About this Research Topic
The use of artificial intelligence (AI) has witnessed a remarkable surge in recent years, finding applications in diverse fields of science and engineering, including structural engineering. The incorporation of AI in structural engineering has the potential to drive significant advancements in the field by enhancing our understanding of complex structural behavior and optimizing designs for better performance and safety. Additionally, the effective use of AI techniques has the potential to minimize costs and environmental impact in structural design while promoting the development of sustainable and resilient infrastructure. However, in some structural engineering applications, obtaining experimental data can be challenging, making it difficult to develop AI-based models. To address this, well-calibrated numerical models are considered as an alternative to generate large datasets for effective machine learning model training.
This Research Topic presents an opportunity for authors to exhibit their latest research findings on the utilization of AI and computational modeling in structural engineering. We welcome contributions that cover a wide range of topics, including numerical simulations of structural behavior, innovative machine learning techniques such as supervised, unsupervised, and reinforcement learning, and metaheuristic multi-objective optimization algorithms for structural engineering. The aim of this collection is to leverage the benefits of AI in advancing the field of structural engineering. Therefore, we invite original research articles and reviews that focus on these areas.
The topics of interest include, but are not limited to:
• Applications of various AI techniques such as supervised, unsupervised, and reinforcement learning in structural engineering;
• Machine learning-based performance predictions of different structural members;
• Application of optimization techniques for different objectives, including cost, environmental impact, and performance;
• Assessment of the stability and vibration properties of plates, shells and fiber reinforced polymer (FRP) composite structures;
• Integration of numerical simulations and machine learning techniques;
• Physics-based machine learning for performance predictions of concrete, steel, and FRP composite structures;
• Finite element simulation and optimization of laminated FRP composite thin-walled structures;
• Development of user-friendly interfaces for the practical implementation of machine learning techniques in structural engineering applications to assist practitioners and designers;
• Big data for structural engineering;
• Applications of AI in structural health monitoring and vibration;
• Machine learning based risk and resilience assessment;
• Machine learning aided performance-based design of buildings and bridges.
This Research Topic presents an opportunity for authors to exhibit their latest research findings on the utilization of AI and computational modeling in structural engineering. We welcome contributions that cover a wide range of topics, including numerical simulations of structural behavior, innovative machine learning techniques such as supervised, unsupervised, and reinforcement learning, and metaheuristic multi-objective optimization algorithms for structural engineering. The aim of this collection is to leverage the benefits of AI in advancing the field of structural engineering. Therefore, we invite original research articles and reviews that focus on these areas.
The topics of interest include, but are not limited to:
• Applications of various AI techniques such as supervised, unsupervised, and reinforcement learning in structural engineering;
• Machine learning-based performance predictions of different structural members;
• Application of optimization techniques for different objectives, including cost, environmental impact, and performance;
• Assessment of the stability and vibration properties of plates, shells and fiber reinforced polymer (FRP) composite structures;
• Integration of numerical simulations and machine learning techniques;
• Physics-based machine learning for performance predictions of concrete, steel, and FRP composite structures;
• Finite element simulation and optimization of laminated FRP composite thin-walled structures;
• Development of user-friendly interfaces for the practical implementation of machine learning techniques in structural engineering applications to assist practitioners and designers;
• Big data for structural engineering;
• Applications of AI in structural health monitoring and vibration;
• Machine learning based risk and resilience assessment;
• Machine learning aided performance-based design of buildings and bridges.
Keywords: Composite structures, Steel and concrete structures, Fiber reinforced polymers, Finite element analysis, Artificial intelligence, Machine learning, Optimization
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
