A Data-Driven and Explainable AI Framework for Quantitative Analysis of Research Trends in Timber Seismic Engineering

This study presents a data-driven and explainable artificial intelligence (XAI) framework for quantitatively analyzing research trends in the seismic performance of timber structures. Unlike conventional bibliometric approaches based on descriptive statistics, the framework integrates large-scale literature mining, natural language processing, topic modeling, network analysis, and SHAP-based machine learning to enable structural and temporal interpretation. A dataset of 248 journal articles from OpenAlex was processed through a unified pipeline, including domain-specific filtering, text preprocessing, and temporal balancing. Topic modeling identified eight research themes spanning traditional component-level mechanics and emerging areas such as cross-laminated timber (CLT), hybrid systems, and performance-based design. Network analysis revealed a highly interconnected structure centered on key concepts such as shear walls, connections, stiffness, and cyclic behavior. SHAP-based analysis further showed that research evolution follows a layered and cumulative pattern rather than simple topic replacement: classical themes remain foundational, while newer concepts such as CLT and structural capacity have become increasingly influential. The proposed framework provides a reproducible and scalable method for quantitatively mapping research structures and temporal dynamics in timber seismic engineering.