摘要
桥梁工程的结构健康监测与预警对提高桥梁的运营管理水平和安全性能至关重要。通过采用混合式系统架构,结合应变传感器、加速度传感器和光纤传感器,利用无线传感网络(Wireless Sensor Network,WSN)和有线网络,能够实现数据的实时采集与传输。引入机器学习算法,包括随机森林(Random Forest,RF)、人工神经网络(Artificial Neural Network,ANN)和支持向量机(Support Vector Machine,SVM),对监测数据进行分析与处理。在系统实施过程中,以杭州湾跨海大桥、武汉长江大桥和成都二环高架桥为案例进行了系统的实施与效果评估。结果表明,所设计的系统在监测准确性、预警及时性、系统稳定性和用户满意度等方面表现优异,能够显著提升桥梁的安全管理水平。研究可为桥梁结构的监测及预警实际应用提供理论依据和技术支持。
The structural health monitoring and early-warning system of bridge engineering are crucial for improving the operational management level and safety performance of bridges.By adopting a hybrid system architecture,combining strain sensors,accelerometers,and fiber optic sensors,utilizing both Wireless Sensor Network(WSN)and wired networks,real-time data collection and transmission can be achieved.It Introduces machine learning algorithms,including Random Forest(RF),Artificial Neural Network(ANN)and Support Vector Machine(SVM),to analyze and process the monitoring data.During system implementation,Hangzhou Bay Bridge,Wuhan Yangtze River Bridge,and Chengdu Second Ring Road Elevated Bridge were selected as case studies for system implementation and performance evaluation.Results show the designed system excels in monitoring accuracy,early-warning timeliness,system stability,and user satisfaction,can significantly enhance bridge safety management.This research provides theoretical foundation and technical support for the practical application of structural health monitoring and early-warning systems in bridge engineering.
作者
张兴
ZHANG Xing(Chengwu County Highway Development Center,Heze,Shangdong Province,274200 China)
出处
《科技资讯》
2024年第19期204-207,共4页
Science & Technology Information
关键词
桥梁工程
结构健康监测
预警系统
效果评估
Bridge engineering
Structural health monitoring
Early-warning system
Performance evaluation
