A multi‐objective genetic algorithm strategy for robust optimal sensor placement

The performance of a monitoring system for civil buildings and infrastructures or mechanical systems depends mainly on the position of the deployed sensors. At the current state, this arrangement is chosen through optimal sensor placement (OSP) techniques that consider only the initial conditions of...

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Vydáno v:Computer-aided civil and infrastructure engineering Ročník 36; číslo 9; s. 1185 - 1202
Hlavní autoři: Civera, Marco, Pecorelli, Marica Leonarda, Ceravolo, Rosario, Surace, Cecilia, Zanotti Fragonara, Luca
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 01.09.2021
Témata:
Bell towers
Buildings
Catastrophic events
Cathedrals
Damage detection
Earthquake damage
Genetic algorithms
Initial conditions
Masonry
Mechanical systems
Optimization
Placement
Position sensing
Seismic hazard
Sensors
ISSN:1093-9687, 1467-8667
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Shrnutí:The performance of a monitoring system for civil buildings and infrastructures or mechanical systems depends mainly on the position of the deployed sensors. At the current state, this arrangement is chosen through optimal sensor placement (OSP) techniques that consider only the initial conditions of the structure. The effects of the potential damage are usually completely neglected during its design. Consequently, this sensor pattern is not guaranteed to remain optimal during the whole lifetime of the structure, especially for complex masonry buildings in high seismic hazard zones. In this paper, a novel approach based on multi‐objective optimization (MO) and genetic algorithms (GAs) is proposed for a damage scenario driven OSP strategy. The aim is to improve the robustness of the sensor configuration for damage detection after a potentially catastrophic event. The performance of this strategy is tested on the case study of the bell tower of the Santa Maria and San Giovenale Cathedral in Fossano (Italy) and compared to other classic OSP strategies and a standard GA approach with a single objective function.
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ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12646