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.

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.

Optimal sensor placement for deployable antenna module health monitoring in SSPS using genetic algorithm

2017 ◽  
Vol 140 ◽  
pp. 213-224 ◽  
Author(s):  
Chen Yang ◽  
Xuepan Zhang ◽  
Xiaoqi Huang ◽  
ZhengAi Cheng ◽  
Xinghua Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Optimal Sensor Placement ◽  
Antenna Module

An Improved Genetic Algorithm for Optimal Sensor Placement in Space Structures Damage Detection

2014 ◽  
Vol 29 (3) ◽  
pp. 121-136 ◽  
Author(s):  
Sahar Beygzadeh ◽  
Eysa Salajegheh ◽  
Peyman Torkzadeh ◽  
Javad Salajegheh ◽  
Seyed Sadegh Naseralavi
Keyword(s):  
Genetic Algorithm ◽  
Damage Detection ◽  
Sensor Placement ◽  
Space Structures ◽  
Improved Genetic Algorithm ◽  
Optimal Sensor Placement

scholarly journals An Approach for Damage Identification and Optimal Sensor Placement in Structural Health Monitoring by Genetic Algorithm Technique

2016 ◽  
Vol 07 (06) ◽  
pp. 814-823 ◽  
Author(s):  
U. Muthuraman ◽  
M. M. Sai Hashita ◽  
N. Sakthieswaran ◽  
P. Suresh ◽  
M. Raj Kumar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimal Sensor Placement ◽  
Structural Health

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.

Acceleration sensor placement technique for vibration test in structural health monitoring using microhabitat frog-leaping algorithm

2017 ◽  
Vol 17 (2) ◽  
pp. 169-184 ◽  
Author(s):  
Shuo Feng ◽  
Jinqing Jia
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Optimal Placement ◽  
Vibration Test ◽  
Acceleration Sensor ◽  
Optimal Sensor Placement ◽  
Structural Health ◽  
Acceleration Sensors ◽  
Sensor Configuration

In this article, a microhabitat frog-leaping algorithm is proposed based on original shuffled frog-leaping algorithm and effective independence method to make the algorithm more efficient to optimize the 3-axis acceleration sensor configuration in the vibration test of structural health monitoring. Optimal sensor placement is a vital component of vibration test in structural health monitoring technique. Acceleration sensors should be placed such that all of the important information is collected. The resulting sensor configuration should be optimal such that the testing resources are saved. In addition, sensor configuration should be calculated automatically to facilitate engineers. However, most of the previous methods focus on the sensor placement of 1-axis sensors. Then, the 3-axis acceleration sensors are calculated by the method of 1-axis sensors, which results in non-optimal placement of many 3-axis acceleration sensors. Moreover, the calculation precisions and efficiencies of most of the previous methods cannot meet the requirement of practical engineering. In this work, the microhabitat frog-leaping algorithm is proposed to solve the optimal sensor placement problems of 3-axis acceleration sensors. The computation precision and efficiency are improved by microhabitat frog-leaping algorithm. Finally, microhabitat frog-leaping algorithm is applied and compared with other algorithms using Dalian South Bay Cross-sea Bridge.

Sensor Placement Optimization in Structural Health Monitoring Using Niching Monkey Algorithm

2014 ◽  
Vol 14 (05) ◽  
pp. 1440012 ◽  
Author(s):  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Ming Gu ◽  
Xu-Dong Zhang
Keyword(s):  
Health Monitoring ◽  
Large Scale ◽  
Sensor Placement ◽  
Population Diversity ◽  
Optimal Placement ◽  
Fitness Sharing ◽  
Sensor Placement Optimization ◽  
Design Variables ◽  
Random Initialization ◽  
Replacement Scheme

Optimal sensor placement (OSP) method plays a key role in setting up a health monitoring system for large-scale structures. This paper describes the implementation of monkey algorithm (MA) as a strategy for the optimal placement of a predefined number of sensors. To effectively maintain the population diversity while enhancing the exploitation capacities during the optimization process, a novel niching monkey algorithm (NMA) by combining the MA with the niching techniques is developed in this paper. In the NMA, the dual-structure coding method is adopted to code the design variables and a chaos-based approach instead of a pure random initialization is employed to initialize the monkey population. Meanwhile, the niche generation operation and fitness sharing mechanism are modified and incorporated to alleviate the premature convergence problem while enhancing the exploration of new search domain. In addition, to promote interactions and share the available resources, the replacement scheme is proposed and adopted among the niches. Finally, numerical experiments are conducted on a high-rise structure to evaluate the performance of the proposed NMA. It is found that the innovations in the proposed NMA can effectively improve the convergence of algorithm and generate superior sensor configurations when compared to the original MA.

scholarly journals A New Optimal Sensor Placement Strategy Based on Modified Modal Assurance Criterion and Improved Adaptive Genetic Algorithm for Structural Health Monitoring

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Can He ◽  
Jianchun Xing ◽  
Juelong Li ◽  
Qiliang Yang ◽  
Ronghao Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Adaptive Genetic Algorithm ◽  
Optimal Sensor Placement ◽  
Modal Assurance Criterion ◽  
Modal Strain Energy ◽  
Structural Health ◽  
Computation Efficiency

Optimal sensor placement (OSP) is an important part in the structural health monitoring. Due to the ability of ensuring the linear independence of the tested modal vectors, the minimum modal assurance criterion (minMAC) is considered as an effective method and is used widely. However, some defects are present in this method, such as the low modal energy and the long computation time. A new OSP method named IAGA-MMAC is presented in this study to settle the issue. First, a modified modal assurance criterion (MMAC) is proposed to improve the modal energy of the selected locations. Then, an improved adaptive genetic algorithm (IAGA), which uses the root mean square of off-diagonal elements in the MMAC matrix as the fitness function, is proposed to enhance computation efficiency. A case study of sensor placement on a numerically simulated wharf structure is provided to verify the effectiveness of the IAGA-MMAC strategy, and two different methods are used as contrast experiments. A comparison of these strategies shows that the optimal results obtained by the IAGA-MMAC method have a high modal strain energy, a quick computational speed, and small off-diagonal elements in the MMAC matrix.

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.

scholarly journals Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool

2017 ◽  
Vol 17 (3) ◽  
pp. 450-460 ◽  
Author(s):  
Austin Downey ◽  
Chao Hu ◽  
Simon Laflamme
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Sensor Network ◽  
Gene Pool ◽  
Large Scale ◽  
Sensor Placement ◽  
Combination Method ◽  
Adaptive Genetic Algorithm ◽  
Type I ◽  
Optimal Sensor Placement

This work develops optimal sensor placement within a hybrid dense sensor network used in the construction of accurate strain maps for large-scale structural components. Realization of accurate strain maps is imperative for improved strain-based fault diagnosis and prognosis health management in large-scale structures. Here, an objective function specifically formulated to reduce type I and II errors and an adaptive mutation-based genetic algorithm for the placement of sensors within the hybrid dense sensor network are introduced. The objective function is based on the linear combination method and validates sensor placement while increasing information entropy. Optimal sensor placement is achieved through a genetic algorithm that leverages the concept that not all potential sensor locations contain the same level of information. The level of information in a potential sensor location is taught to subsequent generations through updating the algorithm’s gene pool. The objective function and genetic algorithm are experimentally validated for a cantilever plate under three loading cases. Results demonstrate the capability of the learning gene pool to effectively and repeatedly find a Pareto-optimal solution faster than its non-adaptive gene pool counterpart.

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