Dynamic Probability Analysis for Construction Schedule Using Subset Simulation
Schedule management is an essential part of construction project management. In practical management affairs, many uncertainties may lead to potential project delays and make the schedule risky. To quantify such risk, the Probabilistic Critical Path Method (PCPM) is used to compute the overdue proba...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/1567261 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561472267354112 |
|---|---|
| author | Shen Zhang Xingyu Wang |
| author_facet | Shen Zhang Xingyu Wang |
| author_sort | Shen Zhang |
| collection | DOAJ |
| description | Schedule management is an essential part of construction project management. In practical management affairs, many uncertainties may lead to potential project delays and make the schedule risky. To quantify such risk, the Probabilistic Critical Path Method (PCPM) is used to compute the overdue probability. Survey shows it could help project managers understand the schedule better. However, two critical factors limited the application of PCPM: computational efficiency and timeliness. To solve these constraints, we combined subset simulation and statistical learning to build a computationally efficient and dynamic simulation system. Numerical experiment shows that this method can effectively improve the computation efficiency without losing any accuracy and outperforms the other approaches with the same assumptions. Besides, we proposed a machine learning-based way to estimate task duration distributions in PCPM automatically. It collects real-time progress data through user interactions and learns the best PERT-Beta parameters based on these historical data. Our estimator provides our simulation system the ability to handle dynamic assessment without laborious human work. These improvements reduce the limitations of PCPM, making the application of PCPM in practical management affairs possible. |
| format | Article |
| id | doaj-art-0764c31ab9de429e95fe4045772ce8b6 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-0764c31ab9de429e95fe4045772ce8b62025-02-03T01:24:54ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/15672611567261Dynamic Probability Analysis for Construction Schedule Using Subset SimulationShen Zhang0Xingyu Wang1Central-South Architectural Design Institute Co., Ltd., Wuhan 430071, ChinaCentral-South Architectural Design Institute Co., Ltd., Wuhan 430071, ChinaSchedule management is an essential part of construction project management. In practical management affairs, many uncertainties may lead to potential project delays and make the schedule risky. To quantify such risk, the Probabilistic Critical Path Method (PCPM) is used to compute the overdue probability. Survey shows it could help project managers understand the schedule better. However, two critical factors limited the application of PCPM: computational efficiency and timeliness. To solve these constraints, we combined subset simulation and statistical learning to build a computationally efficient and dynamic simulation system. Numerical experiment shows that this method can effectively improve the computation efficiency without losing any accuracy and outperforms the other approaches with the same assumptions. Besides, we proposed a machine learning-based way to estimate task duration distributions in PCPM automatically. It collects real-time progress data through user interactions and learns the best PERT-Beta parameters based on these historical data. Our estimator provides our simulation system the ability to handle dynamic assessment without laborious human work. These improvements reduce the limitations of PCPM, making the application of PCPM in practical management affairs possible.http://dx.doi.org/10.1155/2021/1567261 |
| spellingShingle | Shen Zhang Xingyu Wang Dynamic Probability Analysis for Construction Schedule Using Subset Simulation Advances in Civil Engineering |
| title | Dynamic Probability Analysis for Construction Schedule Using Subset Simulation |
| title_full | Dynamic Probability Analysis for Construction Schedule Using Subset Simulation |
| title_fullStr | Dynamic Probability Analysis for Construction Schedule Using Subset Simulation |
| title_full_unstemmed | Dynamic Probability Analysis for Construction Schedule Using Subset Simulation |
| title_short | Dynamic Probability Analysis for Construction Schedule Using Subset Simulation |
| title_sort | dynamic probability analysis for construction schedule using subset simulation |
| url | http://dx.doi.org/10.1155/2021/1567261 |
| work_keys_str_mv | AT shenzhang dynamicprobabilityanalysisforconstructionscheduleusingsubsetsimulation AT xingyuwang dynamicprobabilityanalysisforconstructionscheduleusingsubsetsimulation |