The use of information technology (IT) in a variety of engineering fields is increasing in order to expedite routine engineering design and analysis procedures. In geotechnical engineering, various types of information technology, such as geographical information system (GIS), artificial intelligence (AI), and numerical simulation, are now being actively used to predict, visualize, and analyze physical parameters. In this paper, the recent development in integration of IT into geotechnical engineering fields are presented with emphasis on the use of GIS in tunnelling risk management. A combined technique that couples the artificial neural network (ANN) and the GIS is then presented. The proposed approach involves the development of ANN(s) using a calibrated finite element model(s) for use as a prediction tool and implementation of the developed ANN(s) into a GIS platform for visualization and analysis of spatial distribution of predicted results. A novel feature of the proposed approach is an ability to expedite a routine geotechnical design process that otherwise require significant time and effort in performing numerical analyses for different design scenarios. Two illustrative examples in which the developed approach was implemented are given; one for an urban tunnelling design project and the other for a soft ground improvement design project. It is shown that the proposed approach can be an efficient and robust decision making tool for routine geotechnical design works. This paper describes the concept and details of the proposed approach and its implementation to an urban tunnel and a soft ground improvement design projects.