Analysis of Accident Data and Fatal Risk for Occupational Use of Extension Ladders

1994 ◽  
Vol 38 (14) ◽  
pp. 806-810 ◽  
Author(s):  
Christine T. Wood ◽  
Roger L. McCarthy ◽  
Jeya Padmanaban ◽  
Roman R. Beyer
Keyword(s):  
Construction Industry ◽  
Data Bases ◽  
Fatality Risk ◽  
Severity Of Injury ◽  
Accident Data

This paper presents the results of analyses of injury and fatality accidents associated with the occupational use of metal extension ladders. Data contained in seven different data bases differing in geographic representation and level of severity of injury were analyzed to identify the type of accidents that occur and their frequency. In addition, the risks of fatality and of electrocution fatality associated with occupational use of extension ladders were estimated and compared with the fatal risk for various occupations. The overall fatal risk for extension ladder use by workers is less than the overall fatality risk for the construction industry.

scholarly journals Key performance indicators to improve the competitive dimensions of the construction company

2020 ◽  
Vol 9 (5) ◽  
pp. e54953130
Author(s):  
Aparecida Massako Tomioka ◽  
José Manoel Souza das Neves
Keyword(s):  
Organizational Performance ◽  
Construction Industry ◽  
Performance Indicators ◽  
Research Method ◽  
Web Of Science ◽  
Qualitative Approach ◽  
Review Of The Literature ◽  
Science Data ◽  
Data Bases ◽  
Construction Company

The construction industry is a significant economic and productive sector of a country. Due to the importance of the sector, this study is justified not only for the academia, but also for the productive and business circles. Identifying competitive dimensions and comprehend the organizational performance through performance indicators, allows managers to make decisions through these tools, according to the model in which the organization operates, as close as possible to their reality. The present work aims to analyze the application of performance indicators through the competitive dimensions of the construction company. The used research method was a qualitative approach, being of an applied nature, classified according to the objectives of the research in descriptive and explanatory. The procedure used was the review of the literature through scientific articles in the Web of Science data bases, for the last ten years.

scholarly journals Analyzing the Risk of Safety Accidents: The Relative Risks of Migrant Workers in Construction Industry

2020 ◽  
Vol 12 (13) ◽  
pp. 5430
Author(s):  
Ji-Myong Kim ◽  
Kiyoung Son ◽  
Sang-Guk Yum ◽  
Sungjin Ahn
Keyword(s):  
Risk Factors ◽  
Construction Industry ◽  
Migrant Workers ◽  
Safety Management ◽  
Construction Workers ◽  
Risk Indicators ◽  
Accident Risk ◽  
Relative Risks ◽  
Accident Data ◽  
Safety Accident

This study analyzed the relative risks of migrant workers, and identified risk factors based on quantitative data for the systematic safety management of migrant workers. Many studies have found that migrant workers are more vulnerable to safety accidents than non-migrant workers. Nevertheless, there are few quantitative studies of migrant workers’ accident-risk in the construction industry, where safety accidents are most frequent. In addition, safety management for the identified accident risk factors has not been implemented systematically. To fill the gap, this study uses safety accident data from construction sites, from the +, for the methodical safety management of migrant workers. The t-test and multiple regression analysis methods are used to define the variance in non-migrant and migrant workers, and the risk indicators, respectively. The two analyses show that the results for migrant construction workers were 2.2% higher in safety accident severity than non-migrant workers, and significant factors are also different. This study’s results will provide critical guidance for the safety management of migrant construction workers.

A Survey on Industrial Accident Data Bases

1986 ◽  
pp. 130-136 ◽  
Author(s):  
P. Bockholts ◽  
I. Heidebrink ◽  
T. R. Moss ◽  
J. A. Butler ◽  
C. Fiorentini ◽  
...  
Keyword(s):  
Industrial Accident ◽  
Data Bases ◽  
Accident Data

Quirks of Mass Accident Data Bases

2005 ◽  
Vol 6 (4) ◽  
pp. 308-310 ◽  
Author(s):  
Murray Mackay
Keyword(s):  
Data Bases ◽  
Accident Data

scholarly journals A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry

2020 ◽  
Vol 10 (21) ◽  
pp. 7949
Author(s):  
Jae Yun Lee ◽  
Young Geun Yoon ◽  
Tae Keun Oh ◽  
Seunghee Park ◽  
Sang Il Ryu
Keyword(s):  
Machine Learning ◽  
Construction Industry ◽  
Flow Diagram ◽  
Occupational Accidents ◽  
Support Vector ◽  
Occupational Accident ◽  
Accident Prediction ◽  
Processing Procedure ◽  
Accident Data ◽  
Prediction Modelling

In the construction industry, it is difficult to predict occupational accidents because various accident characteristics arise simultaneously and organically in different types of work. Furthermore, even when analyzing occupational accident data, it is difficult to deduce meaningful results because the data recorded by the incident investigator are qualitative and include a wide variety of data types and categories. Recently, numerous studies have used machine learning to analyze the correlations in such complex construction accident data; however, heretofore the focus has been on predicting severity with various variables, and several limitations remain when deriving the correlations between features from various variables. Thus, this paper proposes a data processing procedure that can efficiently manipulate accident data using optimal machine learning techniques and derive and systematize meaningful variables to rationally approach such complex problems. In particular, among the various variables, the most influential variables are derived through methods such as clustering, chi-square, Cramer’s V, and predictor importance; then, the analysis is simplified by optimally grouping the variables. For accident data with optimal variables and elements, a predictive model is constructed between variables, using a support vector machine and decision-tree-based ensemble; then, the correlation between the dependent and independent variables is analyzed through an alluvial flow diagram for several cases. Therefore, a new processing procedure has been introduced in data preprocessing and accident prediction modelling to overcome difficulties from complex and diverse construction occupational accident data, and effective accident prevention is possible by deriving correlations of construction accidents using this process.

Addressing Health And Safety Concerns For Tradeswomen In The Construction Industry

2017 ◽  
Author(s):  
Hannah M. Curtis ◽  
Hendrika Meischke ◽  
Nancy Simcox ◽  
Sarah Laslett ◽  
Noah Seixas
Keyword(s):  
Construction Industry ◽  
Health And Safety ◽  
Safety Concerns
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