臺灣博碩士論文加值系統

營建業職業災害的比率向來居各產業之冠，本文自台灣地區勞委會北區勞動檢查所1999-2004營建業職業災害的死亡案例中，分別擷取其中建築與土木類個案，採用關聯規則採礦方法以確認營建業職業災害的特性。除一般用以分析的個人與管理等因素外，亦納入與氣候相關的因子。結果顯示雨量對營建業職業災害的發生確有影響，而且會因工程種類而不同，換言之，檢查策略應該依據工程種類與環境變化而制定。本文研究成果提供了一個制定有效的檢查策略與災害防止計畫的方向。

There is a higher rate of occupational injury in the construction industry than most other industries on average. However, steps can be taken to reduce worker risk through effective injury prevention strategies. In this article, association rule mining is employed in identifying the characteristics of occupational injuries in the construction industry. Accident reports during the period 1999-2004 are extracted from case reports of the Northern Region Inspection Office of the Council of Labor Affairs of Taiwan. In addition to general factors, several factors related to weather conditions are included in this article. The results show that there are some patterns of occupational injuries in the construction industry. The effect of rain on the occurrence of fatalities is of great significance. Proposed inspection plans should be in accordance with the type of construction and environmental evaluation. The findings identified in this article provide a direction for more effective inspection strategies and injury prevention programs.

摘 要 I
ABSTRACT II
誌 謝 III
TABLE OF CONTENTS IV
LIST OF TABLES VI
1. INTRODUCTION 1
2. FACTORS CONTRIBUTING TO OCCUPATIONAL INJURIES 5
3. METHOD 11
3.1. MATERIALS 11
3.2. ASSOCIATION RULE 14
3.3. MEASUREMENT OF ASSOCIATION RULE 16
4. RESULTS 18
4.1 ANALYSIS OF ASSOCIATION RULES 18
4.1.1 Civil engineering 19
4.1.2 Building construction 23
5. DISCUSSION 28
6. CONCLUSIONS 34
REFERENCES 36

Agrawal, R., Imielinski, T., and Swami, A., 1993. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, 207-216.
Arboleda, C. A. and Abraham, D. M., 2004. Fatalities in trenching operations-analysis using models of accident causation. Journal of Construction Engineering and Management 130, 273-280.
Baralis, E., and Psaila, G., 1997. Designing templates for mining association rules. Journal of Intelligent Information Systems 9, 7-32.
Brin, S., Motwani, R., and Silverstein, C., 1997. Beyond market baskets: generalizing association rules to correlations. Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, Tucson, AZ, USA, 265-276.
Chae, Y. M., Ho, S. H., Cho, K. W., Lee, D. H., and Ji, S. H., 2001. Data mining approach to policy analysis in a health insurance domain. International Journal of Medical Informatics 62, 103-111.
Chi, C. F., Chang, T. C., and Hung, K. H., 2004. Significant industry-source of injuries-accident type for occupational fatalities in Taiwan. International Journal of Industrial Ergonomics 34, 77-91.
Chi, C. F., Chang, T. C., and Ting, H. I., 2005. Accident patterns and prevention measures for fatal occupational falls in the construction industry. Applied Ergonomics 36, 391-400.
Harper, R. S., and Koehn, E., 1998. Managing industrial construction safety in Southeast Texas. Journal of Construction Engineering and Management 124, 452-457.
Huang, X., and Hinze, J., 2003. Analysis of construction worker fall accidents. Journal of Construction Engineering and Management 129, 262-271.
Hsieh, N. C., 2004. An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications 27, 623-633.
Hinze, J., Pedersen, C., and Fredley, J., 1998. Identifying root causes of construction injuries. Journal of Construction Engineering and Management 124, 67-71.
Hinze, J., Huang, X., and Terry, L., 2005. The nature of struck-by accidents. Journal of Construction Engineering and Management 2, 262-268.
Jeong, B. Y., 1998. Occupational deaths and injuries in the construction industry. Applied Ergonomics 29, 355-360.
Kartam, N. A., Flood, I., and Koushki, P., 2000. Construction safety in Kuwait: issues, procedures, problems, and recommendations. Safety Science 36, 163-184.
Kines, P., 2002. Construction workers’ falls through roofs: fatal versus serious injuries. Journal of Safety Research 33, 195-208.
Larsson, T. J., and Field, B., 2002. The distribution of occupational injuries risks in the Victorian construction industry. Safety Science 40, 439-456.
McKnight, T. L. and Hess, D., 2000. Physical Geography: A Landscape Appreciation, Prentice Hall, Upper Saddle River, NJ.
McVittie, D., Banikin, H., and Brocklebank, W., 1997. The effects of firm size on injury frequency in construction. Safety Science 27, 19-23.
Oleske, D. M., Brewer, R. D., Doan, P., and Hahn, J., 1989. An epidemiologic evaluation of the injury experience of a cohort of automotive parts workers: a model for surveillance in small industries. Journal of Occupational Accidents 10, 239-253.
Rabi, A. Z., Jamous, L. W., AbuDhaise, B. A., and Alwash, R. H., 1998. Fatal occupational injuries in Jordan during the period 1980 through 1993. Safety Science 28, 177-187.
Sawacha, E., Naoum, S., and Fong, D., 1999. Factors affecting safety performance on construction sites. International Journal of Project Management 17, 309-315.
Toole, T. M., 2002. Construction site safety roles. Journal of Construction Engineering and Management 128, 203-210.
Tsay, Y. J., and Chiang, J. Y., 2005. CBAR: an efficient method for mining association rules. Knowledge-Based Systems 18, 99-105.
Williamson, A. M., Feyer, A. -M., and Cairns, D. R., 1996. Industrial differences in accident causation. Safety Science 24, 1-12.
Zhang, C., and Zhang, S., 2002. Association rule mining: models and algorithms, Springer, New York.