ESTIMATING THE IMPACT OF BUILDING INFORMATION MODELING (BIM) UTILIZATION ON BUILDING PROJECT PERFORMANCE

Many benefits of utilizing the Building Information Modeling (BIM) technology have been recognized and reported in the Architectural, Engineering and Construction (AEC) industry literature. However, it seems that the construction industry still hesitates to fully adopt BIM. As some researchers suggest, the root cause may be in the lack of understanding of whether and how BIM improves project outcomes. This research aims to shed some light on this matter by studying the impact of BIM utilization on building project performance. This research follows a model-based approach as opposed to statistically analyzing the project outcomes with and without BIM utilization. The construction project supply chain is modeled at the design and construction activity level to represent the project behavior in terms of cost over time. As traditional project management tools as well as statistical methods are not able to consider the dynamic nature of the projects such as feedbacks, time delays and non-linear relationships, this research uses system dynamics methodology to model the project supply chain. The project supply chain model is calibrated with two sets of the projects; with BIM and without BIM. The two calibrated models, Non-BIM and BIM-utilized, are used to estimate the outcomes of a hypothetical set of the projects. The outcomes are compared in terms of the project performance indexes to analyze the BIM impact on the project performance. Since relatively few projects that utilized BIM were found, this research employs expert elicitation (EE) technique to capture the required knowledge from the industry to estimate the parameters of the BIM-utilized model. The EE is used to build a causal model to capture the impact of BIM utilization on the Non-BIM project model parameters in the absence of sufficient BIM-utilized project data

Inter-phase feedbacks in construction projects

Understanding diverse performance trajectories of projects is of interest to operations researchers and practitioners. Interactions between multiple phases of a project are commonly assumed to be important in project dynamics, yet the strength of these feedback mechanisms has not been rigorously evaluated. In this study we use data from 15 construction projects to estimate the feedbacks between design and construction phases. The estimated factors reveal that undiscovered design rework diminishes construction quality and production rate significantly and construction completion speeds up the detection of undiscovered design rework. Together, these feedbacks can explain as much as 20% of variability in overall project costs. Comparison of model predictions with a separate set of 15 projects shows good predictive power for cost and schedule outcomes and their uncertainty. The estimation and prediction framework offers a template for using data from multiple cases to estimate both case-specific and industry-wide parameters of project models, and for leveraging those estimates for project planning