基于门控宽度模型的结构监测数据预测  

Structural Monitoring Data Prediction Based on G-BLS Model

作　　者：王立新[1,2] 王亚飞 杨佳宇 李储军 汪珂[1,2] 罗向龙[4] WANG Li-xin;WANG Ya-fei;YANG Jia-yu;LI Chu-jun;WANG Ke;LUO Xiang-long(China Railway First Survey and Design Institute Group Ltd.,Xi'an 710043,China;School of Civil Engineering and Architecture,Xi'an University of Technology,Xi'an 710048,China;Zhongxing Telecom Equipment Corporation,Xi'an 710111,China;School of Information Engineering,Chang an University,Xi'an 710064,China)

机构地区：[1]中铁第一勘察设计院集团有限公司,西安710043 [2]西安理工大学土木建筑工程学院,西安710048 [3]中兴通讯股份有限公司,西安710111 [4]长安大学信息工程学院,西安710064

出　　处：《科学技术与工程》2024年第18期7719-7725,共7页Science Technology and Engineering

基　　金：国家自然科学基金(52078421);陕西省自然科学基金(2022JM-056);陕西省“特支计划”青年拔尖人才(陕组通字〔2018〕33号);中铁一院科研项目(院科19-40)。

摘　　要：随着信息技术的不断发展,机器学习在结构自动化监测中的应用逐渐增长。数据分析与预测作为结构自动化监测的重要一环,是保障结构安全的关键。针对目前结构监测数据预测方法未充分挖掘数据特征和运算时间冗长的问题,提出了一种基于门控宽度模型(gated broad learning system,G-BLS)监测数据预测模型。G-BLS在BLS特征节点增加遗忘门和循环反馈门机制,能够控制特征节点提取相关性高的信息。与深度模型相比,G-BLS模型网络结构简单,在保证预测精度的同时大大减少了模型训练时间。实测的地铁基坑沉降数据测试结果表明,G-BLS可有效实现预测监测数据的可靠性与实时性,是一种精准快速的结构监测数据预测方法。With the development of information technology,the application of machine learning in automatic structure monitoring has been increasing gradually.Data analysis and prediction,as an important component of automatic structure monitoring,play a key role in ensuring structural safety.To address the issues of current structural monitoring data prediction methods,which may not fully leverage data characteristics and can be time-consuming,a monitoring data prediction model based on the gated broad learning system(G-BLS)was proposed.G-BLS can control feature nodes to extract highly relevant information by adding forgetting gates and cyclic feedback gates to feature nodes of BLS.Compared to the deep learning models,the network structure of G-BLS model is much simpler,leading to significantly reduced training times.Test results using measured subsidence data from a subway foundation pit demonstrated that G-BLS effectively achieved both reliability and real-time performance in prediction and monitoring data.This model proves to be an accurate and efficient method for structural monitoring data prediction.

关 键 词：结构变形 数据预测 宽度学习 深度学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] U231.3[自动化与计算机技术—控制科学与工程]