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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曾惠斌(H. Ping Tserng) | |
dc.contributor.author | Ming-Chiao Lin | en |
dc.contributor.author | 林敏朝 | zh_TW |
dc.date.accessioned | 2021-06-15T06:46:18Z | - |
dc.date.available | 2014-07-25 | |
dc.date.copyright | 2011-07-25 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-06-20 | |
dc.identifier.citation | 1. Aibinu, A. A.; OdeyinkaH.A., Construction Delays and Their Causative Factor in Nigeria, Journal of Construction Engineering and Management 132(7) (2006) pp667-677.
2. Al-Khalil, M. I.; Al-Ghafly, M. A., Cause of Delay of Construction Projects in Saudi Arabia, Construction Management and Economics 17(5) (1999) pp647-655. 3. Barraza, G. A., Back, W. E., and Mata, F., Probabilistic monitoring of project performance using SS-curves. J. Constr. Engrg. and Mgmt., ASCE, March/April, (2000) pp142-148. 4. Berny, J., Howes, R., Project management control using real-time budgeting and forecasting models. Construction Papers, 2 (1) (1982) pp19-40. 5. Blyth, K.; Lewis, J., Predicting Project and Activity Duration for Building in the U.K, Journal of Construction Research 5(2) (2004) pp329-347. 6. Blyth, Karl and Kaka, Ammar, A novel multiple linear regression model for forecasting S-curves. Eng. Constr. and Archi. Mgmt., 13 (1) (2006) 82-95. 7. Bromilow, F. J., Contract Time Performance – Expectations and Reality, Building Forum (1969)1. 70. 8. Chan, D. W. M.; Kumaraswamy, M. M., A Comparative Study of Causes of Time Overruns in Hong Kong Construction Project, International Journal of Project Management 15(1) (1997) pp55-63. 9. Chan, D. W. M.; Kumaraswamy, M. M., An Evaluation of Construction Time Performance in the Building Industry, Building and Environment 31(6) (1996) pp569-78. 10. Chan, D. W. M.; Kumaraswamy, M. M., Forecasting Construction Duration for Public Housing Projects : a Hong Kong Perspective, Building and Environment 34(5) (1999a) pp633 -46. 11. Chan, D. W. M.; Kumaraswamy, M. M., Modeling and Predicting Construction Durations in Hong Kong Public Housing, Construction Management and Economics (17) (1999) pp351-362. 12. Chang, T. C., Wen, K. L., and Hsu, F. Y., An artificial grey-garch model for transmission of return volatility in NASDAQ. J. of Grey System, 7 (1) (2004) pp28-37. 13. Chao, L. C. and Chien, C. F., Estimating Project S-Curves Using Polynomial Function and Neural Networks. J. Constr. Eng. and Mgmt., ASCE, March, (2009) pp169-77. 14. Charles, W. and Chase J., Composite forecasting: Combining forecasts for improved accuracy. J. of Busni. For., Summer, (2000) pp19-22. 15. Cheng, K. H. and Chang, W. C., A grey mathematical model for earthquake forecasting. J. of Grey System, 4(2) (2001) pp131-149. 16. Clemen, Robert T., Combining forecasting: A review and annotated bibliography. J. of Forecasting, 5, (1989) pp559-83. 17. Deng J. L., Control problem of grey system. Syst. Control Letter, (1) (5) (1982) pp288-294. 18. Deng J. L., Introduction to Grey System Theory. J. of Grey System, 1(1989) pp1- 24. 19. Deng J. L., The Primary Method of Grey System Theory. ISBN 7-5609-3436-6, 2nd, , (2005) Huachong Press, Wuhan, China. 20. Dissanayaka, S. M.; Kumaraswamy, M. M. 1999., Comparing Contributors to Time and Cost Performance in Building Projects, Building and Environment 34(1) p31-42. 21. Enshassi, A.; Mohamed, S.; Abushaban, S., Factors Affecting the Performance of Construction Projects in the Gaza Strip, Journal of civil engineering and management 15(3) (2009) pp269-280. doi:10.3846/139237302009.15 pp269-280. 