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IoT-Based Sensor Nodes for Structural Health Monitoring of Bridges

Authors :
Remigio Guevara
José Luis Molina
M.A. Muñoz
Darwin Zhagñay
Santiago González
Jaime E. Garcia
Source :
Artificial Intelligence, Computer and Software Engineering Advances ISBN: 9783030680794
Publication Year :
2021
Publisher :
Springer International Publishing, 2021.

Abstract

The road infrastructure represents a strategic area for the planification and development of urban and rural environments. In this context, essential facilities such as bridges are subject to damages mainly due to the daily load that represents the vehicular traffic, the environmental conditions and also due to the natural degradation of the structural elements. Therefore, it is fundamental performs a continuous evaluation about the state of an infrastructure. Following, this purpose, this paper presents an architecture for the Structural Health Monitoring (SHM) focused on bridges. The proposal solution involves the application of emerging technologies such as MEMS accelerometers, IoT devices and the deployment of a Wireless Sensor Network topology. Particularly, the architecture implemented consists of a set of ten sensor nodes which are management and synchronized by means of a central station. Furthermore, an additional node with capacity of capture and video recording was implemented Therefore, this last node provides to the system with a visual option for identifying the connection between the accelerations values and the vehicular traffic. In order to evaluated the system, the architecture was deployed along a bridge characterized by a heavy traffic load. Results on the scenario reveals the system allows for determining the main parameters related to state of the structure (i.e., acceleration values, load distribution and the fundamental frequencies of vibration). Consequently, the proposal architecture contributes significantly in the development of solutions focused in the analysis and early detection of damages on essentials structures or facilities.

Details

ISBN :
978-3-030-68079-4
ISBNs :
9783030680794
Database :
OpenAIRE
Journal :
Artificial Intelligence, Computer and Software Engineering Advances ISBN: 9783030680794
Accession number :
edsair.doi...........123b1af9af3391b33e493137d7ec3f85
Full Text :
https://doi.org/10.1007/978-3-030-68080-0_20