Search

Simulation models are built on assumptions, approximations, and estimates. Repetitive long-term projects such as tunnel construction provide opportunities to finetune approximations based on input from actual project progress. Bayesian updating techniques are an effective approach for improving the quality of simulation input and output based on what has already been observed. This paper presents a case study in which Bayesian techniques were applied to a simulation model of an actual tunnel project, the North Edmonton Sanitary Trunk. The study shows that using Bayesian techniques greatly improves the quality of projections. The novelty of this work includes the enhancement of the application of Bayesian updating techniques, the demonstration of simulation applications with a fully monitored tunneling project, and the demonstration of the extent of improvement to planning predictions from the use of actual data and the Bayesian updating techniques. DOI: 10.1061/(ASCE)0733-9364(2006)132:8(882) CE Database subject headings: Bayesian analysis; Simulation; Tunneling; Excavation.

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 since its first introduction into North America in the early 1980s. With this technology, surface disruption can be minimized, especially in urban areas, and high accuracy of installation (usually less than 2 cm over 100 m) can be achieved in both line and grade. But microtunneling machines are very expensive and few contractors have extensive experience with this technology. Microtunneling can also be risky when unexpected obstacles or soil changes occur. Careful constructability analysis is needed, and an appropriate microtunneling method should be selected in order to achieve successful completion of microtunneling projects. A computerized decision support system (DSS) for microtunneling was developed to support decision making for contractors who want to bid on microtunneling projects. This paper discusses the decision-making process for microtunneling and the development of the DSS. When the user enters basic information about the potential project such as drive length, installation depth, pipe diameter, and soil condition, the DSS evaluates whether microtunneling will be economically feasible and suggests appropriate types of microtunneling methods. The user can then select microtunneling machines, types of pipes, and types of shaft construction methods. This DSS is most beneficial when used at the preplanning stage by utility contractors. DOI: 10.1061/(ASCE)0733-9364(2004) 130:6(835) CE Database subject headings: Microtunneling; Economic factors; Soil conditions; Decision support systems; Pipelines.