Risk induced contingency cost modeling for power plant projects

Islam, Muhammad; Nepal, Madhav Prasad; Skitmore, Martin; Drogemuller, Robin

doi:10.1016/j.autcon.2020.103519

Cited by 17 publications

(23 citation statements)

References 70 publications

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“…Existing models have limited applications as they demand a comprehensive risk assessment, which involves making a significant effort to elicit and compute experts' judgments for estimating and budgeting at the early stages of a project. For instance, Islam's et al (2021) use of fuzzy-BBN models for Bangladesh power plant projects requires expert judgment to address uncertainty and risk assessment, which is not only impractical and time-consuming at the project development stage, but also is subjective, vague, and imprecise, which makes the value of the models' outcomes or performances questionable. The application of ANN-based models (Gunduz & Sahin, 2015;Hashemi et al, 2019), on the other hand, is restricted by the limited project-specific cost data available and uncontrolled hidden layers in the ANN "black box".…”

Section: Discussion Of the Findingsmentioning

confidence: 99%

“…The activities or cost items of complex project infrastructure projects are also highly correlated, which cannot be handled by fuzzy set theory. Accordingly, Islam et al (2021) present a fuzzy-Bayesian belief networks (fuzzy-BBNs) approach for risk-induced CC modeling, which can handle interrelationships between the risks, and risk dynamism. However, the developed fuzzy-based models depend on expert judgment-based datasets, which are subjective, imprecise, and vague, while the collection of subjective datasets at the early stage for project budgeting is also a complex task.…”

Section: Pc and CC Prediction Modelsmentioning

confidence: 99%

“…MCS and ANN, in particular, can provide more accurate cost predictions for building and road transportation projects, where historical data are available (Elmousalami, 2020a(Elmousalami, , 2020b. Of these, MCS requires the mean and standard deviation of historical cost data from similar projects to develop a probability distribution function (Chang & Ko, 2017) -a constraint that restricts the application of MCS to power plant projects, which are less frequent and with limited access to cost datasets (Islam et al, 2021). Williams and Gong (2014) use cost data of 92 building projects with an integrated ANN and support vector machine (SVM) for better PC prediction accuracy, and Dursun and Stoy (2016) use 657 building projects for PC prediction by applying an ANN-based multistep ahead approach.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is critical to ensure the availability of both the quantity and quality of cost-data for complex power plant projects. Expert judgment-based models, such as fuzzy set theory, fuzzy expert systems, and fuzzy-Bayesian belief networks require data to be elicited from domain experts, which are sometimes vague, imprecise, subjective, and contingent on specific project characteristics (Idrus et al, 2011;Islam et al, 2021;Salah, 2015). These limitations conspire to make all these models generally impractical at the initiation and planning phase of such complex infrastructure projects as power plants.…”

Section: Introductionmentioning

confidence: 99%

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Budget and Cost Contingency Cart Models for Power Plant Projects

Arifuzzaman

Gazder

Islam

et al. 2022

Journal of Civil Engineering and Management

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Cost overruns are a ubiquitous feature of construction projects, and realistic budgeting at the development stage plays a significant role in their control. However, the application of existing models to budgeting for power plant projects is restricted by the limited amount of project-specific cost data available. This study overcomes this by using a Classification and Regression Tree (CART) approach involving mixed methods of website visits, document study, and expert opinion to predict the amount of project cost (PC) and cost contingency (CC) needed to cover probable cost increases by the use of models containing readily available project attributes and national economic parameters at the project development stage. The modeling process is demonstrated and tested with a case study involving 58 Bangladeshi power plant projects – producing average absolute errors ranging from 0.7% to 1.7% and enabling project cost, inflation rate, and GDP to be identified as significant factors affecting PC and CC modeling. The approach can be applied to predict the PC during preliminary budgeting and selecting a project type and location aligned to the country’s economic status and policy-making strategies, thus facilitating further investment decisions.

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Section: Discussion Of the Findingsmentioning

confidence: 99%

Section: Pc and CC Prediction Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Budget and Cost Contingency Cart Models for Power Plant Projects

Arifuzzaman

Gazder

Islam

et al. 2022

Journal of Civil Engineering and Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…They applied robust multivariable regression to minimize the residuals. Islam et al [41] employed an integrated fuzzy set theory and fuzzy Bayesian belief network model to risk-induced contingency cost modeling for power plant projects.…”

Section: Introductionmentioning

confidence: 99%

Development of Cost Correlations for the Economic Assessment of Power Plant Equipment

et al. 2021

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A comprehensive cost correlation analysis was conducted based on available cost correlations, and new equipment cost correlation models were proposed based on QUE$TOR modeling. Cost correlations for various types of equipment such as pumps, compressors, heat exchangers, air coolers, and pressure vessels were generated on the basis of extracted cost data. The models were derived on the basis of robust multivariable regression with the aim of minimizing the residuals by using the genetic algorithm. The proposed compressor models for both centrifugal and reciprocating types showed that the Turton cost estimation for carbon steel compressor and Matche’s and Mhhe’s data were compatible with the generated model. According to the results, the cost trend in the Turton correlation for carbon steel had a somewhat lower estimation than these correlations. Further, the cost trend of the Turton correlation for carbon steel pressure vessels was close to the presented model trend for both bullet and sphere types. The Turton cost trend for U-tube shell-and-tube heat exchangers with carbon steel shell and stainless steel tube was close to the proposed heat exchanger model. Furthermore, the Turton cost trend for the flat-plate heat exchanger using carbon steel was similar to the proposed model with a slight difference.

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AI’s Role in Project Management: An Overview of the Literature and a Research Agenda

Mariani,

Mancini

2024

Lecture Notes in Civil Engineering

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Risk induced contingency cost modeling for power plant projects

Cited by 17 publications

References 70 publications

Budget and Cost Contingency Cart Models for Power Plant Projects

Budget and Cost Contingency Cart Models for Power Plant Projects

Development of Cost Correlations for the Economic Assessment of Power Plant Equipment

AI’s Role in Project Management: An Overview of the Literature and a Research Agenda

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