George Jergeas

This paper explores factors that contribute to claims of changed soil conditions in infrastructure and building projects through a limited review of Canadian legal cases. Forty-one cases, relating to geotechnical problems covering a period of 25 years (1982–2006), were studied. The information obtained was categorized according to the causes for geotechnical claims. The most frequent causes of geotechnical disputes were found to be different soil conditions and recommendations than expected from those given in the geotechnical report, inaccuracies in the design plans and specifications, and the owner’s failure to disclose important information. The paper also discusses the special requirements for preparing geotechnical information for construction and suggests precautionary measures to reduce the number of disputes or to arrive at an equitable resolution. These measures require more proactive planning and may require additional funds before the bidding stage to perform a more detailed soil investigation, keep communication clear among all participants, and make a greater effort to provide accurate designs and specifications.

Developing and constructing oil sand megaprojects are huge technological, engineering, and construction achievements for Alberta and Canada. Thousands of engineering and project management professionals and workers are contributing to the current effort with great pride and sense of satisfaction. But recent large capital oil sands construction projects have all experienced significant cost and schedule overruns. There are a number of reasons for these overruns. This paper provides a definition of a “megaproject,” illustrates the challenges in delivering oil sands megaprojects, and lists a number of reasons for cost and schedule overruns.

Measuring projects’ cost and schedule risks in an integrated framework using simulation has several modeling challenges that have yet to be addressed by researchers. This paper presents a multilevel network modeling approach that aims to integrate a combination of different networks in one framework, and presents a computer simulation implementation to the cost and time risk assessment network (CTAN). The CTAN is an integrated network that includes uncertainties in the realization of the schedule logic, in activities durations, in project scope, and in cost. The simulation model is a decision support simulation system (DSSS) that currently consists of three modules: the CTAN, the stochastic decision trees, and the stochastic shortest/longest rout network. The CTAN-DSSS may be used in cost and schedule risk assessment. It completely integrates with other DSSS networks and deals with risks associated with cost, time, and scope at equal importance. The DSSS was verified by conducting several tests and validated by its extensive use for both undergraduate and graduate courses in Civil Engineering at the University of Calgary over the last three years.

Decision trees (DTs) have proven to be valuable tools for decision making. The common approach for using DTs is calculating the expected value (EV) based on single-number estimates, but the single-number EV method has limited the DTs’ real-life applications to a narrow scope of decision problems. This paper introduces the stochastic multilevel decision tree (MLDT) modeling approach, which is useful for analyzing decision problems characterized by uncertainty and complexity. The MLDT’s advantages are shown through a computer simulation program: the Decision Support Simulation System (DSSS). The DSSS allows users to model probabilistic linear graph networks and provides a hierarchical modeling method for modeling decision trees to present uncertainties more accurately. It consists of three modules: tree analysis networks (TANs), the shortest and longest path dynamic programming analysis network, and cost time analysis networks. The paper only discusses the TAN module by presenting the MLDT concept under the TAN of the DSSS computer application. The content of the paper includes the modeling approach, its advantages, and examples that can be used in modeling stochastic trees. The DT-DSSS was verified by conducting several tests and validated by using it extensively for undergraduate courses in civil engineering at the University of Calgary for the last two academic years.