Submission Deadline: 31 March 2026 View: 357 Submit to Special Issue
Prof. Dr. Duc Kien Thai
Email: thaiduckien@sejong.ac.kr
Affiliation: Department of Civil and Environmental Engineering, Sejong University, Seoul, 143747, Republic of Korea
Research Interests: numerical simulation of structures, dynamic behavior of materials, impact and blast loadings, failure analysis of RC and FRC structures, safety assessment of building and structures, development of material models for RC and FRC, application of machine learning in structural engineering
Prof. Dr. Thai-Hoan Pham
Email: hoanpt@huce.edu.vn
Affiliation: Department of Concrete Structures, Hanoi University of Civil Engineering, Hanoi, 113000, VietNam
Research Interests: concrete-steel composite structures, finite element analysis, nanoindentation and mechanical properties of materials steel weld zone investigation, microstructural characterization, concrete-filled double-skin steel tubular (CFDST) columns, structural performance prediction using machine learning, RC and FRC panel damage prediction using machine learning, impact and blast response and resistance of structures
In recent decades, research on structures subjected to shock loads such as impact or blast loads has always attracted the attention of many scholars. Although many achievements have been made, this field still faces many challenges. Experimental approaches, although providing real data, are costly, time-consuming and laborious. Therefore, computer simulation methods are a very good alternative. However, due to the complexity of shock loads, analyzing, predicting, or evaluating the effects of loads as well as structural responses of structures using computer methods is always a big challenge for scholars. There are obvious challenges for this research area, including:
·Material models for new materials such as fiber-reinforced concrete, polymer concrete, composite materials, etc. have not been fully developed. Furthermore, the material model that simultaneously responds to nonlinear behavior, the impact of strain rate, or fracture… is still lacking and needs to be developed.
·The developed blast load models, although quite diverse, still do not accurately reflect their actual response, leading to a large gap between the simulation and the experimental results.
·Recently, the machine learning approach has emerged as a promising tool to predict the structural response of members under shock loads, however, the biggest challenge is the accuracy of the data, the narrow and imbalanced datasets.
All of the above remain significant challenges, requiring ongoing efforts from researchers to provide better solutions. Therefore, we hope that the Special Issue entitled “Computer Modeling of Structure Subjected to Shock Loads: Challenges and Solutions” will serve as a platform for researchers to share their findings, providing valuable insights for scientists and readers alike.
Potential topics include, but are not limited to:
·Research on numerical simulation of structural components subjected to blast and impact loads.
·Development of new material models for various materials that meet the dynamic response of the structure subjected to high-speed impact.
·Research on the development of new structures, such as composite structures, mixed material structures, bio-inspired structures, etc., to enhance the ability to withstand shock loads using numerical simulation methods.
·Application of artificial intelligence in analyzing and predicting the load and damage level of structures subjected to shock loads.
·Methods to solve the problem of noise, narrow and unbalanced data sets when predicting the damage of structures subjected to shock loads.
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