Optimal sensor placement on a large-scale civil structure

2004 ◽  
Author(s):  
Michele Meo ◽  
Giuseppe Zumpano
Keyword(s):  
Large Scale ◽  
Sensor Placement ◽  
Optimal Sensor Placement

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

2020 ◽  
pp. 136943322094719
Author(s):  
Xianrong Qin ◽  
Pengming Zhan ◽  
Chuanqiang Yu ◽  
Qing Zhang ◽  
Yuantao Sun
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Complex Structure ◽  
Sensor Placement ◽  
Placement Problem ◽  
Optimal Sensor Placement ◽  
Placement Optimization ◽  
Sensor Placement Optimization ◽  
Sensor Locations

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

scholarly journals Optimal Sensor Placement for Health Monitoring of High-Rise Structure Based on Genetic Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Ming Gu
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Sensor Placement ◽  
Optimal Placement ◽  
Improved Genetic Algorithm ◽  
Optimal Sensor Placement ◽  
Selection Scheme ◽  
Dual Structure ◽  
Coding Method

Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale structures. Based on the criterion of the OSP for the modal test, an improved genetic algorithm, called “generalized genetic algorithm (GGA)”, is adopted to find the optimal placement of sensors. The dual-structure coding method instead of binary coding method is proposed to code the solution. Accordingly, the dual-structure coding-based selection scheme, crossover strategy and mutation mechanism are given in detail. The tallest building in the north of China is implemented to demonstrate the feasibility and effectiveness of the GGA. The sensor placements obtained by the GGA are compared with those by exiting genetic algorithm, which shows that the GGA can improve the convergence of the algorithm and get the better placement scheme.

Modified Monkey Algorithm and its Application to the Optimal Sensor Placement

2012 ◽  
Vol 178-181 ◽  
pp. 2699-2702 ◽  
Author(s):  
Ting Hua Yi ◽  
Xu Dong Zhang ◽  
Hong Nan Li
Keyword(s):  
Large Scale ◽  
Hamming Distance ◽  
Harmony Search ◽  
Sensor Placement ◽  
Random Disturbance ◽  
Intelligent Algorithm ◽  
Optimal Sensor Placement ◽  
Large Scale Structures ◽  
Initial Location ◽  
Scale Structures

Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale structures. In this paper, an optimal sensor placement (OSP) method based on a new intelligent algorithm, i.e., monkey algorithm (MA) is proposed. Considering the limitation of MA and the characteristics of OSP, the integer encoding to solve the location of the monkeys is proposed. The diversity of the monkeys is increased by introducing the hamming distance in the initial location, in order to improve the capacity of global search, and the random disturbance mechanism of the Harmony search is introduced in the process of climbing to improve the capacity of local search. Finally, taking the Dalian World Trade Building as an example, the OSP schemes are chosen. The numerical example demonstrated the feasibility and effectiveness of the proposed method.

scholarly journals Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xun Zhang ◽  
Juelong Li ◽  
Jianchun Xing ◽  
Ping Wang ◽  
Qiliang Yang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Sensor Placement ◽  
Particle Swarm ◽  
Optimal Placement ◽  
Optimal Sensor Placement ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Comparison Results ◽  
Ipso Algorithm

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

Optimal Sensor Placement Using Chaotic Monkey Search Algorithm

2015 ◽  
Author(s):  
Fuli Zhang ◽  
Olga Brezhneva ◽  
Amit Shukla
Keyword(s):  
Health Monitoring ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
National Laboratory ◽  
Sensor Placement ◽  
Uncertainty Modeling ◽  
Step Length ◽  
Placement Problem ◽  
Optimal Sensor Placement

The optimal sensor placement (OSP) problem is integral to modern large scale structures for their health monitoring. Evolutionary algorithms for the OSP problem are attractive as they can result in global optima without gradient information. In this paper, a modification of the Monkey Algorithm with a chaotic search strategy and adaptive parameters is proposed. It includes chaotic initialization, variable search step length, and adaptive watching time. The performance of the proposed chaotic Monkey Algorithm (cMA) is compared with the original Monkey Algorithm. Convergence property of cMA is established. The proposed method is applied to an optimal sensor placement problem for structural health monitoring. The OSP problem is solved for a mass-spring-damper system and then for a model of the I-40 bridge developed by the Los Alamos National Laboratory. Numerical results demonstrate that the proposed Chaotic Monkey Algorithm has capability of solving mixed-variable optimization problems and that it performs better than the originally proposed Monkey algorithm. Finally, nonparametric uncertainty modeling is used to evaluate variability in a model and its effect on the optimal sensor placement.

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