吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (11): 3351-3357.doi: 10.13229/j.cnki.jdxbgxb.20230783

• 计算机科学与技术 • 上一篇    

基于改进ELM-Markov Model的建筑结构稳定性监测算法

刘义艳(),刘兴,刘方方,代杰()   

  1. 长安大学 能源与电气工程学院,西安 710018
  • 收稿日期:2023-07-26 出版日期:2024-11-01 发布日期:2025-04-24
  • 通讯作者: 代杰 E-mail:lyy77111@126.com;daijiechd@chd.edu.cn
  • 作者简介:刘义艳(1981-),女,副教授,博士.研究方向:信号处理,深度学习与故障诊断,北斗定位与电力杆塔形变监测.E-mail:lyy77111@126.com
  • 基金资助:
    基金陕西省重点研发计划项目(2021GY-098);国家重点研发计划项目(2021YFB2601300)

Building structure stability monitoring algorithm based on improved ELM-Markov Model

Yi-yan LIU(),Xing LIU,Fang-fang LIU,Jie DAI()   

  1. School of Energy and Electrical Engineering,Chang'an University,Xi'an 710018,China
  • Received:2023-07-26 Online:2024-11-01 Published:2025-04-24
  • Contact: Jie DAI E-mail:lyy77111@126.com;daijiechd@chd.edu.cn

摘要:

针对结构稳定性直接影响建筑安全的问题,提出了基于改进ELM-Markov Model的建筑结构稳定性监测算法,首先,通过S变换获取建筑结构加速度信号时频图,采用灰度共生矩阵获取加速度信号时频图纹理特征,结合类内和类间散布矩阵生提取敏感特征向量;然后,结合极限学习机(ELM)和马尔科夫模型(Markov Model)构建ELM-Markov Model,对ELM的拟合误差进行Markov状态划分和误差预测,修正ELM预测值,再引入改进的灰狼算法寻优ELM-Markov Model状态数;最后,将敏感特征向量输入优化后的ELM-Markov Model中,实现建筑结构稳定性监测。实验结果表明:本文方法监测误差较小、鲁棒性较强、效率较高。

关键词: 极限学习机, 马尔科夫模型, 建筑结构, 稳定性监测, S变换

Abstract:

A building structure stability monitoring algorithm based on the improved ELM-Markov Model is proposed to address the direct impact of structural stability on building safety. Firstly, the time-frequency map of the building structure acceleration signal is obtained through the S-transform, and the texture features of the acceleration signal time-frequency map are obtained using the gray level co-occurrence matrix. Sensitive feature vectors are extracted by combining the intra class and inter class scatter matrices, Then, ELM Markov Model is constructed by combining Extreme learning machine (ELM) and Markov Model, and the fitting error of ELM is divided into Markov state and predicted by error, and the predicted value of ELM is revised. Then, the improved gray wolf algorithm is introduced to optimize the state number of ELM Markov Model. Finally, the sensitive feature vector is input into the optimized ELM Markov Model to realize the stability monitoring of building structures. The experimental results show that the proposed method has small monitoring error, strong robustness, and high efficiency.

Key words: extreme learning machine, Markov model, building structure, stability monitoring, S transformation

中图分类号: 

  • TP312

图1

灰区间白化因子寻优"

表1

梁柱截面相关参数"

参数
材质空钢管薄钢板
截面尺寸/mm25×25×2.525×4.5
体密度度/(kg·m-37 8507 850
杨氏模量/Pa206×109206×109
惯性矩/m42.17×10-82.03×10-8

图2

不同工况下建筑结构失稳程度监测结果"

图3

监测精度对比"

图4

监测效率检测结果"

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