结构地震响应预测大语言模型

The prediction of seismic responses in structural engineering is a critical component of building assessment, particularly in performance-based seismic engineering. This paper focuses on scenarios with limited data samples in seismic response prediction and proposes a method termed LLM-PaP (large language model with prompt-as-prefix). This method leverages the general analytical capabilities of large language models (LLMs) for time-series data to address performance deficiencies typically encountered by conventional models under small sample conditions. The model integrates the“PaP (prompt-as-prefix)”concept, enhancing the understanding of input sequences by incorporating natural language task instructions and statistical information of seismic input sequence data to guide the reasoning and prediction process. Experimental validation conducted on two datasets demonstrates the effectiveness of the proposed method. The results indicate that LLM-PaP significantly outperforms advanced prediction methods based on MLP, frequency domain analysis, and Transformer models in terms of predictive performance on the datasets. Additional experiments on generalization reveal the superior adaptability of LLM-PaP across datasets. LLM-PaP presents an innovative solution for seismic response prediction tasks, offering new insights and methods for the interdisciplinary research at the large models and seismic response prediction in the future.