Careful and thorough incident investigations and pre-job safety analyses completed by knowledgeable and competent individuals can significantly reduce workplace incidents. Working parties must act together to make the...Careful and thorough incident investigations and pre-job safety analyses completed by knowledgeable and competent individuals can significantly reduce workplace incidents. Working parties must act together to make these safety tools effective. To get the staff units to work together in a co-ordinated manner, they must be shown the value of their work in preventing accidents. Examples of actual accidents investigated during the author's 18 years as a mine inspector in Saskatchewan are discussed within the context of pre-job safety analyses. The causes of the accidents are explored with close reference to how pre-job safety analyses could have prevented their occurrence.展开更多
The way to investigate the cause of an industrial accident is considered. It's important for the staff who investigate an accident to be the person who became independent from a production site. Moreover a special...The way to investigate the cause of an industrial accident is considered. It's important for the staff who investigate an accident to be the person who became independent from a production site. Moreover a special right has to be granted to the staff in the organization. The reason is because it's necessary that they have a different viewpoint from field overseer. The staff's viewpoint is a one related to the importance of the site preservation,necessity of an information feedback and the way to fill out an accident report. It was modeled in the last chapter about a relation of the various factors which have an influence on accident investigation.展开更多
Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that af...Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. Theassociation rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.展开更多
文摘Careful and thorough incident investigations and pre-job safety analyses completed by knowledgeable and competent individuals can significantly reduce workplace incidents. Working parties must act together to make these safety tools effective. To get the staff units to work together in a co-ordinated manner, they must be shown the value of their work in preventing accidents. Examples of actual accidents investigated during the author's 18 years as a mine inspector in Saskatchewan are discussed within the context of pre-job safety analyses. The causes of the accidents are explored with close reference to how pre-job safety analyses could have prevented their occurrence.
文摘The way to investigate the cause of an industrial accident is considered. It's important for the staff who investigate an accident to be the person who became independent from a production site. Moreover a special right has to be granted to the staff in the organization. The reason is because it's necessary that they have a different viewpoint from field overseer. The staff's viewpoint is a one related to the importance of the site preservation,necessity of an information feedback and the way to fill out an accident report. It was modeled in the last chapter about a relation of the various factors which have an influence on accident investigation.
文摘Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. Theassociation rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.
