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

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

Donwoo Lee, Seungjae Lee, Koichi Kusunoki

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

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