Developing a risk breakdown matrix for onshore wind farm projects using fuzzy case-based reasoning

Somi, Sahand; Seresht, Nima Gerami; Fayek, Aminah Robinson

doi:10.1016/j.jclepro.2021.127572

Cited by 14 publications

(5 citation statements)

References 51 publications

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“…It predicts a new situation by remembering previous similar states and their information. This approach has been used in various fields, such as cost forecasting (Kim and Kim, 2010;Kwon et al, 2019Kwon et al, , 2020, safety (Goh and Chua, 2010), related to time and planning (Jin et al, 2016), and risk (Somi et al, 2021;Batselier and Vanhoucke, 2015). Goh and Chua (2010) believe the CBR paradigm is a humanbased approach that tries to solve new problems by recalling previous experiences.…”

Section: Case-based Reasoningmentioning

confidence: 99%

“…, 2019, 2020), safety (Goh and Chua, 2010), related to time and planning (Jin et al. , 2016), and risk (Somi et al. , 2021; Batselier and Vanhoucke, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…, 2021). Despite extensive study on cost and cost overrun forecasting, uncertainties and risks have not yet been adequately used in models such as CBR (Petheram and McMahon, 2019; Somi et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…These variables have an essential role in improving the performance of artificial intelligence models (Abioye et al, 2021;He et al, 2021). Despite extensive study on cost and cost overrun forecasting, uncertainties and risks have not yet been adequately used in models such as CBR (Petheram and McMahon, 2019;Somi et al, 2021). However, various studies have emphasized the role of uncertainties in cost management (Elfahham, 2019;Kermanshachi and Safapour, 2020;Karimi et al, 2018;Dikmen et al, 2007;Pinheiro et al, 2019;Islam et al, 2019;Jin et al, 2014;Choudhry et al, 2014;Yildiz et al, 2014).…”

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confidence: 99%

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Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Sohrabi

Noorzai

2022

ECAM

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PurposeThe present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.Design/methodology/approachThe cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.FindingsThe findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.Originality/valueThis research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.

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Section: Case-based Reasoningmentioning

confidence: 99%

“…, 2019, 2020), safety (Goh and Chua, 2010), related to time and planning (Jin et al. , 2016), and risk (Somi et al. , 2021; Batselier and Vanhoucke, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Sohrabi

Noorzai

2022

ECAM

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“…Nowadays, the development of risk assessment methodologies is clearly visible in the industry and service sector as well. One typical development direction is to combine different mathematical methodologies with a platform risk assessment methodology such as FMEA-failure mode and effect analysis [1,2], RM-risk matrix [3], HAZOP-hazard and operability analysis [4], FTA-fault tree analysis [5], etc. The typical aim of these studies is to develop the platform methodology, increasing its strengths and/or decreasing its weaknesses by adding new, typically mathematical features.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Consequences on Some Aggregation Functions of PRISM (Partial Risk Map) Risk Assessment Method

Bognár

Hegedűs

2022

Mathematics

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The PRISM (partial risk map) methodology is a novel risk assessment method developed as the combination of the failure mode and effect analysis and risk matrix risk assessment methods. Based on the concept of partial risks, three different aggregation functions are presented for assessing incident risks. Since the different aggregation functions give different properties to the obtained PRISM numbers and threshold surfaces (convex, concave, linear), the description of these properties is carried out. Similarity analyses based on the sum of ranking differences (SRD) method and rank correlation are performed and robustness tests are applied related to the changes of the assessment scale lengths. The PRISM method provides a solution for the systematically criticized problem of the FMEA, i.e., it is not able to deal with hidden risks behind the aggregated RPN number, while the method results in an expressive tool for risk management. Applying new aggregation functions, proactive assessment can be executed, and predictions can be given related to the incidents based on the nature of their hidden risk. The method can be suggested for safety science environments where human safety, environmental protection, sustainable production, etc., are highly required.

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A Sustainable Circular Economy in Energy Infrastructure: Application of Supercritical Water Gasification System

Peng

Adumene

et al. 2023

Studies in Systems, Decision and Control

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Developing a risk breakdown matrix for onshore wind farm projects using fuzzy case-based reasoning

Cited by 14 publications

References 51 publications

Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Analysis and Consequences on Some Aggregation Functions of PRISM (Partial Risk Map) Risk Assessment Method

A Sustainable Circular Economy in Energy Infrastructure: Application of Supercritical Water Gasification System

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