Adaptive Sensor Network and Intelligent Data Processing System for Structural Health Monitoring of High-Rise Buildings

Abstract

Traditional sensor networks are often designed based on fixed layouts and are difficult to adapt to changes in structural states. Adaptive sensor networks can intelligently adjust the position, quantity, and monitoring frequency of sensors based on real-time monitoring data, environmental changes, and structural responses, achieving precise monitoring of key areas or potential risk points. This dynamic adjustment mechanism significantly improves monitoring efficiency and accuracy. Faced with massive monitoring data, the intelligent data processing system adopts big data processing technology and machine learning algorithms to automatically identify abnormal signals, predict the trend of structural performance degradation, and timely detect potential structural damage. By training the model, the system can continuously learn and optimize its predictive ability, improving evaluation accuracy. Utilize the powerful computing power and storage space of cloud computing platforms to centrally process and analyze massive amounts of data; At the same time, edge computing nodes are deployed at the sensor end to achieve preliminary data processing and real-time analysis, reduce data transmission delay and improve system response speed. Develop a user-friendly visual interface to convert complex monitoring data into intuitive charts, animations, and other forms, facilitating engineers and managers to quickly understand the structural health status. At the same time, provide decision support recommendations based on data analysis to guide structural maintenance, reinforcement, or renovation work. Adaptive sensor networks can dynamically adjust the deployment and working state of sensors according to environmental changes and monitoring requirements, which improves the flexibility and accuracy of monitoring systems. Intelligent data processing systems utilize advanced algorithms, such as machine learning and pattern recognition, to conduct in-depth analysis of the data, enabling early warning and diagnosis of potential problems with the structure. It provides reference for the establishment of structural health monitoring system of high-rise buildings.