As deliberated previously from literature in Chapter Two, cost and time underestimation due to uncertainty is a global phenomenon in infrastructure projects, and construction of infrastructure
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projects are subject to various uncertainties that affect construction cost and duration differently (El Khalek et al., 2016). The impact of uncertainties depends on the features (size, nature, and complexity) of the infrastructure project.
Various researcher (Touran and Lopez, 2005, Moret and Einstein, 2016, Ang and De Leon, 2005, Arestegui Carvajal, 2014, El Khalek et al., 2016) attempted to tackle this phenomenon by developing different estimating models (fuzzy logic, artificial neural network, fault tree analysis) and techniques (Monte Carlo, Bayesian, decision analysis) based on probabilistic theory; however, till now none of these models and methods have provided a realistic and accurate estimate because of an inaccurate assessment of the impact of uncertainties on cost and time. While modern portfolio theory stipulates that uncertainty/risk, probability and outcome have a direct relationship and the uncertainty and risk should be assessed in the uncertainty portfolio (Omisore et al., 2011). Modern portfolio theory has been used by a few researchers in the field of construction management, and none of them employed this theory in the field of construction cost and time estimation. Therefore, this study adopted the principles of modern portfolio theory and combined it with the philosophy of probabilistic theory and infrastructure characteristics to tackle assessing the uncertainty and risk in construction projects and to address the phenomenon of the underestimation of infrastructure cost and time.
This research is based in the field of construction economics, cost and time estimating of infrastructure projects and construction management. This study aims to develop a hybrid estimation model by adopting unique features of probablistics theory and the modern portfolio concept, which reflects how to assess the uncertainty and the compensations of all possible elements’ cost and time combinations in the uncertainty portfolio of a project and identify all efficient items’ cost and time combinations and hence discard all items’ cost and time that are instead found, by comparison, to be inefficient. The review of literature (AASHTO 2009, Molenaar 2010, Yoe 2011) perspectives provides conceptual evidence that there is an association between infrastructure project characteristics, impacts of uncertainties and accurate estimation of cost and time of the infrastructure project. Therefore, the study embedded the philosophy of the modern portfolio theory in probabilistic theory in developing a conceptual framework for understanding the relationship between different sources of uncertainty in the construction process, infrastructure project characteristics and estimation of cost and time to address the infrastructure cost and time underestimation phenomenon. The conceptual framework of the study (uncertainty portfolio) is shown in Figure 3.3.
The process is hinged on integrated infrastructure project characteristics as variables to develop an uncertainty estimation model using the uncertainty portfolio concept to assess the impact of different sources of uncertainty to improve the accuracy of estimation of infrastructure cost and time. Due to the peculiar nature of uncertainty, the impact of uncertainty is assessed in the uncertainty portfolio of the project.
49 Characteristics of infrastructure projects Sources of uncertainty in construction process of infrastructure projects
Estimation of cost and time of infrastructure
projects
Uncertainty portfolio of
infrastructure projects • Infrastructure characteristics • Impacts of sources of uncertainty
H1
H2
H3
Figure 3.3: Conceptual framework of the study
The conceptual framework considered three basic premises: 1. the estimation of infrastructure cost and time should not be assessed by itself, but by how it contributes to an uncertainty portfolio of the infrastructure project; 2. the impact of uncertainty on cost and time of project should consider the nature of infrastructure activities and project; and 3. the variance of cost and time of an infrastructure project is a function not only of the variance of each individual activity due to uncertainty (variability), but also of the covariance (correlation) between activities and the unforeseen (disruptive events).
The key insight of the conceptual framework developed is that the estimation of infrastructure cost and time should assess the different sources of uncertainty in the uncertainty portfolio of the infrastructure project by using probabilistic theory. Another important argument of the conceptual framework is that the impact of uncertainty on the cost and time of projects should be evaluated at the level of the single activity and the cumulative impact considered as the uncertainty portfolio of the infrastructure.
Based on the three premises of the conceptual framework it can be expected that variability uncertainty of some activities increases while at the same time the variability uncertainty of other activities decreases. The premises of the framework suggest that the relationship among the respective activities in infrastructure projects activities are positively correlated (Touran and Lopez, 2006) and thus increasing cost or time in one activity is associated with increasing cost or time of the other and vice versa, this significantly reduces the risk of double cost or time underestimation error of the infrastructure projects. Also, the conceptual framework considers the principles of probabilistic theory on occurrence of disruptive events in each activity.
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The uncertainty portfolio conceptual framework proposed that the uncertainty portfolio of the infrastructure project is steady, and that the uncertainty portfolio concept provides a reliable platform on which to develop a hybrid estimation model for assessing the uncertainty portfolio of projects and improving the accuracy of infrastructure cost and time estimation.
Furthermore, the coverage of the two common estimation theories on predicting the total construction cost and time investigated in this research, namely: deterministic theory (base estimation) and probabilistic theory (risk-based estimation), compares to the estimation coverage of proposed uncertainty portfolio concept (uncertainty estimation model) illustrated in Figure 3.4.
Figure 3.4: Assessment of coverage of three estimation approaches
The base estimation includes all of the known items (project specifications) and other items that are known and quantified at the point of starting the construction phase (identified risk) using the deterministic method. Risk-based estimation includes the base estimation items along with other items needed to construct the project but have yet to be fully identified and can be quantified (unidentified risk) using some probabilistic methods. The difference between uncertainty estimation with the risk-based estimation is, instead of applying percentage contingency to the cost and time of infrastructure projects to cover cost and time of uncertainty (Shen et al. 2015), all of the inherent uncertainty in the process of infrastructure construction are identified and carefully
B ase E stim ation Risk -b ase d E stim ation Un ce rt ai n ty E sti m atio n Pl anning 100% De sign Cons tr u ction Com p let ed
Planning & Design Phase Construction Phase Project specifications
Known & Known
Identified Risk
known & quantified
Unidentified Risk
Unknown & Quantified
Uncertainty
Unknown & Unquantified
Project Life Cycle
Cost Overruns Time Delays
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divided to the risk and uncertainty-variable and the impact of each one simulated using an uncertainty portfolio of project and probability distribution. Furthermore, the conceptual model depicts the relationship between the project characteristics and the uncertainty in the construction of an infrastructure project, based on the project maturity and uncertainty within the project life cycle.
As presented in Figure 3.4, the level of project uncertainty reduces when more and better data and information become available, and the project progresses into the project life cycle. This is an integrated conceptual model of the philosophy behind the current research and the research hypotheses that are discussed in the next section and were tested and validated through the case study in Chapters Six and Seven.