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 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.

scholarly journals A Multiobjective Perspective to Optimal Sensor Placement by Using a Decomposition-Based Evolutionary Algorithm in Structural Health Monitoring

2020 ◽  
Vol 10 (21) ◽  
pp. 7710
Author(s):  
Tsung-Yueh Lin ◽  
Jin Tao ◽  
Hsin-Haou Huang
Keyword(s):  
Structural Health Monitoring ◽  
Evolutionary Algorithm ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Frame Structure ◽  
Mode Shapes ◽  
Beam Model ◽  
Optimal Sensor Placement ◽  
Modal Assurance Criterion ◽  
Structural Health

The objective of optimal sensor placement in a dynamic system is to obtain a sensor layout that provides as much information as possible for structural health monitoring (SHM). Whereas most studies use only one modal assurance criterion for SHM, this work considers two additional metrics, signal redundancy and noise ratio, combining into three optimization objectives: Linear independence of mode shapes, dynamic information redundancy, and vibration response signal strength. A modified multiobjective evolutionary algorithm was combined with particle swarm optimization to explore the optimal solution sets. In the final determination, a multiobjective decision-making (MODM) strategy based on distance measurement was used to optimize the aforementioned objectives. We applied it to a reduced finite-element beam model of a reference building and compared it with other selection methods. The results indicated that MODM suitably balanced the objective functions and outperformed the compared methods. We further constructed a three-story frame structure for experimentally validating the effectiveness of the proposed algorithm. The results indicated that complete structural modal information can be effectively obtained by applying the MODM approach to identify sensor locations.

An optimal sensor placement design framework for structural health monitoring using Bayes risk

2022 ◽  
Vol 168 ◽  
pp. 108618
Author(s):  
Yichao Yang ◽  
Mayank Chadha ◽  
Zhen Hu ◽  
Michael D. Todd
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Design Framework ◽  
Bayes Risk ◽  
Optimal Sensor Placement ◽  
Structural Health

scholarly journals Evaluation of optimal sensor placement algorithms for the Structural Health Monitoring of architectural heritage. Application to the Monastery of San Jerónimo de Buenavista (Seville, Spain)

2020 ◽  
Vol 202 ◽  
pp. 109843
Author(s):  
Pablo Pachón ◽  
María Infantes ◽  
Margarita Cámara ◽  
Víctor Compán ◽  
Enrique García-Macías ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Architectural Heritage ◽  
Optimal Sensor Placement ◽  
Structural Health ◽  
Placement Algorithms
Sign in / Sign up

Export Citation Format

Share Document