaxial accelerometer, a general-purpose microcontroller, and an external memory for storage measures. The microcontroller communicates with an accelerometer, acquires values, and then saves them in the memory. The system has been characterized and the damage indicator has been evaluated on a testing structure. Experimental results show that the estimated damage indicator increases when the structure is perturbed. In the present work, the damage indicator increased by a maximum value of 24.65 when the structure is perturbed by a 2.5 mm engraving.

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“…However, as we move towards external digital output sensors, we are faced with a unique set of challenges concerning synchronized sensing. Muttillo et al [ 8 ] proposed a wireless system for indoor monitoring using an ADXL355, which is a high-resolution digital accelerometer. However, the case study reports the use of only one sensor node directly connected to two accelerometers for OMA of a beam-element in a building; therefore, the study did not explore the data synchronization aspect.…”

Section: Introductionmentioning

confidence: 99%

Development of Synchronized High-Sensitivity Wireless Accelerometer for Structural Health Monitoring

Shajihan

Chow

Mechitov

et al. 2020

Sensors

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The use of digital accelerometers featuring high sensitivity and low noise levels in wireless smart sensors (WSSs) is becoming increasingly common for structural health monitoring (SHM) applications. Improvements in the design of Micro Electro-Mechanical System (MEMS) based digital accelerometers allow for high resolution sensing required for SHM with low power consumption suitable for WSSs. However, new approaches are needed to synchronize data from these sensors. Data synchronization is essential in wireless smart sensor networks (WSSNs) for accurate condition assessment of structures and reduced false-positive indications of damage. Efforts to achieve synchronized data sampling from multiple WSS nodes with digital accelerometers have been lacking, primarily because these sensors feature an internal Analog to Digital Converter (ADC) to which the host platform has no direct access. The result is increased uncertainty in the ADC startup time and thus worse synchronization among sensors. In this study, a high-sensitivity digital accelerometer is integrated with a next-generation WSS platform, the Xnode. An adaptive iterative algorithm is used to characterize these delays without the need for a dedicated evaluation setup and hardware-level access to the ADC. Extensive tests are conducted to evaluate the performance of the accelerometer experimentally. Overall time-synchronization achieved is under 15 µs, demonstrating the efficacy of this approach for synchronization of critical SHM applications.

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Structural health continuous monitoring of buildings – A modal parameters identification system

Cited by 12 publications

References 17 publications

Análisis de métodos OMA para la extracción de parámetros modales sobre edificios existentes

Análisis de métodos OMA para la extracción de parámetros modales sobre edificios existentes

Structural Health Monitoring: An IoT Sensor System for Structural Damage Indicator Evaluation

Development of Synchronized High-Sensitivity Wireless Accelerometer for Structural Health Monitoring

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