Journal
SUSTAINABILITY
Volume 12, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/su12041606
Keywords
geomatics; neural network; sensors; bridge; viaduct; static and dynamic analysis
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available