22. Edmundas Kazimieras Zaradskas; Zenonas Turskis; Jolanta Tamosaitiene. RISK ASSESEMENT OF ONSTRUCTION PROJECT, Journal of civil engineering and management 16(1):33-46. doi:10.3846/1392-3730. (2010) pp16.33-46. 23. Goldratt, Eliyahu M., “Critical Chain.” North River Press. (1977). 24. Hsu C. C. and Chen C. T. A., modified grey forecasting model for long-term prediction” J. of the Chinese Insti. of Eng., 26(3) (2003) pp301-308. 25. Hsu L. C., “Applying the grey prediction model to the globe integrated circle industry.” Techno. Forecasting and Social Change, (70) (2003) pp563-574. 26. Hsu, P. F. and Hsu, M. G. Forecasting manpower supply and demand of healthcare professional in Taiwan using the grey theory. Pro. The 10th National Conference of Grey Theory & Application, Taiwan, (2005) pp149-156. 27. Hudson, K.W., DHSS expenditure forecasting method. Chartered Surveyor ─ Building and Quantity Surveying Quarterly, 5 (1978) pp42-45. 28. Kaka, A. P., and Price, A.D.F. Modeling standard cost commitment curves for contractors’ cash flow forecasting. Constr. Mgmt. and Econom., ASCE, 11 (1993) pp271-283. 29. Kaka, A. P., The development of a benchmark model that uses historical data for monitoring the progress of current construction projects Engineering. Constr. and Archi. Mgmt., 6/3, (1999) pp256-266. 30. Kenley, R., and Wilson, O.D., A construction project cash flow model – an idiographic approach. Constr. Mgmt. and Econom., ASCE, 4 (1986) pp213-232. 31. Kumaraswamy M. M; Chan D. W. M., Determinants of Construction Duration, Construction Management and Economics 13(3) 1995 pp209-217. 32. Kaka, A.; Price, A. D. F., Relationship between Value and Duration of Construction Projects, Construction Management and Economics 9(4) (1991) pp383-400. 33. Khosrowshahi, F.; Kaka, A. P., Estimation of Project Total Cost and Duration for Housing Project in U.K, Building and Environment 31(4) (1996) pp373-383. 34. Kung, C. Y., Kung, C. J. and Tsai, S. Y., Study of computer game forecasting in Taiwan market application of grey prediction model. J. of Busin. and Strategy, 3(2) (2003) pp1-19. 35. Jagboro, G. O.; Ogunsemi, D. R., Time-Cost Model for Building Projects in Nigeria, Construction Management of Economics 24 (2006) pp253-258. 36. Liang, M. T., Zhao, G. F.,Chang, C. W., and Liang, C. H., Evaluating the Carbonation Damage to Concrete Bridges Using a Grey Forecasting Model Combined with a Statistical Method. J. of the Chinese Institute of Eng., 24 (1) (2001) pp85-94. 37. Li, G. D., Yamaguchi D., and Nagai M., Application of GM (1, 1)- Markove chain combined model to China’s automobile industry. J. of Industrial and Systems Eng., Inter-science Enterprise, 2( 3) (2007) pp327-347. 38. Li, G. D., Yamaguchi D., Nagai M. and Masuda S.,A Prediction Model Using Hybrid Grey GM (1, 1) Model. J. of Grey System, 11(1) (2008) pp19 - 26. 39. Lin, Y. H. and Lee P. C., Novel high-precision grey forecasting model. Automation in Constr., 16 (2007) pp771-77. 40. Lin, Y. H., Lee P. C. and Chang, T. P., Adoptive and high-precision grey forecasting model.” Expert System with Application, 36 (2009) pp9658-62. 41. Love, P. E. D.; Tse, R. Y. C.; Edwards, D. J. Time-Cost Relationships in Australian Building Construction Projects, Journal of Construction Engineering and Management 131(2) (2005) pp187-194. 42. Lyer, K. C.; Jha, K. N., Critical Factors Affecting Schedule Performance: Evidence from India Construction Projects, Journal of Construction Engineering and Management 132(8) (2006) pp871-881. 43. Makridakis, S., et al., The accuracy of extrapolation (time series) method: Results of a forecasting competition.” J. of Forecasting, 1 (1982) pp111-153. 