Data augmentation technique for construction engineering regression surrogate model

Abstract

. The objective of this study is to predict the degree of danger to the human body from motion information such as acceleration, velocity and displacement during a collision between a car and a human body. As a preliminary step, the maximum bending moment that occurs in the leg was predicted using a convolutional neural network. The responses which are represented by learning data generated by 1D-CAE system. A number of training data sets are varied in order to show the enough number to predict. The predictor ’s accuracy is evaluated by the test data sets . We’d like to discuss necess isty of a total number of training data sets and effectiveness of data augmentation technique. In addition, the technique to utilize classification by the t-SNE method to improve accuracy is also examined. t-SNE is based on classification algorithm, however an engineering interpolation should be computed based on physical meanings and influential parameters.