| Summary: | With the rapid development of underground infrastructure and the uncertainty of its location, the possibility of damage due to nearby construction has increased. Thus, for the early warning of dangerous construction behaviors around underground facilities, this paper proposes a novel real-time distributed monitoring method with three levels, comprised of the terminal node, relay node, and server. Corresponding to these three monitoring levels, a vibration-based intelligent solution for recognizing the construction source is presented and compared with the traditional method. First, the blind source separation method was used to separate collected signals into a limited number of monitoring object sources; this helped to minimize the number of required classification categories and reduce the recognition uncertainty caused by signal mixing. Then, the mutual information (MI) method was used to select suitable vibration features, which were used as the input matrix for the resulting intelligent recognition. Finally, the construction behaviors were identified at the server based on returned features. Guided by this method, a sample dataset including pile-driving, train-operation, and environment-vibration signals was constructed and combined with a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. The effects of blind source separation and the MI method are discussed in depth in this paper. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
|
|---|
