Analysis of the Structural Resilience of High-Rise Buildings Against Earthquakes Using Deep Learning-Based Artificial Intelligence Technology

This study aims to analyze the resilience of multi-storey building structures to earthquakes by utilizing artificial intelligence technology based on deep learning. In the context of increasing intensity and frequency of earthquakes in different regions of the world, the need for more sophisticated modeling and evaluation systems of building structures is becoming increasingly important. This research uses a qualitative approach with the literature study method (library research) to explore various results of previous research, scientific journals, and relevant technical reports. The main focus is on the application of deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNNs), in detecting, predicting, and evaluating the performance of building structures to seismic stress. The results of the study show that the application of deep learning is able to improve the accuracy in predicting structural damage and accelerate the decision-making process in disaster risk management. In addition, the technology can be used in real-time sensor data processing and earthquake simulation to identify potential structural collapses early. However, challenges such as limited training data, model complexity, and high computing requirements remain obstacles to the widespread adoption of this technology. This study recommends the need for integration between conventional civil engineering approaches and artificial intelligence to create a more adaptive and efficient earthquake mitigation system