Cutting-edge AI-driven Design, Analysis, and Optimization of Engineering Structures
Submission DeadLine: 30 June 2025
The topics of interest include, but are not limited to:
Cutting-edge AI-aided design of engineering structures
Structural analysis co-driven by DL and physics-based methods
DL-powered engineering design and optimization
LLM models of domain knowledge in engineering structures
Guest editors:
Prof. Xinzheng Lu, PhD
Tsinghua University, Beijing, China
(Intelligent structural design, Disaster prevention and mitigation, Earthquake engineering)
Prof. Ertugrul Taciroglu, PhD
University of California, Los Angeles, USA
(Solid & structural mechanics, Soil-structure interaction, Structural health monitoring, Regional hazard risk & resilience assessment, Machine learning in civil engineering applications)
Prof. Giuseppe Carlo Marano, PhD
Politecnico di Torino, Turin, Italy
(Generative design, Parametric design, Generative AI, Structural optimization)
Dr. Christian Málaga-Chuquitaype, PhD
Imperial College London, London, United Kingdom
(Seismic protection systems, Performance-based earthquake engineering, Computational modelling, Artificial intelligence applications)
Dr. Chen Xiong, PhD
Shenzhen University, Shenzhen, China
(Structural seismic resistance, Structural durability, Application of information technology in civil engineering, Urban and regional building disaster prevention and mitigation, Prefabricated structures)
Dr. Wenjie Liao, PhD
Southwest Jiaotong University, Chengdu, China
(Intelligent structural design, Deep learning, Seismic resilience)
Manuscript submission information:
When submitting your manuscript please choose the special issue “VSI: AI Empowered Structures” from the choice of submission types.
The deadline for submission of manuscripts is June 30, 2025.
All interested authors can submit papers to the special issue through the online journal submission system at EM system: https://www.editorialmanager.com/engstruct
Keywords:
Deep learning; Generative AI; AI-driven design; Intelligent analysis; DL-powered optimization; Domain LLM model; Physics-informed AI
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