Developing a Fuzzy Inference Model for Construction Project Risk Management in Iraq
The construction industry is considered a high-risk business. Risk management is one of the most influential methods used in construction project management to increase the chances of delivering the project successfully, Risk Assessment (RA) is necessary to help organizations identify and mitigate...
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| Format: | Article |
| Language: | English |
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middle technical university
2023-09-01
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| Series: | Journal of Techniques |
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| Online Access: | https://journal.mtu.edu.iq/index.php/MTU/article/view/1478 |
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| author | Gusson H. Al-Momen Redvan Ghasemlounia |
| author_facet | Gusson H. Al-Momen Redvan Ghasemlounia |
| author_sort | Gusson H. Al-Momen |
| collection | DOAJ |
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The construction industry is considered a high-risk business. Risk management is one of the most influential methods used in construction project management to increase the chances of delivering the project successfully, Risk Assessment (RA) is necessary to help organizations identify and mitigate risks; therefore, this paper suggests a framework for developing an intelligent RA. There are many Risk Factors (RF) that affect construction projects, and they vary from one country to another. In this paper, a questionnaire of forty-one questions about RF was performed; its evaluation criteria are risk probability and its impact on cost, time, and quality, this questionnaire relied on several experts’ opinions to identify the most common RF affecting Iraqi construction projects. The collected linguistic data were converted into a triangular fuzzy number. Qualitative Risk Analysis was performed to assess the priority of the identified risks; while the Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed as the intelligent model. The training outcome produced three Fuzzy Inference Systems (FISs) models evaluated using the fuzzy designer application and tested using the fuzzy designer app and MATLAB Simulink to evaluate their accuracy and reliability. Finally, a set of corrective actions were suggested to facilitate the task for users.
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| format | Article |
| id | doaj-art-3daaf874159a473eabc51bdc6f982698 |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | middle technical university |
| record_format | Article |
| series | Journal of Techniques |
| spelling | doaj-art-3daaf874159a473eabc51bdc6f9826982025-01-19T10:59:04Zengmiddle technical universityJournal of Techniques1818-653X2708-83832023-09-015310.51173/jt.v5i3.1478Developing a Fuzzy Inference Model for Construction Project Risk Management in IraqGusson H. Al-Momen0Redvan Ghasemlounia1Faculty of Engineering, Istanbul Gedik Univ., Istanbul, 34876, TurkeyFaculty of Engineering, Istanbul Gedik Univ., Istanbul, 34876, Turkey The construction industry is considered a high-risk business. Risk management is one of the most influential methods used in construction project management to increase the chances of delivering the project successfully, Risk Assessment (RA) is necessary to help organizations identify and mitigate risks; therefore, this paper suggests a framework for developing an intelligent RA. There are many Risk Factors (RF) that affect construction projects, and they vary from one country to another. In this paper, a questionnaire of forty-one questions about RF was performed; its evaluation criteria are risk probability and its impact on cost, time, and quality, this questionnaire relied on several experts’ opinions to identify the most common RF affecting Iraqi construction projects. The collected linguistic data were converted into a triangular fuzzy number. Qualitative Risk Analysis was performed to assess the priority of the identified risks; while the Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed as the intelligent model. The training outcome produced three Fuzzy Inference Systems (FISs) models evaluated using the fuzzy designer application and tested using the fuzzy designer app and MATLAB Simulink to evaluate their accuracy and reliability. Finally, a set of corrective actions were suggested to facilitate the task for users. https://journal.mtu.edu.iq/index.php/MTU/article/view/1478Risk ManagementRisk AssessmentProbabilityFuzzyAdaptive Neuro-Fuzzy Inference System (ANFIS) |
| spellingShingle | Gusson H. Al-Momen Redvan Ghasemlounia Developing a Fuzzy Inference Model for Construction Project Risk Management in Iraq Journal of Techniques Risk Management Risk Assessment Probability Fuzzy Adaptive Neuro-Fuzzy Inference System (ANFIS) |
| title | Developing a Fuzzy Inference Model for Construction Project Risk Management in Iraq |
| title_full | Developing a Fuzzy Inference Model for Construction Project Risk Management in Iraq |
| title_fullStr | Developing a Fuzzy Inference Model for Construction Project Risk Management in Iraq |
| title_full_unstemmed | Developing a Fuzzy Inference Model for Construction Project Risk Management in Iraq |
| title_short | Developing a Fuzzy Inference Model for Construction Project Risk Management in Iraq |
| title_sort | developing a fuzzy inference model for construction project risk management in iraq |
| topic | Risk Management Risk Assessment Probability Fuzzy Adaptive Neuro-Fuzzy Inference System (ANFIS) |
| url | https://journal.mtu.edu.iq/index.php/MTU/article/view/1478 |
| work_keys_str_mv | AT gussonhalmomen developingafuzzyinferencemodelforconstructionprojectriskmanagementiniraq AT redvanghasemlounia developingafuzzyinferencemodelforconstructionprojectriskmanagementiniraq |