Large structures natural frequencies estimation using a limited number of sensors

IEEE Open J. Instrum. Meas.

2022

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Stadium grandstands may experience high vibration levels due to the crowd action. Existing codes and guidelines used for the design and the assessment of grandstands highlight the need of an already ongoing revision, since there is a lack of information about the real nature of the crowd excitation and its accurate evaluation. This paper describes a highly improved computer vision method, based on the use of Digital Image Correlation (DIC), able to provide reliable estimates of the crowd induced loads on stadium grandstands. The effectiveness and the applicability of DIC is validated through a series of experimental tests held at the G. Meazza stadium in Milan. A first study with 27 jumping volunteers is carried out to assess the performance of DIC in a real environment. Then, the procedure is extended to the case of a larger crowd made of 108 individuals. In both cases, the forces estimated through image processing have been used in conjunction with the impulse response function of the stand to determine the resulting structural vibrations. The comparison between measured and computed vibrations provides a good agreement both in the time and the frequency domain.

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Section: The Grandstands Of the G Meazza Stadium In Milanmentioning

confidence: 99%

Experimental Validation of a Vision-Based Technique to Estimate the Crowd Loading on Stadium Grandstands

IEEE Open J. Instrum. Meas.

2022

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“…This data set represents a challenging situation, as it includes data subjected to the contemporary presence of both changing environmental conditions and different structural states, i.e., before, during and after an operation of retrofitting to resolve a construction error that emerged during inspections. Since structural modifications associated with the retrofit manifest, among others, with changes in natural frequencies having a similar variability of those due to environmental parameters [ 37 ], the effect of confounding influences is here rather critical. Furthermore, a very limited number of publications inherent to this case study are currently available.…”

Section: Introductionmentioning

confidence: 99%

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

2022

Sensors

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Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.

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Damage Detection Using Supervised Machine Learning Algorithms for Real-World Engineering Structures

Lecture Notes in Civil Engineering

et al. 2022