• A machine learning framework for first-passage reliability assessment based on PINN is established.
• The proposed method makes full use of the physical properties and sample information of the practical problems.
• Physical problems ...

The first-passage failure of structural dynamic systems is a typical failure mode in engineering. Most of the existing methods use ordinary or partial differential equations (ODEs/PDEs) to describe the dynamic systems, and transform the first-...

• Proposed a novel machine learning approach based on Graph Neural Networks (GNN) for dynamic response simulation of structures subjected to dynamics loads.
• The proposed GNN can simulate full-field spatiotemporal dynamics, generating ...

Prediction of structural responses is essential for the analysis of structural behaviour subjected to dynamic loads. Existing approaches are limited in different ways. Experimental tests are expensive due to the requirement of intensive labour ...

• Improved Machine-Learning (ML) techniques were used to predict Maximum Interstory Drift Ratio (IDRmax) and Residual Interstory Drift Ratio (RIDR) of 384 Special Moment-Resisting Frames (SMRFs) considering Soil-Structure Interaction (SSI).

Nowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To ...

The contribution at hand presents a method for the application of Recurrent neural network based constitutive models within a coupled field Finite Element Analysis. Thereby, an additional scalar field is coupled to the displacement field and ...

• Proof of concept for neural network constitutive models in coupled field simulation.
• Possibility to approximate arbitrary inelastic constitutive behavior.
• Application for softening material behavior within Finite Element ...