Structural Failure Prediction of RCC Structure Under Earthquake Using Machine Learning

Earthquake is a most dangerous and catastrophic disaster in the world. It is essential to introduce earthquake resistance features in construction. An earthquake -prone region must deal with the more challenging problem of earthquakes. And there is no more hundred percent earthquake proof building. The best way to avoid the building damages is to learn from the failures that can occur during a seismic event. This paper presents the failure prediction of RCC building using k-Nearest Neighbors (KNN) algorithm which can be tuned to gives up to 90% accuracy of result. A past- earthquake assessment of building data is used to train and create a model for this project to predict the necessary pattern for a future construction project. Thereby, can be predicted whether the proposed building will be collapsed or facing a minimum damage. Key Words: Earthquake Assessment, Machine Learning, Python, K-Neighbour Algorithm, RCC Building, Failure Prediction, Site issues, Soil and Foundation Condition