Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project

Zhu, Bin; Yu, Lean; Geng, Zhiqiang

doi:10.1007/s10586-016-0585-6

Cited by 15 publications

(14 citation statements)

References 20 publications

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“…e first step in estimating the preliminary cost of the project is selection of the appropriate input factors, and it is vital for attaining good cost-estimation performance or improving the prediction capability of the model [12,21,22]. Accordingly, in the realm of estimation, many studies have applied various factor selection techniques, including both statistical and nonstatistical, in order to identify and select the most significant cost factors that are required for estimating the preliminary cost of projects in the highway construction industry [12,13,21,[23][24][25]. In the following paragraphs, the most cost-influencing factors and the way they are identified and selected for preliminary or top-down cost-estimation purpose are described.…”

Section: Literature Reviewmentioning

confidence: 99%

“…At this stage, the factors affecting the accuracy of the cost estimate, input variables, are identified and defined through intensive literature review and expert interview for the required numerical analysis. e study on various literature studies relevant to the cost estimation of highway construction project explored the most influential cost-estimation accuracy factors or input variables [7,12,23,25,33,[54][55][56]. It is not necessarily true that increasing the number of input variables in an early estimate may seem to improve the accuracy of the estimate [23].…”

Section: Case Study: Numerical Analysismentioning

confidence: 99%

“…Advances in Civil Engineering Zhu et al [25] obtained the key market drivers which affect cost items based on their priorities through parallel Monte Carlo simulation and Likert scale analysis for the purpose of estimating the cost of the chemical engineering construction project. is study identified four key factors, which include technology and specification standard, vender list, target market and economy, and the condition of the project site.…”

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confidence: 99%

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An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Meharie

Gariy

Mutuku

et al. 2019

Advances in Civil Engineering

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Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide-ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Case Study: Numerical Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Meharie

Gariy

Mutuku

et al. 2019

Advances in Civil Engineering

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“…Uncertainty exists almost everywhere; therefore, a probabilistic approach can counter the risks inherent in management [30]. The probabilistic approach has been primarily applied in the field of construction management to analyze construction costs [31][32][33][34][35] and construction periods [36][37][38], while research has continued in many other fields [39,40].…”

Section: Probabilistic Predictionmentioning

confidence: 99%

Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction

Jin

Kim²,

Hyun

et al. 2019

Sustainability

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Decisions made in the early stages of construction projects significantly influence the costs incurred in subsequent stages. Therefore, such decisions must be based on the life-cycle cost (LCC), which includes the maintenance, repair, and replacement (MRR) costs in addition to construction costs. Furthermore, as uncertainty is inherent during the early stages, it must be considered in making predictions of the LCC more probabilistic. This study proposes a probabilistic LCC prediction model developed by applying the Monte Carlo simulation (MCS) to an LCC prediction model based on case-based reasoning (CBR) to support the decision-making process in the early stages of construction projects. The model was developed in two phases: first, two LCC prediction models were constructed using CBR and multiple-regression analysis. Through k-fold validation, one model with superior prediction performance was selected; second, a probabilistic LCC model was developed by applying the MCS to the selected model. The probabilistic LCC prediction model proposed in this study can generate probabilistic prediction results that consider the uncertainty of information available at the early stages of a project. Thus, it can enhance reliability in actual situations and be more useful for clients who support both construction and MRR costs, such as those in the public sector.

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“…The modeling of the economic-financial risk analysis was based on the probability distribution of possible values for each input variable of the model, thus considering stochastic variables as a result of the assigned triangular parameterization (Table 1) for the simulation by Monte Carlo method. The triangular distribution was used due to the lack of historical information of the variables, which allows flexibility regarding the degree of asymmetry, since its parameters are based on the estimates of the most probable value, the minimum value and the maximum value of the variable [44,45]. The quantitative methods of investment analysis NPV, MIRR and PI were considered as output variables.…”

Section: Incorporation Of Economic and Financial Riskmentioning

confidence: 99%

Risk Analysis of the Economic Benefits of Ochroma pyramidale: A Case Study of Forest Planting in Brazil

Miyajima¹,

Barreto²,

Oliveira³

et al. 2018

JAST-B

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The forest investment projects, analyzed through the application of simulation techniques, allow measuring more accurately the benefits or losses, considering the systemic uncertainties related to forest plantation. In this perspective, the objective of this study was to identify the relative importance of sources of uncertainties in determining the net present value (NPV) of planting of Ochroma pyramidale on a forested property located in the state of São Paulo, Brazil. Thus, it assigns probability distributions to the parameters with uncertainties and applies the Monte Carlo technique to obtain the stochastic behavior of the NPV, of the modified internal rate of return (MIRR) and the profitability index (PI). The probabilistic results of the quantitative methods analyzed allowed us to conclude that for the planting O. pyramidale, with 5-year cutting age, indicated economic profitability. In addition, there is a probability that 84.6% of the project will add economic value to the invested capital.

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Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project

Cited by 15 publications

References 20 publications

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction

Risk Analysis of the Economic Benefits of Ochroma pyramidale: A Case Study of Forest Planting in Brazil

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