近年來台北都會區迅速發展，建築物朝向高層化及深層化的結果，使得施工愈形艱難，工程費用也愈加鉅額。透過執行深開挖專案工程的過程，有效地蒐集相關數據如工程經費、監測數據等，有助於量化分析深開挖工程之效益及監測管理值。本研究蒐集台北地區深開挖工程34個專案之執行預算、15個逆打深開挖專案之安全監測數據，透過統計理論、多準則決策理論、類神經網路理論及統計製程理論來量化分析，藉此建立台北地區深開挖工程之成本模式、逆打深開挖監測管理值及深開挖內支撐工法施工製程的改善方法。
台灣的營造商因資本額較低且面臨原料高漲及低獲利狀況下，如何有效地控制施工成本是增加競爭力之重要關鍵。本研究分析蒐集的案例後，發現管理費、結構工程費及裝修工程費為影響專案成本之主要因素；經由迴歸分析建立上述因數與專案總預算及總樓地版面積之成本模型，模型可以快速提供估價之?考及分析預算的合理性。應用TOPSIS評估案例的績效，發現績效較佳之專案的管理費約占總預算的10%以內。
本研究蒐集台北地區逆打深開挖案例觀測數據，以統計理論和國內外文獻及相關法規歸納出深開挖的安全監測管理值，並將9個逆打擋土壁體之資料透過類神經網路理論來作為壁體變位預測之研究。在深開挖的挖掘過程中，量測地下連續壁之壁體變位為監測擋土壁體之重要數據，觀測值的管理得以避免支撐系統的破壞。應用倒傳類神經網路理論來預測壁體最大變位，因該網路本身具有學習的能力，且無須完全了解其因果關係；透過此學習模型得以將深開挖(4~8階段)的壁體變位的觀測值被用於訓練蒐集以及證實。根據研究的結果，顯示類神經網路能合理預測連續壁壁體的最大壁體變位及其位置。類神經考量權重中總分析得知影響壁體變形因子(相對重要性%)最重要的是開挖深度(相對重要性占14.79%)。
深層化的開挖工法以內支撐系統為開挖工程中最常採用方式，因其支撐材料（H型鋼）可重複使用，較為經濟。內支撐系統之目的在於主要抵擋土結構之變形量，而整個系統以中間柱及水平支撐為主要構件。本研究以中間柱施工及水平支撐承受壓力做為施工製程管制之對象，並應用統計製程管制分析其製程能力，可有效去除失控原因，因而提升製程能力及施工安全。

This paper explores Taiwan’s construction industry in terms of its property environment as well as its profit making capabilities. Taiwan’s construction industries are generally low in capital and they face problems like expensive raw materials and low profit margins. Under such circumstances and increased competition within the construction industry, coupled with a decline in the number of such employees, there is a need to build a costing model for deep excavation projects. Thirty-four case studies from Taipei urban area’s deep excavation projects were analyzed, statistical and performance analyses were done on each case study’s execution budget. Based on the results of these two analyses, it was found that total budget and total floor area have very high correlations with budgets of management, structural construction, and finish work. The relationship between total budget and budgets of management, structural construction, and finish work and the relationship between total floor area and budgets of management, structural construction, and finish work are developed through regression analyses. Based on these regression models, the total budget and total floor area can be estimated easily once the budgets of management, structural construction, and finish work are known.
Deep excavation is widely undertaken in the construction of high-rise building foundations in urban areas. Measurement of diaphragm wall deflection is so important in deep excavation that monitoring data are always adopted to evaluate construction performance so as to avoid failures of the supporting system. This paper attempts to predict the diaphragm wall deflection in deep excavations by using a back-propagation artificial neural network (NN) learning model. Case histories of deep excavations (with 4 to 8 excavation stages) from the construction projects in Taipei Basin are collected for training and verification. From the results of this research, it is shown that the artificial NN can reasonably predict both magnitude and location of the maximum deflection of the braced wall in deep excavations.
The internal bracing system is more often in excavating the much more deep foundation, which is popular and economic because its materials can be used repeatedly. The main function of the internal bracing system is to keep out the deflection of the earth structure. The main components of the system are horizontal strut and center post. The statistical process control and safety monitoring system in this study are used effectively to analyze the process capability of the axially pressure of the horizontal strut and installation of center post during the construction. It shows that tools of the statistical process control (SPC) can maintain improving quality of internal bracing system very well.

誌謝 Ⅰ
摘要 Ⅲ
目錄 VII
表目錄 X
圖目錄 XII

第一章 緒論 1

1.1 研究動機 1
1.2 研究目的 2
1.3 研究內容 2
1.4 研究方法與流程 3
1.5 論文內容 5

第二章 文獻回顧 8

2.1 台北盆地地質狀況 8
2.2 深開挖工程行為及施工 10
2.3 預測壁體變形之研究方法 17
2.4 類神經網路的應用 20
2.5 TOPSIS多評準決策法之應用 24
2.6 統計製程理論的應用 26
2.7 營造業成本分析 27

第三章 深開挖土建工程成本分析 31

3.1 成本分析 31
3.2 資料分析 34
3.3 關聯性分析 39
3.4 離散分析檢討 40
3.5 成本模型建立 41

第四章 深開挖土建工程績效分析 68

4.1 績效分析方法 68
4.2 績效分析 71
4.3 模型與績效之關聯探討 76

第五章 深開挖工程安全監測管理 81

5.1 安全監測系統建立之目的 82
5.2 安全監測系統之設置 86
5.3 安全監測管理值模式 87
5.4 逆打深開挖觀測值模式 92

第六章 應用類神經網路分析及預測最大壁體水平變位 104

6.1 類神經網路理論 104
6.2 基本資料分析 113
6.3 網路建構 114
6.4 變位預測分析 116
6.5 網路預測結果之分析 118
6.6 網路預測案例驗證 119
6.7 權重分析 120

第七章 深開挖內支撐之控管 143

7.1 擋土支撐架構 143
7.2 統計製程管制理論 145
7.3 案例研究 149
7.4 製程管制成效 158


第八章 結論與建議 186

8.1 結論 186
8.2 建議 188

參考文獻 190
作者簡歷 201

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