博碩士論文 102325603 詳細資訊




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姓名 黛安娜(DIANA WAHYU HAYATI)  查詢紙本館藏   畢業系所 營建管理研究所
論文名稱 以貝式信念網路為基礎之工作危害分析與風險評估方法
(Bayesian Belief Network Approach for Analysing Job Hazard and Assessing Risk)
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摘要(中) 在這世界上,建設是多數國家最危險的產業。不可否認的是,建設在多數國家的確貢獻了非常大量的意外。這些意外的發生避免不了與工程特性有關。由於受限於時間的獨特性,工程本身也擁有複雜的特性,其中就有上百或千的活動包含在工程內。但是,這些活動與工程的危險性與風險是成正比的。因此,在前置計畫的危險分析及風險評估是克服意外發生不可或缺的階段。有許多的研究探討了工作的危險性甚至是風險,但是有關工作危險性與風險評估的分析,研究多數都分別以質化的方式執行。除此之外,有些研究實證了風險的量化評估但是排除了工作危險性的分析。因此,工程單位並沒有完全了解工程建設意外的風險。
本研究將呈現工作危險性分析的基礎模型,此模型結合了工作危險性分析與量化風險評估,目的為協助工程單位,尤其是承包商,擁有更清楚及了解建設工程中的意外風險。此外,相較於依賴工程師安全性的經驗或直覺的傳統風險評估方法,本研究將介紹以貝式信念網路(Bayesian Belief Network,BBN)方法於工程單位,尤其是承包商,做為更客觀的風險評估方法。本研究將BBN方法與傳統方法做比較,主要為了驗證BBN為此研究的方法。結論發現BBN的方法相較於質化的方式,擁有更小的模糊水平,例如:風險矩陣。這方法會是可以於廣泛的工程做彈性的運用。若有必要,承包商可以依據工程的轉況增加潛在的危險性及依據領域的狀況增加變數關係圖。此外,在擁有適當及完整的資料,BBN方法可以是風險主觀與客觀之間的中介
摘要(英) Construction industry is one of the most dangerous industries in most countries in the world. It is indisputable to acknowledge that it is the source of a very large number of accidents in almost all the countries. The large number of the accidents cannot be separated from the characteristics of the projects. Each project has its particularities which make it complex. It may include over hundreds or even thousands activities. Those activities are directly proportional to the hazards and risks in the projects. Therefore, analyzing hazards and assessing risks in the preplanning stage are necessary to overcome the accidents that might occur. A lot of previous studies discuss about job hazards and their risks mostly qualitatively. Although the Job Hazard Analysis contains risk assessment in the qualitative term but it was not enough because qualitative is done on a much more individualized basis. On the other hand, several studies have addressed the quantitative risk assessment but ruled out job hazard analysis. As a result, the parties involved may not entirely understand the risks associated with the construction of the project. Therefore, the objective of this study is to present a job hazard analysis-based model that combines hazard analysis and quantitative risk assessment in a single entity. This intends to assist individuals, especially contractors, in order to have clearer understanding about the risks of accidents in projects construction. Moreover, introducing Bayesian Belief Networks (BBN) to the parties, especially contractors as an approach for assessing the risk is more objective than traditional approach which only relies on experience or intuition of the safety engineer. Comparing the BBN approach with traditional approach is addressed in order to validate the BBN as the approach in this study. The result shows that the BBN approach has less ambiguity than any qualitative approach, such as the risk matrix. It is a flexible approach that can be applied to large projects. If necessary, the contractors can add potential hazards that may be suitable to the conditions of the project and add variable relationship diagrams based on the conditions of the field. Besides, BBN approach can be an intermediary between subjective and objective views of risks when it is provided by appropriate and complete data.
關鍵字(中) ★ 工作危險性分析
★ 風險評估
★ 貝葉斯網路
★ 風險矩陣
關鍵字(英) ★ Job Hazard Analysis
★ Risk Assessment
★ Bayesian Belief Network
★ Risk Matrix
論文目次 ABSTRACT i
摘要 ii
ACKNOWLEDGEMENT iii
TABLE OF CONTENT iv
LIST OF FIGURES vi
LIST OF TABLES vii
CHAPTER I INTRODUCTION 1
1.1 BACKGROUND 1
1.2 PROBLEM STATEMENT 3
1.3 OBJECTIVES 3
1.4 SCOPE AND LIMITATION OF THE RESEARCH 3
1.5 RESEARCH FLOWCART 4
1.6 THESIS ORGANIZATION 5
CHAPTER II LITERATURE REVIEW 6
2.1. JOB HAZARD ANALYSIS 6
2.1.1. Overview of Job Hazard Analysis 6
2.1.2. Steps in Conducting a JHA 8
2.1.3. JHA Document 11
2.1.4. Previous Study of Job Hazard Analysis 12
2.2. RISK ASSESSMENT 16
2.2.1. Overview of Risk Assessment 16
2.2.2. Risk Assessment Method 17
2.2.3. Previous Study of Risk Assessment 22
CHAPTER III THE PROPOSED BAYESIAN BELIEF NETWORK-BASED APPROACH 27
3.1 JOB HAZARD ANALYSIS 28
3.2 RISK ASSESSMENT 31
3.2.1 Introduction Bayesian Belief Network as Risk Assessment Approach 31
3.2.2 Build the Model of Occurrence 33
3.2.3 Build the Model of Severity 37
3.3 Comparison and Validation 38
CHAPTER IV CASE STUDIES AND DISCUSSIONS 39
4. 1 STUDY CASES OVERVIEW 39
4.2 CASE STUDY 1 39
4.2.1 Occurrence of Case Study 1 39
4.2.1.1 Determine the Variable of Occurrence 39
4.2.1.2 Relationship among Variables in Each Hazard 40
4.2.1.3 Calculating Class Interval 42
4.2.1.4 Conditional Probability Table 45
4.2.1.5 Inference Process for Occurrence 47
4.2.2 Severity of Case Study 1 49
4.2.2.1 Determine the Variables of Severity 49
4.2.2.2 Relationship among Variables in Each Hazard 49
4.2.2.3 Calculating Class Interval for Severity 49
4.2.2.4 Conditional Probability Table for Severity 52
4.2.2.5 Inference Process for Severity 54
4.2.3 Comparison and Validation 56
4.2.4 Discussion 71
4.3 CASE STUDY 2 72
4.3.1 Occurrence of Case Study 2 72
4.3.1.1 Determine the Variable of Occurrence 72
4.3.1.2 Relationship among Variables in Each Hazard 72
4.3.1.3 Calculating Class Interval 74
4.3.1.4 Conditional Probability Table 76
4.3.2 Severity of Case Study 2 78
4.3.2.1 Determine the Variables of Severity 78
4.3.2.2 Relationship among Variables in Each Hazard 78
4.3.2.3 Calculating Class Interval for Severity 78
4.3.2.4 Conditional Probability Table for Severity 80
4.3.2.5 Inference Process for Severity 81
4.3.3 Comparison and Validation 82
4.3.4 Discussion 96
CHAPTER V CONCLUSION 98
5.1 CONCLUSION 98
REFERENCES 100
APPENDIX : QUESTIONNAIRE 104
LIST OF FIGURES

