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RISS 인기검색어
- 검색키워드
- 저자 : Carmelo Gentile (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Vibration-based structural health monitoring of stay cables by microwave remote sensing
Carmelo Gentile,Alessandro Cabboi 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.16 No.2
Microwave remote sensing is probably the most recent experimental technique suitable to the non-contact measurement of deflections on large structures, in static or dynamic conditions. In the first part of the paper, the main techniques adopted in microwave remote sensing are described, so that advantages and potential issues of these techniques are presented and discussed. Subsequently, the paper addresses the application of the radar technology to the measurement of the vibration response on the stay cables of two cable-stayed bridges. The dynamic tests were performed in operational conditions (i.e. with the excitation being mainly provided by micro-tremors, wind and traffic) and the maximum deflections of the cables were generally lower than 5.0 mm. The investigation clearly highlights: (a) the safe and simple use of the radar on site and its effectiveness to simultaneously measure the dynamic response of all the stay cables of an array; (b) the negligible effects of the typical issues and uncertainties that might affect the radar measurements; (c) the accuracy of the results provided by the microwave remote sensing in terms of natural frequencies and tension forces of the stay cables; (d) the suitability of microwave interferometry to the repeated application within Structural Health Monitoring programmes.
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Detecting and localizing anomalies on masonry towers from low-cost vibration monitoring
Paolo Borlenghi,Carmelo Gentile,Antonella Saisi 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.2
The structural health of masonry towers can be monitored by installing few accelerometers (or seismometers) at the top of the building. This cost-effective setup provides continuous and reliable information on the natural frequencies of the structure and allows to detect the occurrence of structural anomalies; however, to move from anomaly detection to localization with such a simplified distribution of sensors, a calibrated numerical model is needed. The paper summarizes the development of a Structural Health Monitoring (SHM) procedure for the model-based damage assessment in masonry towers using frequency data. The proposed methodology involves the subsequent steps: (i) preliminary analysis including geometric survey and ambient vibration tests; (ii) FE modeling and updating based on the identified modal parameters; (iii) creation of a Damage Location Reference Matrix (DLRM) from numerically simulated damage scenarios; (iv) detection of the onset of damage from the analysis of the continuously collected vibration data, and (v) localization of the anomalies through the comparison between the experimentally identified variations of natural frequencies and the above-defined DLRM matrix. The proposed SHM methodology is exemplified on the ancient <i>Zuccaro</i> tower in Mantua, Italy. Pseudo-experimental monitoring data were generated and employed to assess the reliability of the developed algorithm in identifying the damage location. The results show a promise toward the practical applications of the proposed strategy for the early identification of damage in ancient towers.
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Monitoring an iconic heritage structure with OMA: the Main Spire of the Milan Cathedral
Antonello Ruccolo,Carmelo Gentile,Francesco Canali 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.2
One of the most remarkable structural elements characterizing the Milan Cathedral is its Main Spire, built in Candoglia marble and completed in 1769. The Main Spire, reaching the height of about 108 m and supporting the statue of the Virgin Mary, is about 40 m high and stands on the octagonal <i>tiburio</i> erected around the main dome. The structural arrangement of the spire includes a central column which is connected through a spiral staircase to 8 perimeter columns and each column is stiffened by inverse flying buttress. Metallic clamps and dowels connect the marble blocks and metallic rods connect the perimeter columns to the central core. A large monitoring system was recently installed in the Milan Cathedral, including seismometers and temperature sensors at 3 levels of the Main Spire as well as a weather station at the top of the spire. After a concise historic background on the Main Spire and the description of the sensing devices installed in this structure, the paper focuses on the dynamic characteristics of the spire and their evolution during a time span of about 16 months. The presented results highlight that: (a) a high density of vibration modes is automatically detected in the frequency range 1.0-7.0 Hz; (b) the lower identified modes correspond to global modes of the cathedral; (c) the normal evolution in time of the resonant frequencies is characterized by clear fluctuations induced by the environmental effects (temperature and wind); (d) especially the dependence of resonant frequencies on temperature is very distinctive and reveals the key role of the metallic elements in the overall dynamic behavior; (e) notwithstanding the remarkable effects exerted by the changing environment on the resonant frequencies, output-only removal of environmental effects and novelty analysis allow an effective monitoring of the structural condition.
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Structural novelty detection based on sparse autoencoders and control charts
Rafaelle P. Finotti,Carmelo Gentile,Flávio Barbosa,Alexandre Cury 국제구조공학회 2022 Structural Engineering and Mechanics, An Int'l Jou Vol.81 No.5
The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.
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