Construction cost analysis under uncertainty with correlated cost risk analysis model

Ökmen, Önder; Öztaş, Ahmet

doi:10.1080/01446190903468923

Cited by 34 publications

(13 citation statements)

References 13 publications

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“…Petroutsatou et al Downloaded by [Umeå University Library] at 09:43 06 April 2015 (2012) developed an early cost estimation model using two types of neural networks: (1) the multilayer feedforward network; and (2) the general regression neural network. In Wang et al (2012) and Okmen and Oztas (2010), the efficiency and effectiveness of the model is evaluated through an application of CCRAM and Monte Carlo simulation (MCS) based method using the same hypothetical data. The findings show that CCRAM operates well and produces more consistent results compatible with the theoretical expectancies.…”

Section: Cost Prediction Techniquesmentioning

confidence: 99%

An Early Cost Estimation Model for Hydroelectric Power Plant Projects Using Neural Networks and Multiple Regression Analysis

Gunduz

Sahin²

2015

Journal of Civil Engineering and Management

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Energy is increasingly becoming more important in today's world, whereas energy sources are drastically decreasing. One of the most valuable energy sources is hydro energy. Because of limited energy sources and excessive energy usage, cost of energy is rising. Among the electricity generation units, hydroelectric power plants are very important, since they are renewable energy sources and they have no fuel cost. To decide whether a hydroelectric power plant investment is feasible or not, project cost and amount of electricity generation of the investment should be precisely estimated. In this paper, fifty four hydroelectric power plant projects are analysed by using multiple regression and artificial neural network tools. As a result, two cost estimation models have been developed to estimate the hydroelectric power plant project cost in early stages of the project.

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Section: Cost Prediction Techniquesmentioning

confidence: 99%

An Early Cost Estimation Model for Hydroelectric Power Plant Projects Using Neural Networks and Multiple Regression Analysis

Gunduz

Sahin²

2015

Journal of Civil Engineering and Management

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“…It is no surprise then that risk, simply defined here as the measure of exposure to financial loss, or gain (Akintoye 2000), has been heavily cited as one of the main causes of failure to meet cost targets on construction projects (Skitmore and Ng 2003, Öztas 2004, Okmen and Öztas 2010.…”

Section: Risk and Uncertaintymentioning

confidence: 99%

“…Causes of cost growth have been attributed to several sources including improperly managed risk and uncertainty (Okmen and Öztas 2010), scope creep (Love et al 2011, Gil andLundrigan 2012), optimism bias (Lovallo andKahneman 2003, Jennings 2012) and suspicions of foul-play and corruption (Wachs 1990, Flyvbjerg 2009). While not attempting to provide a definitive list of all possible sources, the following section of the paper provides a synthesis of mainstream arguments on the causes of cost growth to provide a holistic view of the subject.…”

Section: Sources Of Cost Growthmentioning

confidence: 99%

Rethinking construction cost overruns: cognition, learning and estimation

Ahiaga-Dagbui

Smith

2014

Journal of Financial Management of Property and Construction

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Purpose – Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost overruns, and the dynamics between cognitive dispositions, learning and estimation. A cost prediction model has also been developed using data mining for estimating final cost of projects. The paper aims to discuss these issues. Design/methodology/approach – A mixed-method approach was adopted: a qualitative exploration of the causes of cost overrun followed by an empirical development of a final cost model using artificial neural networks. Findings – A conceptual model to distinguish between the often conflated causes of underestimation and cost overruns on large publicly funded projects. The empirical model developed in this paper achieved an average absolute percentage error of 3.67 percent with 87 percent of the model predictions within a range of ±5 percent of the actual final cost. Practical implications – The model developed can be converted to a desktop package for quick cost predictions and the generation of various alternative solutions for a construction project in a sort of what-if analysis for the purposes of comparison. The use of the model could also greatly reduce the time and resources spent on estimation. Originality/value – A thorough discussion on the dynamics between cognitive dispositions, learning and cost estimation has been presented. It also presents a conceptual model for understanding two often conflated issues of cost overrun and under-estimation.

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“…A potential downside of experienced-based estimation is the difficulty in thoroughly evaluating the complex relationships between the many cost influencing variables already identified in this paper, or its inability to quickly generate different cost alternatives in a sort of what-if analysis. Furthermore, as noted by Okmen and Öztas (2010) in their research on cost analysis within an environment of uncertainty, traditional cost estimation i.e. the estimation of the cost of labour and materials and making allowance for profits and overheads for individual construction items, is deterministic by nature.…”

Section: Cost Overrunsmentioning

confidence: 99%

Dealing with construction cost overruns using data mining

Ahiaga-Dagbui

Smith

2014

Construction Management and Economics

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One of the main aims of any construction client is to procure a project within the limits of a predefined budget. However, most construction projects routinely to overrun their cost estimates. Existing theories on construction cost overrun suggest a number of causes ranging from technical difficulties, optimism bias, managerial incompetence and strategic misrepresentation. However, much of the budgetary decision making process in the early stages of a project is carried out in an environment of high uncertainty with little available information for accurate estimation. Using nonparametric bootstrapping and ensemble modelling in artificial neural networks, final project cost forecasting models were developed with 1600 completed projects in this experimental research. This helped to extract information embedded in data on completed construction projects, in an attempt to address the problem of dearth of information in the early stages of a project. 92% of the 100 validation predictions were within ±10% of the actual final cost of the project whiles 77% were within ±5% of actual final cost. This indicates the model's ability to generalise satisfactorily when validated with new data. The models are being deployed within the operations of the industry partner involved in this research to help increase the reliability and accuracy of initial cost estimates.

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Construction cost analysis under uncertainty with correlated cost risk analysis model

Cited by 34 publications

References 13 publications

An Early Cost Estimation Model for Hydroelectric Power Plant Projects Using Neural Networks and Multiple Regression Analysis

An Early Cost Estimation Model for Hydroelectric Power Plant Projects Using Neural Networks and Multiple Regression Analysis

Rethinking construction cost overruns: cognition, learning and estimation

Dealing with construction cost overruns using data mining

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