Localization of low velocity impacts on CFRP laminates based on FBG sensors and BP neural networks

Wen, Xianglong; Sun, Quanzhi; Wen-hu, LI; Ding, Guoping; Song, Chunsheng; Zhang, Jinguang

doi:10.1080/15376494.2021.1956653

Cited by 13 publications

(7 citation statements)

References 24 publications

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“…This research [17] investigates the application of deep learning techniques, such as convolutional neural networks and recurrent neural networks, for building foundation damage identification. It explores the use of deep learning models to analyze sensor data and automatically detect patterns indicative of structural damage.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification and Repair of Structural Damage of Building Foundations Based on Genetic Algorithm

Yachun Hu

2024

jes

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The identification and repair of structural damage in building foundations are critical tasks in ensuring the safety and integrity of civil infrastructure. This research paper proposes a method based on genetic algorithms for the identification and repair of structural damage in building foundations. The proposed approach begins with the identification of structural damage by analysing sensor data, including vibration measurements and strain gauges, obtained from the building's foundation. A genetic algorithm is then employed to optimize the identification process by iteratively searching for the most likely damage scenarios based on the collected sensor data. Once the damage is identified, the algorithm proceeds to develop an optimal repair strategy. It considers various parameters, such as available repair materials, budget constraints, and desired structural performance, to generate a repair plan that minimizes cost and maximizes the restoration of structural integrity. The repair plan is optimized using the genetic algorithm to find the best combination of repair techniques and materials. To evaluate the effectiveness of the proposed method, extensive simulations and case studies are conducted on different types of building foundations. The performance of the algorithm is assessed in terms of its accuracy in identifying structural damage and the efficiency of the repair strategy.

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Section: Literature Reviewmentioning

confidence: 99%

Identification and Repair of Structural Damage of Building Foundations Based on Genetic Algorithm

Yachun Hu

2024

jes

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“…The combination of methods can effectively improve the model's fault location ability. In testing experiments, the optimized new model significantly improved the accuracy of fault location and prediction after training, and it effectively reduced the positioning error [15]. In the fault identification of mechanical components, the combination of the BPNN and GA can be used for the fault identification of mechanical components.…”

Section: Related Workmentioning

confidence: 99%

Fault Location of Distribution Network Based on Back Propagation Neural Network Optimization Algorithm

et al. 2023

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Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. To improve the accuracy of dual fault diagnosis in the DN, this study optimizes BPNN by combining the genetic algorithm (GA) and cloud theory. The two types of BPNN before and after optimization are used for single fault and dual fault diagnosis of the DN, respectively. The experimental results show that the optimized BPNN has certain effectiveness and stability. The optimized BPNN requires 25.65 ms of runtime and 365 simulation steps. And in diagnosis and positioning of dual faults, the optimized BPNN exhibits a higher fault diagnosis rate, with an accuracy of 89%. In comparison to ROC curves, the optimized BPNN has a larger area under the curve and its curve is smoother. The results confirm that the optimized BPNN has high efficiency and accuracy.

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“…Impact localization can be achieved by measuring the flight time of ultrasonic Lamb waves [ 24 , 25 ], using reference database-based impact localization algorithms [ 26 , 27 ], or utilizing strain amplitude [ 28 ] and other techniques. Xianglong Wen et al [ 29 ] optimized the structure of the FBG sensing network, extracted feature vectors from impact signals, and achieved an average positioning error of 2.1 cm using a backpropagation (BP) neural network model. Pratik Shrestha et al [ 30 ] proposed and implemented an algorithm based on error outliers, achieving an average prediction error of 10.7 mm for impact points on composite panels.…”

Section: Introductionmentioning

confidence: 99%

Low Velocity Impact Monitoring of Composite Tubes Based on FBG Sensors

Huan,

Lu,

Shen

et al. 2024

Sensors

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Carbon fiber reinforced composites (CFRP) are susceptible to hidden damage from low velocity external impacts during their service life. To ensure the proper monitoring of the state of the composites, it is crucial to predict the location of an impact event. In this paper, fiber Bragg grating (FBG) sensors are affixed to the surface of a carbon fiber composite tube, and an optical sensing interrogator is used to capture the central wavelength shift of the FBG sensors due to low-velocity impacts. A discrete wavelet transform is used for noise reduction in the response signals. Then, the differences in the captured response signals of the FBG sensors at different locations of the impact were analyzed. Moreover, two methods were implemented to predict the location of low-velocity impacts, according to the differences in the captured response signals. The BP neural network-based method utilized three data sets to train the neural network, resulting in an average localization error of 20.68 mm. In contrast, the method based on error outliers selected a specific data set as the reference dataset, achieving an average localization error of 13.98 mm. The comparison of the predicted results shows that the latter approach has a higher predictive accuracy and does not require a significant amount of data.

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Localization of low velocity impacts on CFRP laminates based on FBG sensors and BP neural networks

Cited by 13 publications

References 24 publications

Identification and Repair of Structural Damage of Building Foundations Based on Genetic Algorithm

Identification and Repair of Structural Damage of Building Foundations Based on Genetic Algorithm

Fault Location of Distribution Network Based on Back Propagation Neural Network Optimization Algorithm

Low Velocity Impact Monitoring of Composite Tubes Based on FBG Sensors

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