Figure 1. 1 Research Flowchart 4

Figure 2. 1 Risk Analysis Flowchart 8
Figure 2. 2 Kinds of JHA Document 11
Figure 2. 3 Linguistic variable HOT 18
Figure 2. 4 Risk Matrix 20
Figure 2. 5 FMEA Form 22

Figure 3. 1 Research Framework 27

Figure 4. 1 Relationship among Variables for fall from Elevation (Case Study 1) 40
Figure 4. 2 Relationship among Variables for Collisions (Case Study 1) 41
Figure 4. 3 Relationship among Variables Burning by High or Low Temperature Materials 41
Figure 4. 4 Analysis of Conditional Probability Table 45
Figure 4. 5 The Probability of Occurrence or Fire and Explosion before Inference Process 46
Figure 4. 6 Inference Process of Fire and Explosion (Case Study 1) 48
Figure 4. 7 Severity Variable’s Relationship (Case Study 1) 49
Figure 4. 8 Analysis of Conditional Probability Table of fall from Elevation Severity 52
Figure 4. 9 The Probability of Severity of Falls from Elevation before Inference Process 53
Figure 4. 10 Inference Process of Falls from Elevation using Hugin Lite 8.1(Case Study 1) 55
Figure 4. 11 Relationship among Variables of Occurrence (Case Study 2) 73
Figure 4. 12 Inference Process of Collisions (Case Study 2) 77
Figure 4. 13 Severity Variable’s Relationship (Case Study 2) 78
Figure 4. 14 Inference Process of Collisions (Case Study 2) 81