44. Makridakis, S.,Wheelwright, S.C. &Hyndman, R.j., Forecasting: methods and application” 3rd edn, John Wiley & Sons, New York (1998). 45. Miskawi, Z. An S-curve equation for project control. Constr. Mgmt and Econom., ASCE,7 (1989) pp115-124. 46. Ng, S. T.; Mak, M. M. Y.; Skitmore, R. M.; Lam, K. C.; Varnarm, M., The Predictive Ability of Bromilow’s Time-Cost Model, Construction Management of Economics 19(2) (2001) pp165-173. 47. Nkado, R. N., Construction Time Information System for the Building Industry, Construction Management of Economics 10 (1992) pp489-509. 48. Nkado, R. N., Construction Time-influencing Factors: the Contractor’s Perspective, Construction Management of Economics 13 (1995) 81-89. 49. Neter, J.; Kutner, M. H.; Nachtsheim, C. J.; Wasserman, W., Applied Linear Regression Models. 3rd ed. Irwin, Burr Ridge, Illinois. (1996) pp330-435. 50. Odusami; Olusanya. Client’s Contribution to Delays on Building Projects, The Quantity Surveyor 30 (2000) pp30-3. 51. Ogunsemi, D. R.; Jagboro, G.O., Time-Cost Model for Building Projects in Nigeria; Construction Management of Economics 24 (2006) pp253-258. 52. Relamila, P. D.; Hall, K. A., Total Systems Intervention: an Integrated Approach to Time, Cost and Quality Management, Construction Management and Economics 13 (1995) pp235-241. 53. Russell, J., S., Jaselskis, E., J., Lawrence, S., P., Continuous Assessment of Project Performance. J. Constr. Engrg. and Mgmt., ASCE, March, (1997). pp64-71. 54. Skitmore, Martin, Parameter prediction for cash flow forecasting models. Constr. Magmt. and Econom., ASCE, 10 (1992) pp397-413. 55. Skitmore, Martin, A method for forecasting owner monthly construction project expenditure flow” J. of Forecasting 14(1998) pp17-34. 56. Siegel, Andrew F. Practical Business Statistics. 5th ed. (2003) McGraw – Hill. 57. Stephn A. Delurgio., Forecasting Principles and Applications. (1998) McGraw – Hill. 58. Tuker, S. N., A single alternative formula for Department of Health and Social Security S-Curves.” Constru. Magmt. and Econom., ASCE, 6, (1988) pp13-23. 59. Wang, Z. X., Dang, Y. G., Liu, S. F., and Zhou, J., The Optomization of Background Value in GM (1, 1) Model. J. of Grey System, 10(2) (2007) pp69 – 74. 60. Walker, D. H. T., An Investigation into Construction Time Project, Construction Management o f Economics 13(3) (1995) pp263-274. 61. Wheelwright, S. C. and Makridakis, S., Forecasting Methods for Management 4th ed. John Wiley & Sons, New York. (1985) pp55-64. 62. Wooldridge, Jeffrey M. 2003. Introductory Econometrics: A Modern Approach 2nd ed. South-Western of Thomson, Mason, Ohio. 264-266. 63. Yeo, K. T. and Ning, J. H., Integrating supply chain and critical chain concepts in engineer-procure-construct (EPC) projects. J. of Project Mgmt., ASCE, 20 (2002) pp253-262. 64. Yokum J. T. and Armstrong J. S., Beyond accuracy: Comparison of criteria used to select forecasting methods. J. of Forecasting, ASCE, 11 (1995) pp591-97. 65. Zhang, B. X. and Lou, J. J., A study of grey forecasting and its control analysis of grain yield. System Eng., 1(1) (1985). pp91-98. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/48104 | - |
dc.description.abstract | 營建時程一直是建築專案的基本議題,營建時程執行的逾期與延宕會導致專案成本的增加,而工期及進度是營建專案在時程管理及控制上兩大重要的內容。雖然營建業相關人員已清楚明瞭工程階段工期與進度管理的重要性,不過,迄今為止,仍然有許多營建專案合約的執行結果並不能在原訂時程內完成,並且執行期間延遲的現象也普遍性存在。專案營建逾期與延宕的情況是有不確定的情況,長久以來一直引起工程業界許多的關注。
