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This paper studies the inverse problem of recovering load magnitudes from stress-intensity-factor profiles along a crack front, using SIFBench finite-element data. It characterizes when the combined load is identifiable and provides an estimator with calibrated uncertainty for ill-posed regimes.
This paper proposes a lightweight multi-agent framework using AutoGen for automated concrete barrier design, achieving over 98% accuracy and showing that smaller models can outperform massive ones in this domain.
This paper introduces HELM, a human-agent framework that automates finite element modeling of concrete bridge barriers, increasing success rates from 20% to 75% using commercial FE software.
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.