A factor-based probabilistic cost model to support bid-price estimation

Wang, Wei‐Chih; Wang, Shih-Hsu; Tsui, Yu-Kun; Hsu, Ching-Hsiang

doi:10.1016/j.eswa.2011.11.049

Cited by 15 publications

(9 citation statements)

References 33 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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“…As cost estimation of construction projects is done under great uncertainty, it is of interest then to examine risk factors in cost estimation of building construction projects so as to have strategy for mitigation. This is important as accurate cost estimates are linked to project success [5] and [6].Accurate cost estimates are acknowledged as key for contractors to remain competitive in business [7]. The significance of accurate cost estimates in the construction industry cannot be overemphasized; as cost estimates are an integral component in the prioritization, planning and control of development projects.…”

Section: Introductionmentioning

confidence: 99%

Risk Factors in Cost Estimation: Building Contractors’ Experience

Eliufoo¹

2018

AJCEA

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The study has examined risk factors in cost estimation of building construction projects. An investigation was made of risk emanating from risk factors as perceived by building contractors. Data was collected by use of questionnaires were a total of 61 questionnaires were administered to building contractors. The analysis considered four categories of risk factors: construction, physical, financial and estimator's risk factors. The results show estimator's risk factors as perceived to have the highest risk on cost estimates. Overall respondents view indicate all four categories of risk factors have an influence to project performance at a range of moderate to high. The study has also established potential risks that originate from risk factors. Such risks are noted to work against the achievement of project goals and include amongst others, project delays, cost overrun and performance failures. It is hence recommended by the study for not only contractors to be vigilant in mitigation of risk factors, but clients as well should also take this initiative as they are bound to suffer when inaccurate cost estimates are committed to a project.

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“…Unit costs decision-making for each bid item, and the total cost of the bid consists of four steps [18] - [20]:…”

Section: Selected Literature Reviewmentioning

confidence: 99%

Triangular distribution and PERT method vs. payoff matrix for decision-making support in risk analysis of construction bidding: A case study

Mirkovic¹

2020

FACTA U SERIES ARCHI

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Decision-making in construction bidding represents a complex process due to the present risk. Risk or uncertainty cannot be ignored and should be treated as a constituent of decision-making. The paper aims to emphasize the importance of probability theory by comparing insufficiently applicable methods in practical bidding. The triangular distribution and the PERT method belong to three-point estimate techniques, while payoff matrices represent a multi-criteria approach. Also, selected methods belong to quantitative techniques for risk cost analysis. Still, the risk costs determination of the unit costs and the total costs of bids is often based on an intuitive approach. Therefore, compared results of the triangular distribution, PERT method, and payoff matrix techniques (minimin, minimax, expected monetary value, and expected opportunity loss) indicate the significance of risk costs estimating in tendering. The analysis of the results showed some overlaps in risk costs values obtained by the PERT method and expected monetary value technique. Those are due to the specificity of the chosen practical example and cannot be adopted as a rule. This means that selected methods and techniques are very useful for all bid estimation. The paper proved the complexity of decision-making, where the primary goal is to award a contract.

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A factor-based probabilistic cost model to support bid-price estimation

Cited by 15 publications

References 33 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

Risk Factors in Cost Estimation: Building Contractors’ Experience

Triangular distribution and PERT method vs. payoff matrix for decision-making support in risk analysis of construction bidding: A case study

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