LIST OF TABLES

Table 2. 1 Previous Study of Job Hazard Analysis 12
Table 2. 2 Previous study of risk assessment 22

Table 3. 1 Potential Hazard 30
Table 3. 2 State of Variable 35
Table 3. 3 Conditional Probability Table 36
Table 3. 4 State of Severity Variable 37

Table 4. 1 Number Worker Injured in 2003-2013 42
Table 4. 2 Total Number of Injured Workers in 2003-2013 42
Table 4. 3 Percentage of Injured Workers by Each Variable (Case Study 1) 43
Table 4. 4 Total Number Worker Injured in Each Variable (Case Study 1) 44
Table 4. 5 State Classification in Each Variable (Case Study 1) 44
Table 4. 6 The Probability of Occurrence before Inference Process (Case Study 1) 47
Table 4. 7 The Probability of Occurrence after Inference Process (Case Study 1) 48
Table 4. 8 Accident Costs (in thousands, NT dollars) 50
Table 4. 9 Lost Time Injury in Percent and the Average of Work Days Lost (Case Study 1) 50
Table 4. 10 Lost Time Injury in Percent and the Average of Work Days Lost (Case Study 1) 51
Table 4. 11 Severity State Classification in Each Variable (Case Study 1) 51
Table 4. 12 The Probability of Severity before Inference Process (Case Study 1) 54
Table 4. 13 The Probability of Severity after Inference Process (Case Study 1) 55
Table 4. 14 The Difference of Occurrence Result (Case Study 1) 57
Table 4. 15 The Difference of Severity Results (Case Study 1) 61
Table 4. 16 The 5 Smallest Difference of Risk Based on the Job (Case Study 1) 65
Table 4. 17 Risk in Traditional Approach (Case Study 1) 65
Table 4. 18 Risk in Proposed Approach (Case Study 1) 65
Table 4. 19 Risk in Percent (Case Study 1) 67
Table 4. 20 Risk Level in Traditional Approach (Case Study 1) 68
Table 4. 21 Risk Level in Proposed Approach (Case Study 1) 69
Table 4. 22 Risk Level of Two Approaches (Case Study 1) 70
Table 4. 23 Range of Difference (Case Study 1) 71
Table 4. 24 Percentage of Injured Workers by Each Variable (Case Study 2) 74
Table 4. 25 Total Number Worker Injured in Each Variable (Case Study 2) 75
Table 4. 26 State Classification in Each Variable (Case Study 2) 75
Table 4. 27 The Probability of Occurrence before Inference Process (Case Study 2) 76
Table 4. 28 The Probability of Occurrence after Inference Process (Case Study 2) 77
Table 4. 29 Lost Time Injury in Percent and the Average of Work Days Lost (Case Study 2) 79
Table 4. 30 Lost Time Injury in Percent and the Average of Work Days Lost (Case Study 2) 79
Table 4. 31 Severity State Classification in Each Variable (Case Study 2) 80
Table 4. 32 The Probability of Severity before Inference Process (Case Study 2) 80
Table 4. 33 The Probability of Occurrence after Inference Process (Case Study 2) 82
Table 4. 34 The Difference of Occurrence Result (Case Study 2) 83
Table 4. 35 The Difference of Severity Results (Case Study 2) 87
Table 4. 36 The 5 Smallest Difference of Risk Based on the Job (Case Study 2) 90
Table 4. 37 Risk in Traditional Approach (Case Study 2) 90
Table 4. 38 Risk in Proposed Approach (Case Study 2) 91
Table 4. 39 Risk in Percent (Case Study 2) 92
Table 4. 40 Risk Level in Traditional Approach 93
Table 4. 41 Risk level in Proposed Approach (Case Study 2) 94
Table 4. 42 Risk Level of Two Approaches (Case Study 2) 95
Table 4. 43 Range of Differences (Case Study 2) 96
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指導教授 王翰翔(Han-Hsiang Wang) 審核日期 2015-8-4
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