Reliable Time Contingency Estimation Based on Adaptive Neuro-Fuzzy Inference System in Construction Projects
Project scheduling is one of the most essential processes and plays a critical role in determining the success of construction projects. The reliable time contingency enables project planners to effectively address uncertainties and various types of risks that may affect the project duration. The tr...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10225043/ |
| _version_ | 1827856441662242816 |
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| author | Tanitchet Doungsoma Paijit Pawan |
| author_facet | Tanitchet Doungsoma Paijit Pawan |
| author_sort | Tanitchet Doungsoma |
| collection | DOAJ |
| description | Project scheduling is one of the most essential processes and plays a critical role in determining the success of construction projects. The reliable time contingency enables project planners to effectively address uncertainties and various types of risks that may affect the project duration. The traditional scheduling technique such as deterministic methods and probabilistic methods may not be suitable when used for planning construction projects with uncertainties and risks. This paper proposes a new framework that integrates risk management into project scheduling to establish a more reliable project schedule. The proposed model adopts the adaptive neuro-fuzzy inference system (ANFIS) called the ANFIS-TOOL to model the possibility of risk occurrences integrated with project scheduling in terms of risk lag time (ANFIS-RLT) to generate risk-integrated project duration (RPD). The learning capabilities of adaptive neural networks are utilized to adjust the parameters of the model according to the fuzzy rules, aiming to achieve the most suitable representation of the model. In a real-life case study, a sheet pile wall with a temporary bracing system (SPBS) was applied to demonstrate the application of this technique. The root mean square error (RMSE) was used to validate the accuracy of the model before being applied to real construction projects with a high degree of accuracy. This model can be used as an excellent tool to generate a more reliable schedule baseline in construction projects. |
| first_indexed | 2024-03-12T12:24:48Z |
| format | Article |
| id | doaj.art-8b1fc7e908794ef0a4aff0d1921e617f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T12:24:48Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8b1fc7e908794ef0a4aff0d1921e617f2023-08-29T23:00:34ZengIEEEIEEE Access2169-35362023-01-0111904309044810.1109/ACCESS.2023.330695910225043Reliable Time Contingency Estimation Based on Adaptive Neuro-Fuzzy Inference System in Construction ProjectsTanitchet Doungsoma0https://orcid.org/0009-0000-3094-1117Paijit Pawan1Department of Civil Engineering, School of Engineering, Sripatum University, Bangkok, ThailandDepartment of Civil Engineering, School of Engineering, Sripatum University, Bangkok, ThailandProject scheduling is one of the most essential processes and plays a critical role in determining the success of construction projects. The reliable time contingency enables project planners to effectively address uncertainties and various types of risks that may affect the project duration. The traditional scheduling technique such as deterministic methods and probabilistic methods may not be suitable when used for planning construction projects with uncertainties and risks. This paper proposes a new framework that integrates risk management into project scheduling to establish a more reliable project schedule. The proposed model adopts the adaptive neuro-fuzzy inference system (ANFIS) called the ANFIS-TOOL to model the possibility of risk occurrences integrated with project scheduling in terms of risk lag time (ANFIS-RLT) to generate risk-integrated project duration (RPD). The learning capabilities of adaptive neural networks are utilized to adjust the parameters of the model according to the fuzzy rules, aiming to achieve the most suitable representation of the model. In a real-life case study, a sheet pile wall with a temporary bracing system (SPBS) was applied to demonstrate the application of this technique. The root mean square error (RMSE) was used to validate the accuracy of the model before being applied to real construction projects with a high degree of accuracy. This model can be used as an excellent tool to generate a more reliable schedule baseline in construction projects.https://ieeexplore.ieee.org/document/10225043/Adaptive neuro-fuzzy inference systemrisk managementtime contingencyscheduling |
| spellingShingle | Tanitchet Doungsoma Paijit Pawan Reliable Time Contingency Estimation Based on Adaptive Neuro-Fuzzy Inference System in Construction Projects IEEE Access Adaptive neuro-fuzzy inference system risk management time contingency scheduling |
| title | Reliable Time Contingency Estimation Based on Adaptive Neuro-Fuzzy Inference System in Construction Projects |
| title_full | Reliable Time Contingency Estimation Based on Adaptive Neuro-Fuzzy Inference System in Construction Projects |
| title_fullStr | Reliable Time Contingency Estimation Based on Adaptive Neuro-Fuzzy Inference System in Construction Projects |
| title_full_unstemmed | Reliable Time Contingency Estimation Based on Adaptive Neuro-Fuzzy Inference System in Construction Projects |
| title_short | Reliable Time Contingency Estimation Based on Adaptive Neuro-Fuzzy Inference System in Construction Projects |
| title_sort | reliable time contingency estimation based on adaptive neuro fuzzy inference system in construction projects |
| topic | Adaptive neuro-fuzzy inference system risk management time contingency scheduling |
| url | https://ieeexplore.ieee.org/document/10225043/ |
| work_keys_str_mv | AT tanitchetdoungsoma reliabletimecontingencyestimationbasedonadaptiveneurofuzzyinferencesysteminconstructionprojects AT paijitpawan reliabletimecontingencyestimationbasedonadaptiveneurofuzzyinferencesysteminconstructionprojects |