An efficient two‐stage method for optimal sensor placement using graph‐theoretical partitioning and evolutionary algorithms. Issue 4 (21st January 2019)

• Record Type: Journal Article
• Authors: Kaveh, Ali; Dadras Eslamlou, Armin
• Is Part Of: Structural control and health monitoring. Volume 26:Issue 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-21
• Subjects: evolutionary algorithms -- graph‐theoretical partitioning -- mode shape visualization -- optimal sensor placement -- triaxial sensors; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2325
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: This paper presents a two‐stage optimal sensor placement method for modal identification of structures. At the first stage, using a graph theoretical technique, the structure is partitioned into equal substructures. At the second stage, a preset number of triaxial sensors are proportionately allocated to the substructures. The location of sensors is determined using an evolutionary optimization algorithm, which optimizes the triaxial modal assurance criterion of the structure. The first stage leads to the even distribution of the sensors. This stage not only improves the mode shape visualization as the secondary criterion but also accelerates the optimization process by space reduction. Here, various graph‐theoretical methods including k ‐means, k ‐means++, and spectral partitioning are examined as the partitioning techniques. In addition, a dynamic version for quantum‐inspired evolutionary optimization algorithm (DQEA) is proposed and applied to find the placement of triaxial sensors, along with the standard version of quantum‐inspired evolutionary algorithm and genetic algorithm. In order to examine the efficiency of the methods, the bridge model of the University of Central Florida, USA, is considered as the benchmark structure. The results show that the proposed method efficiently satisfies both criteria. Moreover, the introduced optimization algorithm (DQEA) outperforms other algorithms.