營建專案工程時程預測之研究 ─ 以SRC工程專案為例

The Study of Forecasting Construction Schedule for Building Projects ─ Based on Cases of SRC Projects

林敏朝(Ming-Chiao Lin)

指導教授 ： 曾惠斌

共同指導教授 ： 荷世平(S. Ping Ho)

國立臺灣大學/工學院/土木工程學研究所/博士(2011年)

https://doi.org/10.6342/NTU.2011.01839

摘要

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。

關鍵字

SRC營建專案 ； 預測 ； 灰動態預測模型 ； 複迴歸

並列摘要

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.

並列關鍵字

SRC building project ； Forecast ； Grey dynamic prediction model

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