Efficient Bayesian model updating of large-scale bridges based on physics-informed neural networks surrogate model

Assessment model for the long-term behavior of PSC-I girder using a Bayesian physics-informed neural network

Gitae Roh, Ki-Yeol Kim, Donghyun Kang, Chi-Ho Jeon, Changsu Shim

A computational tool for determining shear capacity of joints in precast ultra-high-performance concrete segmental bridges based on explainable machine learning

Waleed Bin Inqiad

Seismic response prediction of reinforced concrete frames using graph neural networks with physical interpretability

Donwoo Lee, Seungjae Lee, Koichi Kusunoki

Energy-based seismic demand of multi-storey structures under pulse-like and non pulse-like ground motions

Raffaele Laguardia, Giulia Angelucci, Fabrizio Mollaioli

Assessing seismic demand is a critical task in energy-based seismic design, as it depends on both ground motion characteristics and structural hysteretic properties. In recent years, growing attention has been devoted to demand evaluation through energy-related parameters, such a…Read more