Structural health monitoring (SHM), together with condition monitoring (CM), nondestructive evaluation (NDE), statistical process control (SPC), and damage prognosis (DP), through the most recent techniques of survey and data processing, allows to identify, evaluate, and monitor with ever-greater clarity the structural characteristics and the level of damage of any building and, therefore, to predict its trend over time. The use of traditional and experimental sensor networks and the processing of the data obtained from them allow to identify anomalies in the behavior of structures in operation, as well as to implement early warning systems. The use of accelerometric sensors is helpful for identifying the representative parameters of the structural behavior; the measurements of the displacements, on the other hand, allow a quick estimate of the magnitude strictly correlated to any damage suffered by the structure during a seismic event or a failure. In this work we try to reach the last three steps of the hierarchical structures proposed by Ritter, which are remembered to be damage location, damage assessment, and prediction. To obtain these levels, it is necessary to combine all the analyzes of the simple SHM that leads to the sending of an alarm, to a cognitive capacity of the building, also achieved with the use of artificial intelligence. In particular, the connection of SHM with AI and with building information modeling (BIM) can make the system cognitive, making it capable of managing (e.g., ensure, predict, assess) the healthiness of a building. The article also presents a case study to highlight how the proposed methodology is applicable to concrete cases.

Structural Health Monitoring in Cognitive Buildings

Zinno R.;Guido G.;Salvo F.;Artese S.;De Ruggiero M.;Vitale A.;Gentile A. F.
2023-01-01

Abstract

Structural health monitoring (SHM), together with condition monitoring (CM), nondestructive evaluation (NDE), statistical process control (SPC), and damage prognosis (DP), through the most recent techniques of survey and data processing, allows to identify, evaluate, and monitor with ever-greater clarity the structural characteristics and the level of damage of any building and, therefore, to predict its trend over time. The use of traditional and experimental sensor networks and the processing of the data obtained from them allow to identify anomalies in the behavior of structures in operation, as well as to implement early warning systems. The use of accelerometric sensors is helpful for identifying the representative parameters of the structural behavior; the measurements of the displacements, on the other hand, allow a quick estimate of the magnitude strictly correlated to any damage suffered by the structure during a seismic event or a failure. In this work we try to reach the last three steps of the hierarchical structures proposed by Ritter, which are remembered to be damage location, damage assessment, and prediction. To obtain these levels, it is necessary to combine all the analyzes of the simple SHM that leads to the sending of an alarm, to a cognitive capacity of the building, also achieved with the use of artificial intelligence. In particular, the connection of SHM with AI and with building information modeling (BIM) can make the system cognitive, making it capable of managing (e.g., ensure, predict, assess) the healthiness of a building. The article also presents a case study to highlight how the proposed methodology is applicable to concrete cases.
Cognitive buildings
Damage detection
Monitoring techniques
Structural health monitoring (SHM)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/341389
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