| DC 欄位 |
值 |
語言 |
| DC.contributor | 營建管理研究所 | zh_TW |
| DC.creator | 黛安娜 | zh_TW |
| DC.creator | DIANA WAHYU HAYATI | en_US |
| dc.date.accessioned | 2015-8-4T07:39:07Z | |
| dc.date.available | 2015-8-4T07:39:07Z | |
| dc.date.issued | 2015 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=102325603 | |
| dc.contributor.department | 營建管理研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 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
| zh_TW |
| dc.description.abstract | 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. | en_US |
| DC.subject | 工作危險性分析 | zh_TW |
| DC.subject | 風險評估 | zh_TW |
| DC.subject | 貝葉斯網路 | zh_TW |
| DC.subject | 風險矩陣 | zh_TW |
| DC.subject | Job Hazard Analysis | en_US |
| DC.subject | Risk Assessment | en_US |
| DC.subject | Bayesian Belief Network | en_US |
| DC.subject | Risk Matrix | en_US |
| DC.title | 以貝式信念網路為基礎之工作危害分析與風險評估方法 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Bayesian Belief Network Approach for Analysing Job Hazard and Assessing Risk | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |