基于门控宽度模型的结构监测数据预测

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。

Structural Monitoring Data Prediction Based on G-BLS Model

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) is 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.