Research on Intelligent Processing and Pattern Recognition Technology of Civil Engineering Monitoring Data

This paper investigates intelligent processing and pattern recognition techniques for civil engineering monitoring data, aiming to enhance the efficiency and accuracy of data processing to ensure project safety and prolong service life. Initially, the paper explores intelligent processing technologies, including data preprocessing, feature extraction, model establishment, and result interpretation, emphasizing the core role of pattern recognition technology in civil engineering monitoring. Subsequently, a deep learning (DL)-based data cleaning and feature extraction model is proposed, utilizing Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) to process time series data, thereby improving the usability and value of the data. Moreover, a structural damage identification method based on Support Vector Machine (SVM) is introduced, which achieves efficient recognition of structural damage in civil engineering by selecting feature subsets sensitive to structural damage. Through case studies, the application effects of the proposed models and methods in bridge health monitoring systems are verified, demonstrating that these technologies can accurately identify structural damage, providing strong support for the safety assessment and maintenance of bridges. This research not only improves the efficiency and accuracy of data processing in civil engineering monitoring but also offers scientific evidence for the safety management of civil engineering projects, showcasing broad application prospects.