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Smart Structures and Systems
  Volume 15, Number 1, January 2015, pages 191-207
DOI: http://dx.doi.org/10.12989/sss.2015.15.1.191
 


Sensor placement optimization in structural health monitoring using distributed monkey algorithm
Ting-Hua Yi, Hong-Nan Li and Xu-Dong Zhang

 
Abstract
    Proper placement of sensors plays a key role in construction and implementation of an effective structural health monitoring (SHM) system. This paper proposes a novel methodology called the distributed monkey algorithm (DMA) for the optimum design of SHM system sensor arrays. Different from the existing algorithms, the dual-structure coding method is adopted for the representation of design variables and the single large population is partitioned into subsets and each subpopulation searches the space in different directions separately, leading to quicker convergence and higher searching capability. After the personal areas of all subpopulations have been finished, the initial optimal solutions in every subpopulation are extracted and reordered into a new subpopulation, and the harmony search algorithm (HSA) is incorporated to find the final optimal solution. A computational case of a high-rise building has been implemented to demonstrate the effectiveness of the proposed method. Investigations have clearly suggested that the proposed DMA is simple in concept, few in parameters, easy in implementation, and could generate sensor configurations superior to other conventional algorithms both in terms of generating optimal solutions as well as faster convergence.
 
Key Words
    structural health monitoring; optimal sensor placement; distributed monkey algorithm; harmony search algorithm; modal assurance criterion
 
Address
Ting-Hua Yi: School of Civil Engineering, Dalian University of Technology, Dalian 116023, P.R. China;
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116023, P.R. China
Hong-Nan Li and Xu-Dong Zhang: School of Civil Engineering, Dalian University of Technology, Dalian 116023, P.R. China
 

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