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Open AccessArticle

Geomatics and Soft Computing Techniques for Infrastructural Monitoring

1
DICEAM—Civil, Energy, Environmental and Material, Engineering Department, Mediterranean University, 89124 Reggio Calabria, Italy
2
PAU—Heritage-Architecture-Urbanism, Mediterranean University, 89124 Reggio Calabria, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(4), 1606; https://doi.org/10.3390/su12041606
Received: 5 December 2019 / Revised: 5 February 2020 / Accepted: 11 February 2020 / Published: 21 February 2020
(This article belongs to the Special Issue Towards Sustainable Engineering: New Technologies and Methodologies)
Structural Health Monitoring (SHM) allows us to have information about the structure under investigation and thus to create analytical models for the assessment of its state or structural behavior. Exceeded a predetermined danger threshold, the possibility of an early warning would allow us, on the one hand, to suspend risky activities and, on the other, to reduce maintenance costs. The system proposed in this paper represents an integration of multiple traditional systems that integrate data of a different nature (used in the preventive phase to define the various behavior scenarios on the structural model), and then reworking them through machine learning techniques, in order to obtain values to compare with limit thresholds. The risk level depends on several variables, specifically, the paper wants to evaluate the possibility of predicting the structure behavior monitoring only displacement data, transmitted through an experimental transmission control unit. In order to monitor and to make our cities more “sustainable”, the paper describes some tests on road infrastructure, in this contest through the combination of geomatics techniques and soft computing. View Full-Text
Keywords: geomatics; neural network; sensors; bridge; viaduct; static and dynamic analysis geomatics; neural network; sensors; bridge; viaduct; static and dynamic analysis
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Barrile, V.; Fotia, A.; Leonardi, G.; Pucinotti, R. Geomatics and Soft Computing Techniques for Infrastructural Monitoring. Sustainability 2020, 12, 1606.

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