Graph neural network-assisted evolutionary algorithm for rapid optimization design of shear-wall structures

Y Fei, S Qin, W Liao, H Guan, X Lu - Advanced Engineering Informatics, 2025 - Elsevier
When solving expensive optimization problems (EOPs), eg, optimization design of shear-
wall structures, conventional evolutionary algorithms (EAs) face a challenge of elevated …

Training and application of graph neural networks for predicting structural responses targeted at tall building structures

A Tang, C Li, J Yang, H Zhang, Q Zheng… - Journal of Building …, 2025 - Elsevier
Finite element analysis (FEA) methods are typically computationally intensive and time-
consuming, particularly in structural optimization tasks that involve multiple iterations. To …

Efficiency and explainability of design‐oriented machine learning models to estimate seismic response, fragility, and loss of a steel building inventory

M Zaker Esteghamati… - Earthquake Engineering & …, 2025 - Wiley Online Library
Abstract Machine learning (ML) has recently been used as an efficient surrogate to estimate
different steps of performance‐based earthquake engineering (PBEE), from dynamic …

Time history seismic response prediction of multiple homogeneous building structures using only one deep learning‐based Structure Temporal Fusion Network

Z Li, Q Yang, Q Deng, Y Gong, D Tian… - … & Structural Dynamics, 2024 - Wiley Online Library
Structural response prediction under earthquakes is crucial for evaluating the structural
performance and subsequent functional restoration. Deep learning provides the potential to …

Intelligent design of steel–concrete composite beams based on deep reinforcement learning

CH Lin, B Fu, L Zhang, N Li, GS Tong - Structures, 2024 - Elsevier
In this work, an automated member design method is proposed based on deep
reinforcement learning (DRL). Using steel–concrete composite beam design as a case …