以貝式信念網路為基礎之工作危害分析與風險評估方法

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研究生：	黛安娜 DIANA WAHYU HAYATI
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
指導教授：	王翰翔 Han-Hsiang Wang
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：	2015
畢業學年度：	103
語文別：	英文
論文頁數：	113
中文關鍵詞：	工作危險性分析、風險評估、貝葉斯網路、風險矩陣
外文關鍵詞：	Job Hazard Analysis, Risk Assessment, Bayesian Belief Network, Risk Matrix
相關次數：	點閱：14 下載：0
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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

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.

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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