MACHINE ACCIDENTS AND PROJECT DELIVERY IN KWAZULU-NATAL CONSTRUCTION INDUSTRY

A. Olatunji, Iruka Chijindu
{"title":"MACHINE ACCIDENTS AND PROJECT DELIVERY IN KWAZULU-NATAL CONSTRUCTION INDUSTRY","authors":"A. Olatunji, Iruka Chijindu","doi":"10.53974/unza.jonas.5.1.710","DOIUrl":null,"url":null,"abstract":"This article identified machine accidents, the number of days lost, cost impact and related accidents in the Construction Industry in the KwaZulu-Natal province of South Africa. Accidents in the construction industry cause severe challenges to the business. The critical parameters for assessing project deliveries are health and safety, cost, time and project quality. The events of accidents on construction premises sabotages these critical parameters of delivery. The main focus of this article was to determine the significance of lost days and the number of accidents on the cost per accident, using the accident data of KwaZulu-Natal from the year 2000 to 2020. Statistical tests were conducted to determine the significance of the lost days and the number of accidents (independent variables) on the cost per accident (dependent variable). Five statistical tests were used in the analysis of the data and tests were grouped into three classes; regression, correlation and paired sample tests. Regression is subdivided into ANOVA, correlation and model summary test. All five tests display the significance of testing variables. The results revealed that there was a significant relationship between the dependent and the independent variables. There was also a positive relationship between lost days and the average cost per accident. At the same time, there was a negative relationship between the number of accidents and the average cost per accident. The positive B value of lost days mean that it directly influenced the average cost per accident. This means that for every increase in days lost to accidents on the site, the costs increased and vice versa. The negative B of the number of days indicated that accidents did not directly influence the average cost per accident. Further, the machine accidents that most caused fatalities were: motorised equipment, truck, lorries, dumpers, building structures, roof work, scaffoldings and staging and wall projections. It is recommended that workers pay more attention to the sources of accidents while working on site.","PeriodicalId":16473,"journal":{"name":"Journal of natural sciences, life and applied sciences","volume":"40 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of natural sciences, life and applied sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53974/unza.jonas.5.1.710","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This article identified machine accidents, the number of days lost, cost impact and related accidents in the Construction Industry in the KwaZulu-Natal province of South Africa. Accidents in the construction industry cause severe challenges to the business. The critical parameters for assessing project deliveries are health and safety, cost, time and project quality. The events of accidents on construction premises sabotages these critical parameters of delivery. The main focus of this article was to determine the significance of lost days and the number of accidents on the cost per accident, using the accident data of KwaZulu-Natal from the year 2000 to 2020. Statistical tests were conducted to determine the significance of the lost days and the number of accidents (independent variables) on the cost per accident (dependent variable). Five statistical tests were used in the analysis of the data and tests were grouped into three classes; regression, correlation and paired sample tests. Regression is subdivided into ANOVA, correlation and model summary test. All five tests display the significance of testing variables. The results revealed that there was a significant relationship between the dependent and the independent variables. There was also a positive relationship between lost days and the average cost per accident. At the same time, there was a negative relationship between the number of accidents and the average cost per accident. The positive B value of lost days mean that it directly influenced the average cost per accident. This means that for every increase in days lost to accidents on the site, the costs increased and vice versa. The negative B of the number of days indicated that accidents did not directly influence the average cost per accident. Further, the machine accidents that most caused fatalities were: motorised equipment, truck, lorries, dumpers, building structures, roof work, scaffoldings and staging and wall projections. It is recommended that workers pay more attention to the sources of accidents while working on site.
夸祖鲁-纳塔尔省建筑业的机械事故和项目交付
本文确定了南非夸祖鲁-纳塔尔省建筑行业的机器事故,损失的天数,成本影响和相关事故。建筑行业的事故给企业带来严峻的挑战。评估项目交付的关键参数是健康和安全、成本、时间和项目质量。施工场所的事故事件破坏了这些关键的交付参数。本文的主要重点是确定损失天数和事故数量对每次事故成本的重要性,使用夸祖鲁-纳塔尔省2000年至2020年的事故数据。进行统计检验以确定损失天数和事故数量(自变量)对每次事故成本(因变量)的显著性。采用5个统计检验对数据进行分析,并将检验分为三类;回归、相关和配对样本检验。回归分为方差分析、相关分析和模型总结检验。所有五个测试都显示了测试变量的显著性。结果表明,因变量和自变量之间存在显著的相关关系。损失天数与每次事故的平均成本之间也存在正相关关系。同时,事故数量与每次事故的平均成本之间存在负相关关系。损失天数的正B值意味着它直接影响每起事故的平均成本。这意味着,现场事故损失的天数每增加,成本就会增加,反之亦然。天数的负B表示事故不直接影响每起事故的平均成本。此外,造成死亡人数最多的机械事故是:机动设备、卡车、卡车、自卸车、建筑结构、屋顶工作、脚手架、舞台和墙壁投影。建议工人在现场工作时多注意事故的来源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信