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This paper proposes an agentic LLM framework for automated structural analysis of 3D frame systems from natural language inputs, achieving 90% accuracy on ten representative 3D frames through a multi-agent pipeline.
This paper validates a multi-resolution ConvLSTM framework for predicting retaining wall deformation during staged excavation, using field data from 11 sites in South Korea and achieving an average MAE of 1.4 mm.