Microtunneling decision support system (MDS) using Neural-Autoregressive Hidden Markov Model

Abstract

Microtunneling is a trenchless technology method used for installing new pipelines. The inherent advantages of this method over open-cut trenching have led to its increasing use. This paper presents a general model for microtunneling decision support system (MDS) that can be used as a basis for developing more effective microtunneling design and construction. The model objectives are to: (1) develop a description of local geology that reflects the uncertainty of the information on which it is based and (2) provide the input data necessary for other decision support systems. MDS is composed of two main modules: (1) geology prediction model (GPM) module which is based on Neural-Autoregressive Hidden Markov Model and (2) excavation method selection module to select appropriate excavation method based on GPM result. In order to validate the proposed model, a microtunneling project: Zhong-he drainage water tunnel in Taiwan, was used as a case study. The result shows that the MDS model achieves these objectives to a satisfactory degrese.