Accident Rate Estimation Modeling Based on Human Factors Using Fuzzy C-Means Clustering Algorithm Muhammad MAS

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Accident Rate Estimation Modeling Based on Human Factors Using Fuzzy C-Means Clustering Algorithm Muhammad MAS

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Title: Accident Rate Estimation Modeling Based on Human Factors Using Fuzzy C-Means Clustering Algorithm Muhammad MAS
Author: M.A.S. Mahmoud, Muhammad
Abstract: This is a reference paper that attempts to use a completely different approach to analyze accidents. A model was developed for data collected from an accident rate questionnaire filled-in by laborers working for a reputable construction company in Kuwait. This questionnaire was designed to include information about human factors, as well as other factors such as work type, managerial factors, training, physical factors and the historical accident rate for each labor during his period of employment in this particular construction company, as well as his experience during his career life time. The collected data was split into a training set for model construction and a test for model verification. The training information was classified into a number of groups or clusters, the centroids of these clusters were subsequently used to generate a set of rules to develop a fuzzy engine, which can then predict and forecast the rate of accidents. The test cases were used to verify and validate the developed model. Some outcome and correlated results have been driven by the mode and illustrated in this paper.
URI: http://hdl.handle.net/123456789/1858
Date: 2012-11-30


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