Seismic Waveform Recognition and Adaptive Adjustment System of Seismic Design Parameters Optimized by AI Algorithm

Seismic waveform recognition is crucial for seismic activity monitoring and early warning in the field of earthquake science and engineering, and seismic design for building seismic-waveform recognition and data is dependent on the use of artificial intelligence to achieve better earthquake detection and seismic design parameters. The development of Artificial Intelligence (AI) technology and the application of deep learning algorithms have created the conditions for automatic recognition of seismic waveforms and optimization of seismic design parameters. Based on the optimization of the artificial intelligence algorithm, this paper develops a seismic waveform recognition and seismic design parameter adaptive adjustment system. The system uses a convolutional neural network (CNN) model to recognize seismic waveforms, and enhances the recognition accuracy and processing speed of the model via various methods. The system also includes an adaptive adjustment module. Real-time seismic activity data are used to dynamically adjust the seismic design parameters. From the experimental results, the seismic waveform recognition by the system has been verified. The recognition rate of the CNN model can reach 95.2%, the processing time of the model is 578ms, and the system response is very fast. The maximum CPU utilization of the system does not exceed 28.9%, therefore the system has low resource consumption, high stability and reliability. The average time between failures indicates that the system has high reliability, and the minimum average duration of system failures is only 0.7 h. At the same time, the system exhibits good performance in adaptive adjustment of seismic design parameters, which can be quickly adjusted based on real-time data, thereby providing more accurate and effective seismic protection for buildings and infrastructure.