Geomatics and Soft Computing Techniques for Infrastructural Monitoring

被引:7
作者
Barrile, Vincenzo [1 ]
Fotia, Antonino [1 ]
Leonardi, Giovanni [1 ]
Pucinotti, Raffaele [2 ]
机构
[1] Mediterranean Univ, DICEAM Civil Energy Environm & Mat Engn Dept, I-89124 Reggio Di Calabria, Italy
[2] Mediterranean Univ, PAU Heritage Architecture Urbanism, I-89124 Reggio Di Calabria, Italy
关键词
geomatics; neural network; sensors; bridge; viaduct; static and dynamic analysis; GPS; IDENTIFICATION; STRENGTH; SENSORS;
D O I
10.3390/su12041606
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
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页数:14
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