一般工程實務上,業界評估工程需要的時間是由承辦或規劃人員的技能及主觀的認知推估出來的,或是以僅考量工程成本為唯一因素的工期模型進行推估的,或是依既有慣例規定而非就專案特性客觀的、廣泛的評估;但這些經驗、規定或模型並不適用於特殊的SRC建築專案工程時程上的推估。本篇論文,考量了工程專案本身的基本特性及外在不確定因素兩大類別的變數而建立了SRC建築專案的工期預估模型,該模型特別是將未被重視的天候及變更設計兩項因素予以量化並且納入模型內。另外,任何一件營建專案工程所面臨的環境,普遍是缺乏適用的案例資訊與情事變化頻繁等不確定因素,以及工程專案本身單一與獨特性等事實現象;而傳統統計方法需要大量的資料才能進行建立工程進度預測模型,並且所建立的模型要彈性地在工程不同階段下對變數的係數進行調變,以作為進度的預測是有困難的。因此,本篇論文提出SRC營建專案工期預測模型及可動態性地預測執行階段進度預測的新方法,並且在推論及實際運用之前,進行嚴謹的必要性的診斷後,再分別以數個實際工程案例對工期預估模型及動態進度預測方法進行準確度的測試及驗證。測驗所得到的結果顯示出:SRC建築專案實際營建所花費的時間與工期預估模型所推估的工期相當接近,另外現場最近一期實際執行進度也與進度預測方法所推估的進度相當接近,誤差大部份在10%範圍內。因此,本篇論文提出有關的營建時程的工期預測模型及進度動態性預測方法,在營建專案的運用上是具有可靠性的務實方法。 | zh_TW |
dc.description.abstract | The construction schedule is always essential issue of building project. Schedule overrun brings about project cost overrun and many disputes. Duration and progress are two important subjects of schedule management or schedule control for construction project. Although the industry participants are aware of the importance of duration and progress in the construction phase of projects, it was observed that significant part of the construction contracts had not met the stipulated period and delay still generally occurred. The construction duration overrun and delay are problematic in the construction industry and generate much concern for a long time.
In engineering practice, most methods estimating project duration in the industry depend on the subjective skill and cognition of the estimators and planners rather than on objective assessment, or duration models taken to construct projects were only considered with the construction size as measured by the final cost. In this dissertation, two types of variables, project characteristic and uncertain external factor, are incorporated into the construction duration model for SRC building projects. Uncertain external factors, whether and change order never been quantified in existing models, are specially considered in the prediction model and sign out their significance. Furthermore, there is a fact that few data, emerging changes, uncertainties and uniqueness always exist in the construction project engineering environment. Forecasting S-curve progress by conventional statistical prediction methods require a large amount of data to build progress prediction model, and is difficultly to determine the model coefficients to form a sectional model for flexibly adapting any current construction situation. A novel construction progress prediction approach based on modified grey dynamic prediction model also is proposed in this dissertation. The progress prediction approach can timely reflect real progress growth trends across different construction stages for individual construction project. In these two developing processes, necessary diagnostics and tests have been adopted to examine the aptness of the two models before inference. And then, several practical cases are respectively taken to test the accuracy of two models proposed. Results show that the actually necessary construction duration for SRC building project is considerably closed to the duration predicted by the proposed mode, and the dynamic forecasting approach proposed to forecast construction progress during construction phase is able to get better prediction accuracy almost within 10 % whether typical S-curves or practical cases. It is concluded that whether the predictive duration model or forecasting construction progress approach proposed for SRC building projects could be applicable to practical construction projects with a reasonable reliability. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T06:46:18Z (GMT). No. of bitstreams: 1 ntu-100-D93521019-1.pdf: 1318434 bytes, checksum: a29a8aa105db110cc65bc9a592f59250 (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | Abstract III
