Development and validation of an automated operational modal analysis algorithm for vibration-based monitoring and tensile load estimation

Rainieri, Carlo; Fabbrocino, Giovanni

doi:10.1016/j.ymssp.2015.01.019

Cited by 115 publications

(65 citation statements)

References 26 publications

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“…The dynamic in situ test was commissioned by the University Administration and was performed by the S2X company, which also carried out the data processing with software developed in the LabView Environment. 37,38 The measurement equipment for the AVT consisted of 20 uniaxial piezoelectric accelerometers, to measure only the horizontal motion of the building; the instruments were mounted on leveled base plates to ensure accurate alignment. Data acquisition was carried out using a 24-bit modular acquisition system type Compact DAQ_1 with 32 channels, which was controlled by a laptop PC.…”

Section: Experimental Dynamic Identification Test and Resultsmentioning

confidence: 99%

“…The aim of the dynamic test was to determine the dynamic characteristics of the case study building through ambient vibration test (AVT). The dynamic in situ test was commissioned by the University Administration and was performed by the S2X company, which also carried out the data processing with software developed in the LabView Environment . The measurement equipment for the AVT consisted of 20 uniaxial piezoelectric accelerometers, to measure only the horizontal motion of the building; the instruments were mounted on leveled base plates to ensure accurate alignment.…”

Section: The Case Study Buildingmentioning

confidence: 99%

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The structural identification of the infill walls contribution in the dynamic response of framed buildings

Angelis

Pecce

2019

Struct Control Health Monit

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Summary Reinforced concrete (RC) frame buildings containing unreinforced masonry infill walls are commonly used in structural systems around the world. The performance of this type of building can be significantly affected by the presence of infill walls according to their type and distribution in the plane and along the height as revealed by earthquakes. This paper presents data collection, system identification, and finite element modeling of an existing RC framed building designed for gravity loads and containing unreinforced masonry infill walls. Based on a previously carried out ambient vibration test, a three‐dimensional finite element model, comprising infill walls and partitions, was successfully updated on the basis of the global modes identified by the in situ test, pointing out the important role of the nonstructural components for this type of building. The influence of the infill walls and partitions on the vibration period was examined, introducing a comparison with simple formulations for the period calculation suggested by codes or available in the literature. The updated model has been further used to assess an approach for an approximate estimation of the story and global stiffness of the building considering the effect of the secondary elements. The dynamic test with the model updating results an efficient procedure for a complete identification of the elastic behavior of the structure.

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Section: Experimental Dynamic Identification Test and Resultsmentioning

confidence: 99%

Section: The Case Study Buildingmentioning

confidence: 99%

The structural identification of the infill walls contribution in the dynamic response of framed buildings

Angelis

Pecce

2019

Struct Control Health Monit

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“…In recent years, many studies attempted to automate the interpretation of stabilization diagram or the modal parameter estimation algorithm in general [13][14][15][16][17]. Owing to the fact that analyzing the stabilization diagram reduces to finding modes with similar properties, the majority of automation strategies borrow methods from statistical machine learning with supervised and unsupervised learning algorithms.…”

Section: Pertinent Literaturementioning

confidence: 99%

“…For instance, a vast majority of those methods require user-defined parameters or thresholds. To the authors' knowledge, the only exceptions are the algorithms proposed by Vanlanduit et al [6], Reynder et al [7], Verboven et al [8], and Rainieri et al [15,17]. However, their methods involve high-dimensional optimization procedures, either for the estimation step [6,8] or the clustering step [7], which deteriorates their performance both in terms of the computational efficiency and convergence.…”

Section: Problem Statementmentioning

confidence: 99%

Automated modal parameter estimation using correlation analysis and bootstrap sampling

Yaghoubi

Vakilzadeh

Abrahamsson

2018

Mechanical Systems and Signal Processing

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The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provide bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical.

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“…This is a very attractive approach when monitoring huge structures because it allows considering the unmeasured environmental excitation as the source to force the system (e.g., [33][34][35][36][37][38][39][40]). This section focuses on the choice of the algorithm used to carry out modal extraction.…”

Section: Identification Of Modal Parametersmentioning

confidence: 99%

The Monitoring of Palazzo Lombardia in Milan

Berardengo

Busca

Grossi

et al. 2017

Shock and Vibration

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This paper discusses the monitoring of Palazzo Lombardia, one of the tallest high-rise buildings in Italy. First, the layout of the monitoring system is addressed for a general description of the sensors used. The paper provides details about how data coming from transducers are used. Special focus is put on the use of signals acquired by means of accelerometers, which are employed for the estimation of modal parameters through operational modal analysis. The procedure used for choosing the modal analysis algorithm and fixing the values of its main parameters is discussed in detail. The modal identification results on the first eight months of monitoring are discussed in the second part of the manuscript, together with a statistical analysis. This allows for a first model of the relationships between eigenfrequencies and environmental variables aiming at a general structural health monitoring procedure based on the evolution of the building's modal parameters.

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Development and validation of an automated operational modal analysis algorithm for vibration-based monitoring and tensile load estimation

Cited by 115 publications

References 26 publications

The structural identification of the infill walls contribution in the dynamic response of framed buildings

The structural identification of the infill walls contribution in the dynamic response of framed buildings

Automated modal parameter estimation using correlation analysis and bootstrap sampling

The Monitoring of Palazzo Lombardia in Milan

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