AI Applications in Earthquake Resistance Using Change in Structural Design

Earthquake disasters cause huge loss to human society as well as the national economy. In this current era of technological growth, artificial intelligence (AI) system plans a vital role in handling the predictions both in research and predictions toward futuristic endeavors in the field of prevention. Similar to human intelligence, AI technique allows the machines to understand, predict, and react to the problems as a software. In recent years, AI has its own subset associated with machine learning algorithms; design recognition and deep learning mechanism were seeking attention effortlessly with proven intelligence and effectiveness. In the near future, AI has its own feasible necessary solution against accurate predictions. This review of literature on AI applications in earthquake resistance over the structural engineering is conducted based on the parameters similar to seismic structural design to reduce risk in posterior earthquake, seismic self-cantered system design, and various AI mechanisms such as neural network, artificial neural networking techniques, and convolutional neural networking; layered approach along with structural change and behavior system with AI technique to predict accurately the prediction of earthquake to reduce the lost human lives as well as landscape and properties is disclosed with detailed description with the suitable technique how predictions are made over the design structure and is elaborated with the review of literature with the relevant figures. In conclusion, future work where there is preexisting AI-based earthquake prediction can include many more parameters that determine earthquakes other than the regular parameters and that can be taken into account to precisely determine the size of earthquake, and its plateau changes in respected magnitude and latitude can be prevented. Accordingly, the structural design of the buildings with the appropriate materials can be constructed without causing much more loss.