Next Article in Journal
Did Haze Pollution Harm the Quality of Economic Development?—An Empirical Study Based on China’s PM2.5 Concentrations
Next Article in Special Issue
Finite Element Analysis of Geogrid-Stabilized Unpaved Roads
Previous Article in Journal
Reviewing Neighborhood Sustainability Assessment Tools through Critical Heritage Studies
Previous Article in Special Issue
Planning and Simulation of Intermodal Freight Transport on International Networks. Hub and Spoke System in Euro-Mediterranean Area
Open AccessArticle

Geomatics and Soft Computing Techniques for Infrastructural Monitoring

DICEAM—Civil, Energy, Environmental and Material, Engineering Department, Mediterranean University, 89124 Reggio Calabria, Italy
PAU—Heritage-Architecture-Urbanism, Mediterranean University, 89124 Reggio Calabria, Italy
Author to whom correspondence should be addressed.
Sustainability 2020, 12(4), 1606;
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
Show Figures

Figure 1

MDPI and ACS Style

Barrile, V.; Fotia, A.; Leonardi, G.; Pucinotti, R. Geomatics and Soft Computing Techniques for Infrastructural Monitoring. Sustainability 2020, 12, 1606.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop