演化式人工智慧建立專案實獲完工工期推論模式之研究

Evolutionary Artificial Intelligence Based Earned Schedule Inference Model for Construction Project

10.3966/101632122018060104005

鄭明淵(Min-Yuan Cheng)；張于漢(Yu-Han Chang)；Doddy Prayogo；吳建燁(Jiuan-Ye Wu)

工期預測 ； 實獲值管理 ； SOS-LSSVM ； Prediction ； Duration ； Earned Value Management ； SOS-LSSVM

建築學報

104期（2018 / 06 / 30）

73 - 87

繁體中文

施工過程中，受限於環境、天氣等眾多因素影響，造成完工工期經常難以準確掌控，施工單位在預測工期時，必須仰賴過去之經驗，無法即時反映影響工期之因素並利用工程現況客觀地預測完工工期。本研究以建築工程之建築物完工工期為研究標的，不包含機電、裝修工程等工期，應用演化式人工智慧作為推論模式之核心SOS-LSSVM(Symbiotic Organisms Search-Least Squares Support Vector Machine)，透過案例學習發展建立專案實獲完工工期推論模式找出每期輸入變數與待完工成本之間的映射關係，進而計算預估完工工期(Estimate Schedule At Completion, ESAC)。藉此作為施工過程中作為時程管控的參考依據，以達到提前預警的目的。結果顯示本研究發展之模式RMSE低於0.03、MAPE低於10%、MAE低於3%及相關係數達0.99，相較傳統實獲值工期預測及其他人工智慧模式具較佳預測準確率。經實際案例分析對於管理者能夠有效進行時程之管控且降低工程成本。

Because of factors such as the environment and weather during a construction process, accurate control of schedule at completion (SAC) is often difficult. Builders must rely on past experience to predict the project duration. Thus, they often cannot react punctually to factors affecting the construction duration or predict objectively the SAC by using the project's current progress. This study developed an SAC inference model for building structure using the basis evolutionary artificial intelligence - Symbiotic Organisms Search-Least Squares Support Vector Machine (SOS-LSSVM). Through training with these historical cases, it was used to map the relationships between the input variables and the cost of construction work to be completed. The learning results indicated good performance, with Root Mean Square Error (RMSE) of less than 0.03, a Mean Absolute Percentage Error (MAPE) of less than 10% , d a Mean Absolute Error (MAE) of less than 3% and a correlation of 0.99, proving the SOS-LSSVM model as more reliable than the currently prevailing method. In case study, the proposed model for provides more accurate results for assisting managers with schedule and cost management.

1. Anbari, F.(2003).Earned Value Method and Extensions.Project Manage Journal,34(4),12-23.
2. Breiman, L.,Friedman, J.,Olshen, R.,Stone, C.(1984).Classification and Regression Trees.NY:Chapman & Hall/CRC.
3. Chan, W. M.(1998).Hong Kong,The University of Hong Kong.
4. Cheng, M. Y.,Chiu, C. K.,Chiu, Y. F.,Wu, Y. W.,Syu, Z. L.,Prayogo, D.,Lin, C. H.(2014).SOS Optimization Model for Bridge Life Cycle Risk Evaluation and Maintenance Strategies.Journal of the Chinese Institute of Civil and Hydraulic Engineering,26(4),293-308.
5. Cheng, M. Y.,Hoang, N. D.,Wu, Y. W.(2013).Hybrid intelligence approach based on LS-SVM and differential evolution for construction cost index estimation: A Taiwan case study.Automation in Construction,35,306-313.
6. Cheng, M. Y.,Prayogo, D.(2014).Symbiotic Organisms Search: A New Metaheuristic Optimization Algorithm.Computers & Structures,139,98-112.
7. Cheng, M. Y.,Prayogo, D.,Tran, D. H.(2015).Optimizing Multiple-Resources Leveling in Multiple Projects Using Discrete Symbiotic Organisms Search.Journal of Computing in Civil Engineering,30(3),04015036.
8. Cheng, M. Y.,Wu, Y. W.(2009).Evolutionary support vector machine inference system for construction management.Automation in Construction,18(5),597-604.
9. Daniel, W.,Chan, M.,Kumaraswamy, M. M.(1996).An Evaluation of Construction Time Performance in the Building Industry.Building and Environment,31(6),569- 578.
10. Henderson, K.(2004).Further Developments in Earned Schedule.The Measurable News,Spring,15-6-20-2.
11. Jacob, D.(2003).Forecasting Project Schedule Completion with Earned Value Metrics.The Measurable News,7-9.
12. Kaming, P. F.,Olomolaiye, P. O.,Holt, G. D.,Harris, F. C.(1997).Factors influencing construction time and cost overruns on high-rise projects in Indonesia.Construction Management and Economics,15,83-94.
13. Kass, G. V.(1980).An Exploratory Technique for Investigating Large Quantities of Categorical Data.Journal of the Royal Statistical Society, Series C: Applied Statistics,29(2),119-127.
14. Lin, M. C.,Tserng, H. P.,Ho, S. P.,Young, D. L.(2011).Developing a construction-duration model based on a historical dataset for building project.Journal of Civil Engineering and Management,17(4),529-539.
15. Lipke, W.(2003).Schedule is different.The Measurable News,31-34.
16. Nkado, R. N.(1995).Construction time-influencing factors: The contractor's perspective.Construction Management and Economics,13(1),81-89.
17. Prayogo, D.(2015).Taipei,Department of Construction Engineering, National Taiwan University of Science and Technology.
18. Shi, H. W.,Li, W. Q.(2009).The Grey Relational Analysis on Building Construction Duration Cases.International Conference on Future Bio Medical Information Engineering,China:
19. Suykens, J.(2002).Least Square Support Vector Machines.World Scientific Publishing Co. Pte.Ltd..
20. Tran, D. H.,Cheng, M. Y.,Prayogo, D.(2016).A Novel Multiple Objective Symbiotic Organisms Search (MOSOS) for Time-Cost-Labor Utilization Tradeoff Problem.Knowledge-Based Systems,94,132-145.
21. Vandevoorde, S.,Vanhoucke, M.(2006).A comparison of different project duration forecasting methods using earned value metrics.International Journal of Project Management,24,289-302.
22. 李宗憲(2008)。臺南市=Tainan, Taiwan，國立成功大學高階管理碩士在職專班=EMBA, National Cheng Kung University。
23. 林哲宏(2010)。嘉義縣=Chiayi, Taiwan，國立中正大學高階主管管理研究所=EMBA, National Chung Cheng University。
24. 曾清枝(2014)。專案管理：以系統手法管理專案。臺北市=Taipei, Taiwan:雙葉書廊=Yeh Yeh Book Gallery。

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