Chapter 1 Introduction 1 1.1 Research Background and Motivation 1 1.2 Problem Statement 4 1.3 Research Objectives 7 1.4 Research Methodology 8 1.4.1 Multiple Linear Regression Analysis to Construction Duration for SRC Building Project 9 1.4.2 Variables 9 1.4.2 Prediction Approaches to Forecast Construction Progress 11 1.5 Significance of Research 13 1.5.1 Construction Duration Model of SRC Building Project 13 1.5.2 Construction Progress Prediction Approach 14 1.6 Structure of the Dissertation 15 Chapter 2 Literature Review 17 2.1 Construction Duration Model and Affecting Factors 17 2.2 Construction Progress Forecasting Approach 19 2.3 Summary 24 Chapter 3 Methodology of the Model Development 25 3.1 Multiple Linear Regression Analysis to Construction Duration for SRC Building Project 25 3.1.1 Operation of Software Package 25 3.1.2 Transformation of Variables 26 3.1.3 Steps of Building Duration Model 27 3.2 Prediction Approaches to Forecast Construction Progress 29 3.2.1 Typical S-Curves 29 3.2.2 Smoothing Methods 31 3.2.3 Moving average Process 33 3.2.4 Grey Prediction Theory 33 3.2.5 The Combination Forecast 35 3.3. Summary 36 Chapter 4 Construction Duration Prediction Model Built for SRC Project 38 4.1 Sample Description 38 4.2 Omitting Outlying Sample 38 4.3 The Premises of Building Projects Carried Out in the Research 39 4.5 Model-Building Process and Variables Choice 40 4.6 Criteria for Duration Model 43 4.7 Diagnostics for Duration Model 46 4.7.1 Scatter Plot Matrix and Correlation Matrix 46 4.7.2 Test for Heteroskedasticity 48 4.7.2 Residual 48 4.7.4 Identifying Outlying Observations 50 4.7.5 Multicollinearity Diagnostics – VIF 54 4.8 Sensitivity Analysis 54 4.9 Model Validation for Duration Model 56 4.10 Effectiveness of construction duration prediction model 56 4.10.1 The Predictive Ability of the Regression Model 56 4.10.2 The Result of the Proposed Duration Prediction Model 57 4.11 Discussion 59 Chapter 5 Prediction Approaches to Forecast Construction Progress 62 5.1 Smoothing Methods 62 5.2 Time Series Moving-average Process 64 5.3 Grey Dynamic Prediction GM (1, 1) Model 66 5.4 Error’s modified model 66 5.4.1 modified model 1 67 5.4.2 modified model 2 68 5.5 The Combination of Dynamic GM(1,1) and Error’s Modified Model 69 5.6 The Steps of Forecasting Progress Prediction Model 70 5.7 Criteria for Construction Progress Prediction Approach 85 5.8 Testing for Progress Prediction Approach 93 5.8.1 Average of Absolute Error for the Grey Dynamic GM (1, 1) 93 5.8.2 Precision for the Combination of Grey Dynamic GM (1, 1) and Error’s Grey Dynamic GM (1, 1) Model 93 5.9 Effectiveness of construction progress prediction model 97 5.10 Discussion 103 Chapter 6 Conclusion and suggestion 107 6.1 In the Part of Construction Duration Model 107 6.2 In the Part of Construction Progress Prediction Approach 108 Reference | |
dc.language.iso | en | |
dc.title | 營建專案工程時程預測之研究
─ 以SRC工程專案為例 | zh_TW |
dc.title | The Study of Forecasting Construction Schedule for Building Projects ─ Based on Cases of SRC Projects | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 荷世平(S. Ping Ho) | |
dc.contributor.oralexamcommittee | 楊德良(D. L. Young),張國鎮,陳振川,郭斯傑,王明德 | |
dc.subject.keyword | SRC營建專案,預測,灰動態預測模型,複迴歸, | zh_TW |
dc.subject.keyword | SRC building project,Forecast,Grey dynamic prediction model, | en |
dc.relation.page | 116 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-06-21 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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