Review on artificial intelligence application in structural earthquake engineering

Artificial Intelligence (AI) has brought a transformative shift to seismic engineering during past decade, enabling engineers to address complex challenges with unprecedented precision and efficiency. By leveraging machine learning (ML) and deep learning (DL) technologies, researchers are redefining seismic analysis, structural response prediction, and damage assessment. AI-driven methods such as artificial neural networks (ANNs) and convolutional neural networks (CNNs) have proven highly effective in analyzing seismic data and predicting structural performance during earthquakes. These tools process vast datasets collected from global seismic networks, facilitating real-time monitoring and more accurate damage assessments. They predict structural responses with remarkable precision, optimize designs for resilience, and better prepare for natural forces Furthermore, advancements like physics-informed neural networks (PiNNs) integrate engineering principles with AI, providing models that are both reliable and interpretable. This paper reviews the advancements of AI application in earthquake engineering during the past decade ^{(Open Access Articles)}, current challenges and future directions.