Complexity 2020:1-13 (2020)

Authors
Siyu Chen
University of the Arts London
Abstract
Dam behavior is difficult to predict due to its complexity. At the same time, dam deformation behavior is vital to dam systems. Developing a precise prediction model of dam deformation from prototype data is still challenging but determinant in the structural safety assessment. In this paper, an artificial neural network, trained by the improved artificial fish swarm algorithm and backpropagation algorithm, is proposed for predicting the dam deformation. Initially, crossover operator is embedded into AFSA, which aims to enhance the performance. In light of the influence mechanism of many factors on dam deformation behavior, the hybrid model uses statistical input to obtain the optimal connection weights and threshold values of the neural network. The hybrid model integrates IAFSA’s strong global searching ability and BP’s strong local search ability. To avoid overfitting the training set data, a validation set is adopted to check the generalization capability. Subsequently, the obtained optimal parameters are applied to predict the dam deformation behavior. The hybrid model’s preciseness is verified against the radial displacements of a pendulum in a concrete arch dam and simulations of four models: statistical model, BP-ANN optimized by genetic algorithm, particle swarm optimization, and AFSA. Results demonstrate that the proposed model outperforms other models and may provide alarms for safety control.
Keywords No keywords specified (fix it)
Categories (categorize this paper)
DOI 10.1155/2020/5463893
Options
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

PhilArchive copy


Upload a copy of this paper     Check publisher's policy     Papers currently archived: 59,107
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles

Predicting Books’ Overall Rating Using Artificial Neural Network.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (8):11-17.
A Proposed Artificial Neural Network for Predicting Movies Rates Category.Ibrahim M. Nasser, Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (2):21-25.
Artificial Neural Network for Predicting Animals Category.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (2):18-24.
Developing Artificial Neural Network for Predicting Mobile Phone Price Range.Ibrahim M. Nasser, Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Information Systems Research (IJAISR) 3 (2):1-6.

Analytics

Added to PP index
2020-10-29

Total views
1 ( #1,472,574 of 2,428,023 )

Recent downloads (6 months)
1 ( #511,645 of 2,428,023 )

How can I increase my downloads?

Downloads

Sorry, there are not enough data points to plot this chart.

My notes