Weighted Statistical Model of Dam Monitoring Based on Improved Particle Swarm Optimization Algorithm

WANG Wei, XU Kai, FANG Xu-shun, ZHONG Qi-ming

Journal of Changjiang River Scientific Research Institute ›› 2017, Vol. 34 ›› Issue (8) : 41-46.

PDF(2175 KB)
PDF(2175 KB)
Journal of Changjiang River Scientific Research Institute ›› 2017, Vol. 34 ›› Issue (8) : 41-46. DOI: 10.11988/ckyyb.20160419
ENGINEERING SAFETY AND DISASTER PREVENTION

Weighted Statistical Model of Dam Monitoring Based on Improved Particle Swarm Optimization Algorithm

  • WANG Wei, XU Kai, FANG Xu-shun, ZHONG Qi-ming
Author information +
History +

Abstract

The weights of all factors in weighted statistical model of dam monitoring were determined with engineering experience, which could result in the lack of the information of some factors. According to monitoring data, the regression coefficients and weights of weighted statistical model can be objectively determined by Particle Swarm Optimization algorithm, but for high dimension optimization, the algorithm has some deficiencies such as slow convergence and local minimums. In view of this, an improved Particle Swarm Optimization algorithm in consideration of the information of average location in particles is proposed. The learning factors are determined based on the information of average location in single particle and particle groups. The analysis results of earth-rock dam example show that the improved Particle Swarm Optimization algorithm enhances the ability of jumping out of the local minimum. The factors of weighted statistical model of safety monitoring for earth-rock dam are consistent in actual situation with this improved algorithm. Especially in the early stages of operation with few monitoring data, dam monitoring model based on improved Particle Swarm Optimization algorithm has better precision. The improved algorithm could be a new method of data analysis in dam monitoring field.

Key words

earth-rock dam / weighted statistical model / improved Particle Swarm Optimization algorithm / optimization computation / weight coefficient

Cite this article

Download Citations
WANG Wei, XU Kai, FANG Xu-shun, ZHONG Qi-ming. Weighted Statistical Model of Dam Monitoring Based on Improved Particle Swarm Optimization Algorithm[J]. Journal of Changjiang River Scientific Research Institute. 2017, 34(8): 41-46 https://doi.org/10.11988/ckyyb.20160419

References

[1] 孙振刚,张 岚,段中德.我国水库工程数量及分布[J]. 中国水利,2013,(7):10-11.
[2] 沈 毅,郭金运,周 俊,等. 基于灰色模型及其改进模型的土石坝沉降预测[J]. 山东理工大学学报(自然科学版), 2014, 28(1):6-9.
[3] 陈维江,马震岳,董毓新. 建立大坝安全监控数学模型的一种新方法[J]. 水利学报,2002,33(8):91-95.
[4] 刘东海,高 歌. 基于遗传和声算法并考虑实际压实质量的土石坝沉降预测模型[J]. 水电能源科学,2014,32(5):47-50.
[5] YANG Xue-ming, YUAN Jiang-ye, YUAN Jin-sha, et al . An Improved WM Method Based on PSO for Electric Load Forecasting[J]. Expert Systems with Applications, 2010,37(12): 8036-8041.
[6] ALRASHIDI M R, EL-NAGGAR K M. Long Term Electric Load Forecasting Based on Particle Swarm Optimization[J]. Applied Energy, 2010,87(1):320-326.
[7] 罗润林,阮怀宁,黄亚哲,等.岩体初始地应力场的粒子群优化反演及在FLAC 3D 中的实现[J]. raybet体育在线 院报, 2008,25(4): 73-76.
[8] 张 磊,金永强,李子阳,等.CPSO-NN模型在大坝安全监控中的应用[J].水利水电科技进展,2008,28(4):8-10.
[9] 吕蓓蓓,杨远斐. PSO-RBF在大坝变形监测中的应用[J].水电能源科学,2012, 30(8):77-79.
[10] 范振东,崔伟杰,陈 敏,等. 基于IPSO-RVM的大坝安全预警模型[J]. raybet体育在线 院报,2016,33(2):48-51.
[11] BEHNAMIAN J, GHOMI S M T F. Development of a PSO-SA Hybrid Metaheuristic for a New Comprehensive Regression Model to Time-Series Forecasting[J]. Expert Systems with Applications, 2010, 37(2):974-984.
[12] 安丽霞,张彩珍,侯志伟,等. 具有动态调节机制的多粒子群改进算法及应用[J]. 兰州交通大学学报,2015,34(1):71-76.
[13] 顾冲时,吴中如. 大坝与坝基安全监控理论和方法及其应用[M]. 南京:河海大学出版社,2006.
[14] LAZZÚS J A. Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization[J]. International Journal of Thermophysics, 2011,32(5): 957-973.
[15] 王 伟,沈振中. 大坝统计预警模型的改进粒子群耦合方法[J]. 武汉大学学报(信息科学版),2009,34(8):987-991.

Funding

国家自然科学基金项目(51379129); 水利部公益性行业科研经费项目(sg315002)
PDF(2175 KB)

Accesses

Citation

Detail

Sections
Recommended

/

Baidu
map