Structural Engineering

Structural engineering research is increasingly leveraging machine learning to address challenges in design, analysis, and monitoring of structures. Current efforts focus on developing and applying physics-informed neural networks (PINNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) for tasks such as kinematic analysis, dynamic response evaluation, and topology optimization. These methods aim to improve accuracy, efficiency, and scalability compared to traditional methods like finite element analysis, particularly for complex systems and large-scale designs. The integration of machine learning promises significant advancements in structural design, predictive maintenance, and overall safety.