Security detection of building structure based on sparse encoding deep learning algorithm

Abstract

Most health problems of building structures are accumulative damages which are difficult to detect, and it is more difficult to monitor the structure health due to the complexity of the practical structure and the environment noise, and the existing methods need lots of data for model training but it is very complicated to mark the data in practice. In order to solve above problems, the wireless sensor network is configured and the sparse encoding method is adopted to monitor the bridge structure health, and meanwhile the sparse encoding algorithm is adopted for training on the basis of the characteristic extraction of many unlabeled instances, thus to compress data dimensionality and preprocess unlabeled data. Then, the deep learning algorithm is adopted to predict the bridge structure health monitoring type, and meanwhile Hessian optimization is improved on the basis of the linear conjugate gradient in order to replace uncertain Hessian matrix by positive semidefinite Gaussian - Newton curvature matrix for secondary objective combination, thus to improve the efficiency of the deep learning algorithm. The experiment result shows that the security detection of the bridge structure based on deep learning algorithm can monitor the high-accuracy structure health conditions under the sparse encoding of the environment noise.