This project proposes two new methods of deep neural networks and handcrafted features for damage detection. The first method uses a convolution neural network (CNN) to extract deep features in time series and Long Short Term Memory (LSTM) network to find a statistically significant correlation of each lagged feature in time series data. | TRƯỜNG ĐẠI HỌC GIAO THÔNG VẬN TẢI DEEP LEARNING APPROACHES FOR DAMAGE DETECTION IN STRUCTURES UNDER CHANGING DAMAGE CONDITIONS Supervisor TS. Bùi Ngọc Dũng Student Lê Kiến Trúc Vũ Quang Tuyền Lớp CNTT3-K58 CNTT6-K60 Abstract This project proposes two new methods of deep neural networks and handcrafted features for damage detection. The first method uses a convolution neural network CNN to extract deep features in time series and Long Short Term Memory LSTM network to find a statistically significant correlation of each lagged feature in time series data. Instead of using the LSTM network the second method uses handcrafted features to find the sensitive features to the damage. These two types of features are combined to increase damage detection ability compared to deep features only. Từ khóa damage detection machine learning deep learning CNN LSTM. 1. INTRODUCTION Structural damage is defined as any change in the structural properties that prevent the system from performing at the desired level of safety and functionality 1 . Inherent in this definition of damage is the concept that damage detection requires a comparison between two states of the system one of which must be representative of the reference usually undamaged conditions of the system. There are several machine learning methods for damage detection such as Principal Component Analysis PCA 2 Support Vector Machine 3 Artificial Neural Network ANN 4 Autoregressive model AR 5 . In general these methods all consist of three main steps First is to collect data second is to process raw data to get important features and finally base these features to detect damage. The approaches of damage detection based on the above machine learning methods have been successfully applied to model the normal and damaged condition even when severe effects of varying factors impose difficulties to the damage detection. However these approaches have limitations due to the dependence on the structural inspections Kỷ yếu nghiên
