Estimation methods

Several techniques and methods for the estimation of the impact of risk and uncertainty on construction cost and duration of infrastructure projects have been developed by practitioners and researches, in which the use and suitability of each technique depends on the purpose for which it is employed and the amount of available data at the project phase of estimation. Leonard (2009) grouped different estimation techniques into: expert judgment, analogous, engineering build-up, parametric and hybrid model.

2.9.2.1 Expert judgment estimation

Expert judgment, guided by historical information, provides valuable insight about the environment and information from previous similar projects. Expert judgment can also be used to determine whether to combine methods of estimating and how to reconcile differences between them. Expert judgement models use algorithms, heuristics, expert system programming, and fuzzy logic techniques.

According to Leonard (2009) the advantages of expert judgment is: it can be used when no historical data are available; it takes minimal time and is easy to implement; an expert may give a different perspective or identify factors not previously considered leading to a better understanding of the project; it can help in cross-checking for estimation relations that require data significantly beyond the data range; it can be combined with other estimation techniques; and it can be applied in all project phases. However, the expert judgment has the following disadvantages: its lack of objectivity; and it is not very accurate or valid as a primary estimation method.

2.9.2.2 Analogous estimation

Analogous estimation is a technique for estimating the duration or cost of an activity or a project using historical data from a similar activity or project. Analogous estimating uses parameters from a previous, similar project, such as duration, budget, size, weight, and complexity, as the basis for estimating the same parameter or measure for a future project. Analogous estimating is frequently used to estimate a value when there is a limited amount of detailed information about the project (Chou and Yang, 2012). Analogous estimating is generally less costly and less time consuming than other techniques, but it is also less accurate. It can be used in conjunction with other estimating methods. Analogous estimating is most reliable when the previous activities are similar in fact and not just in appearance, and the project team members preparing the estimates to have the needed expertise (Chou and Yang, 2012).

The analogy method has the following advantages: it can be used before the detailed project is known; if the analogy is strong, the estimate will be defensible; an analogy can be developed quickly and at minimum cost; and the tie to historical data is simple enough to be readily understood. Analogy also has some disadvantages: an analogy relies on a single data point; it is often difficult to find the detailed cost, technical details required for analogies; there is a tendency to be too subjective about the technical parameter adjustment factors; difficulties in the measure

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of the concept of degree of similarity, and the difficulty of incorporating the effect of technological progress and of context factors. (Leonard, 2009).

2.9.2.3 Engineering build-up estimation

Engineering estimating is based on the detailed analysis and features of the project. The estimated variables of the project are estimated in a very analytical way. This model is done at the lowest level of details. Because of the high level of detail, each step of the work flow should be identified, measured and tracked and the results for each outcome should be summed to make the point estimate (Peurifoy and Oberlender, 2002).

The advantages of the engineering estimating include: the estimator’s ability to determine exactly what the estimate includes and whether anything was overlooked; its unique application to the specific project; it gives a good insight into major project variables contributions; and easy transfer of results to another project. Some disadvantages of this approach are: it can be expensive to implement and is time consuming; it is not flexible enough to what-if questions; new estimates must be built for each alternative; the project specification must be well known and stable; all variations and changes must be reflected in the estimate; some errors can grow into large errors during the summations; and some elements can be omitted by accident. (Leonard, 2009).

2.9.2.4 Parametric estimating

Parametric estimating utilises a statistical relationship between relevant historical data and other variables to calculate the cost or duration of a project. This technique produces high levels of accuracy depending upon the sophistication and underlying data built into the model (Peurifoy and Oberlender, 2002). Activity durations and cost can be quantitatively determined by multiplying the quantity of work to production rate and unit cost respectively. Parametric techniques such as regression, Bayesian, case-based reasoning, neural networks, statistical models, Monte Carlo simulation, and decision rules, can be used, in conjunction with other estimating methods.

The parametric estimating approach has several advantages compared to the other estimation approaches: if the data are available, parametric relationships can be derived at any level; if the design or specification of project changes, estimation can be quickly modified and used to answer what-if questions and design alternatives; it can produce sensitivity analysis for varying input parameters; parametric relationships derived from statistical analysis generally have both objective measures of validity and a calculated standard error that can be used in risk analysis; and it relies on historical data which increase the estimate’s defensibility. However, the disadvantages of parametric estimating include: the underlying database must be consistent and reliable and it may be time consuming to normalise the data; the database must be updated from time to time to capture most current cost, technical details; using data outside the database range causes errors; and complicated estimation techniques, such as nonlinear analysis may make it difficult for others to readily understand the relationship independent variables and estimated cost and time (Leonard, 2009).

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Artificial intelligence, such as case-based reasoning (CBR), neural networks (NNs), genetic algorithms (GA) and variations of such, is able to facilitate the parametric estimation. Much research has been carried out in exploring the applicability of AI methodologies in cost and time estimating, specifically NNs and CBR (Leśniak and Zima, 2018, Kim et al., 2004, Hsiao et al., 2012). The AI parametric approaches offer powerful abilities to estimate construction costs and time accurately (Wauters and Vanhoucke, 2016, Ebrat and Ghodsi, 2014, Odeyinka et al., 2013, Ji et al., 2018).

The AI parametric model has the following advantages: it can be adjusted to best fit the hardware or software being estimated; estimates are based on a database of historical data; and it can be calibrated to match a specific project environment. However, the parametric model includes the following disadvantages: the results depend on the quality of the underlying database; they require many inputs that may be subjective; and accurate calibration is required for valid results (Leonard, 2009).

2.9.2.5 Hybrid estimating models

The concept of hybrid estimating models are to combine unique features of each estimating technique to capture different patterns or features in the data set to improve the reliability of estimation by assessing precisely the risk and uncertainty (Leonard, 2009). A hybrid estimating model is especially useful when it is not clear which model would provide a more accurate estimation (Arashpour et al., 2016).

As discussed in Section 2.7, in construction projects, there are three main sources of uncertainty: variabilities in cost and time, correlation between the costs, times and cost-time and disruptive event (also known as risk, unforeseen events).

Variability in cost and time of projects are modeled with PERT (Programme Evaluation and Review Technique), APRAM (Advanced Programmatic Risk Analysis and Management Model) and CSRAM (Construction Risk Analysis Model) (Ökmen and Öztaş, 2010). Touran (1993b); Bakhshi and Touran (2012); Moret and Einstein (2011b) and Firouzi et al. (2016) modeled the correlation between project cost and times variables. For instance, Firouzi et al. (2016) developed a hybrid generic copula-based Monte Carlo model and assessed the correlations between the construction costs. Very few models are available to assess the impact of disruptive events (risk, unforeseen) on construction cost and duration. WSDOT (2012) developed CEVP (Cost Estimation Validation Process) to model risk events in construction projects by employing impact matrix. There is evidence that available hybrid estimating models improve the estimation of construction project by quantifying the impact of variability, correlation, and disruptive events in project cost and time. However, none of these estimating models is capable of modelling the three sources of uncertainty and assessing their cumulative impact on construction projects cost and time (Moret and Einstein, 2016). In the subsequent section, the Cost Estimate Validation Process (CEVP), as the one of the most advanced risk-based estimating tool, is discussed.

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