Testa, Giusiana (2023) Safety of existing bridges Traffic load models for accurate safety checks and methods for damage detection. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Safety of existing bridges Traffic load models for accurate safety checks and methods for damage detection |
| Creators: | Creators Email Testa, Giusiana giusiana.testa@unina.it |
| Date: | 9 October 2023 |
| Number of Pages: | 152 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Civile, Edile e Ambientale |
| Dottorato: | Ingegneria strutturale, geotecnica e rischio sismico |
| Ciclo di dottorato: | 35 |
| Coordinatore del Corso di dottorato: | nome email Iervolino, Iunio iunio.iervolino@unina.it |
| Tutor: | nome email Bilotta, Antonio UNSPECIFIED Iervolino, Iunio UNSPECIFIED |
| Date: | 9 October 2023 |
| Number of Pages: | 152 |
| Keywords: | traffic load, micro-traffic simulations, dynamic identification, optimal sensor placement, artificial neural networks, anomaly detection |
| Settori scientifico-disciplinari del MIUR: | Area 08 - Ingegneria civile e Architettura > ICAR/09 - Tecnica delle costruzioni |
| Date Deposited: | 13 Oct 2023 11:40 |
| Last Modified: | 09 Apr 2025 13:18 |
| URI: | http://www.fedoa.unina.it/id/eprint/14999 |
Collection description
The calibration of traffic loads based on real traffic data of the road network and health monitoring system are two useful tools for the assessment of their safety. Therefore, in this thesis (i) the innovative data processing techniques aimed at structural monitoring and structural damage detection are showed and compared and (ii) a methodology for calibrating traffic loads based on traffic micro-simulations is discussed. Regarding structural monitoring, a dynamic identification technique namely Frequency Domain Decomposition (FDD) is described. The use of this technique allows to detect the dynamic properties of structures in terms of vibration frequencies and modal shapes. The characterization of the dynamic behavior of structures is aimed at: (i) detecting any anomalies that may be indicative of a damage condition and (ii) updating the FE model. In order to fully characterize the dynamic behavior of structures with the smallest number of sensors, it is necessary to adequately design the sensor network. In fact, the accuracy of dynamic identification results depends on the number and location of sensors placed on the structure. Therefore, optimal sensor placement (OSP) techniques for the accurate design of the accelerometer network are described. Since the accuracy of the dynamic identification depends on the number and position of the sensors placed on the structure. The main OSP methods provide an efficiency ranking for the possible positions of an accelerometer. However, a selection criterion for the optimal sensors number is not provided. In this thesis, a criterion is firstly proposed to minimize the number of sensors and optimize their position using the efficiency ranking of the Effective Independent Method. The methodology is applied to the prestressed railway bridge of Circumflegrea of Naples. Moreover, the results obtainable through FDD are compared with the adoption of artificial intelligence algorithms, i.e., an unsupervised data driven methodology. The latter is developed by combining a Variational Autoencoder and a One Class - Support Vector Machine. This method is applied to two case studies, i.e., a steel structure and prestressed concrete joists. Finally, the methodology for probabilistically characterizing the traffic loads on bridges based on network-level traffic micro-simulation is described through application to the A56, that is the urban highway that connects the districts of Naples (Italy).
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