基于LSTM_eKan模型的建筑结构安全监测研究

随着建筑结构日益复杂和规模不断扩大,结构安全监测在保障工程安全和延长结构使用寿命方面变得至关重要。该文提出一种基于LSTM_eKan深度学习模型的综合评估方法,用于监测和评估工程结构的安全状况。研究的核心在于开发和优化一套高效的数据预处理技术及预测模型,以提高监测数据的准确性和可靠性。LSTM_eKan模型通过引入注意力机制,能够更加精准地捕捉时间序列数据中的关键特征,减少冗余信息的干扰,从而大幅提升预测的精度与稳定性。与传统方法相比,LSTM_eKan在结构安全监测任务中展现显著的优势。

With the increasing complexity and scale of building structures,structural safety monitoring has become crucial for ensuring engineering safety and extending the lifespan of structures.This paper proposes a comprehensive evaluation method based on the LSTM_eKan deep learning model,aimed at monitoring and assessing the safety condition of engineering structures.The core of the study focuses on developing and optimizing an efficient data preprocessing technique and predictive model to enhance the accuracy and reliability of monitoring data.By introducing an attention mechanism,the LSTM_eKan model is able to capture key features in time-series data more effectively,reducing interference from redundant information.This significantly improves the precision and stability of the predictions.Compared to traditional methods,LSTM_eKan demonstrates notable advantages in structural safety monitoring tasks.