同济大学学报（自然科学版）2025，Vol.53Issue(12)：1837-1847,11.DOI:10.11908/j.issn.0253-374x.24282
基于集成神经网络的超高层结构异常监测数据诊断
Abnormal Monitoring Data Diagnosis for Super High-Rise Structures Based on Ensemble Neural Network
摘要
Abstract
To address the inefficiency in manual identification of diverse anomalies in structural health monitoring(SHM)data,a method based on ensemble learning model is proposed to detect the abnormal data in super high-rise building SHM systems.By employing short-time Fourier transform,the time-frequency domain information containing the main structural vibrational modes is extracted and compressed,thus the feature extraction and high-fidelity compression of original data attained.Moreover,a Bagging ensemble strategy is introduced,and multiple training subsets are generated through bootstrap sampling,based on which each individual neural network model is trained independently.By aggregating the prediction results of multiple well-trained models,the precision of anomaly detection is enhanced.Furthermore,the proposed method is applied into the Shanghai Tower SHM system to validate the feasibility and reliability.The results indicate that the diagnosis accuracy reaches 98.8%by the proposed ensemble model-based abnormal data detection method,and high precision and strong robustness of the anomaly SHM data diagnosis are confirmed.关键词
结构健康监测/超高层结构/自编码器神经网络/集成学习/异常数据识别Key words
structural health monitoring(SHM)/super high-rise building/autoencoder neural network/ensemble learning/abnormal data detection分类
建筑与水利引用本文复制引用
秦宁宇,吴杰,张其林..基于集成神经网络的超高层结构异常监测数据诊断[J].同济大学学报（自然科学版）,2025,53(12):1837-1847,11.基金项目
国家重点研发计划(2023YFC3805700) （2023YFC3805700）
