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研究生： 普妮塔
Dian Pramita Sarie
論文名稱： 以巨量引響因子預測基樁工程生產力減損之程度-以印尼為例
PREDICTING PILE CONSTRUCTION PRODUCTIVITY LOSS USING MACRO IMPACT FACTORS IN INDONESIA
指導教授： 陳介亮
Jieh - Haur Chen
口試委員:
學位類別： 碩士
Master
系所名稱： 工學院 - 土木系營建管理碩士班
Master's Program in Construction Management, Department of Civil Engineering
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 86
中文關鍵詞： 基樁工程 、巨量因子 、支援向量回歸機 、生產力耗損
外文關鍵詞： pile construction, macro factor, SVR, productivity loss
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    • 近年來，印尼面臨在基樁工程生產力減損之問題，在增加基樁工程生產力之前，辨識出其影響因子及其影響程度為首要任務。本研究目標為辨識影響基樁工程之巨量因子，藉由支援向量回歸機精準預測其生產力之減損，並從相似之案例得知其可能耗損之數量。 文獻回顧對於支援向量回歸機指出5項巨量因子(勞工、管理、環境、材料及設備)及8種投入項目(土壤情況、基樁種類、基樁材料、專案大小、專案所在地、基樁深度、基樁數量及設備數量)，並由以上所提出之因子及項目找出在印尼爪哇島5個主要地區共110項基樁工程專案。支援向量回歸機經10次交叉驗證後得到87.2% 的精確度，並由以往相似案例可得知生產力的減損大約占總生產力之18.55%。調查結果將使從事此作業者更加注意減損問題以增加整體生產力。

    Pile construction productivity loss in Indonesia had been occurred for years. Before improving pile construction productivity, impact factors and how much potential loss are urgent to identified. The research objectives are to identify the macro factors that influence pile construction, to develop a SVR model that precisely predicts productivity loss, and to provide potential loss quantities using the most similar historical case(s). Literature review identifies 5 macro factors (labor, management, environment, material, and equipment) and 8 inputs (soil condition, pile type, pile material, project size, project location, pile depth, pile quantity, and equipment quantity) for Support Vector Regression (SVR) model, and then leads the study to collect 110 pile construction projects among 5 major areas in Java island of Indonesia. The SVR evaluated using 10-way cross validation yields an accuracy rate at 87.2%. The most likely productivity loss obtained based on the most similar historical cases is approximately 18.55% of total productivity. The findings would push the practitioners to pay attention to the loss in order to improve the overall productivity.

    4. TABLE OF CONTENTS ABSTRACT i 摘要 ii ACKNOWLEDGEMENT iii TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii CHAPTER I : INTRODUCTION 1 1.1 Research Background 1 1.2 Research Objectives 2 1.3 Research Scope and Limitations 2 1.4 Methodology 2 1.5 Research Flowchart 3 1.6 Thesis Organization 3 CHAPTER II : LITERATURE REVIEW 5 2.1 Productivity Concept 5 2.2 Pile Construction Productivity Measurement and Performance 6 2.3 Pile Construction Productivity Measurement Methods 7 2.4 Multiple Regression Model Coeficcient Pile Construction 8 2.4.1 Average Productivity in Pile Construction Productivity 8 2.4.2 Impact Factors of Pile Construction Productivity Measurement on Performance 9 2.5 Piling Process Productivity Factors 10 2.6 Pile Construction Productivity Prediction 14 2.7 Support Vector Machine 15 2.7.1 Overview of Support Vector Machine 15 2.7.2 Support Vector Machine Classification 17 2.7.3 Support Vector Machine Regression 18 2.8 Time series 19 CHAPTER III: DATA COLLECTION AND ANALYSIS 21 3.1 Research Framework 21 3.2 Data Analysis 22 3.2.1 Overview Data 22 3.2.2 Project Characteristics 23 3.3 Factor Analysis 27 3.4 Macro Impact Factors For Pile Construction Productivity 31 CHAPTER IV: MODEL DEVELOPMENT 35 4.1 Arranging the Datasets 35 4.2 Model Implementation and Evaluation 41 4.2.1 Step Analysis with R program 41 4.3 Interpretation Prediction Result and Discussion 45 CHAPTER V: CONCLUSION 54 5.1 Conclusion 54 5.2 Research Recommendation 55 REFERENCE 56 APPENDIX : PARAMETER RESULT 59 APPENDIX : MODEL RESULT 67

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