Author

Hanlin Dong

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Open access Aug 2026

A Unified Physics-Constrained Deep Reinforcement Learning Framework for Parameter Identification of Nonlinear Hysteretic Models

This study develops a unified physics-constrained deep reinforcement learning framework for OpenSees Steel02 and DowelType identification, giving accuracy comparable with tuned PSO at the same online OpenSees-call budget while retaining a reusable learned initialization step.

Hanlin Dong, Chunhua Liu, Mingji Fang et al. · Buildings · 0 citations