Anas Al-Refaie, A. Alashwal, Zulkiflee Abdul-Samad, H. Salleh, A. Elshafie
{"title":"Evaluation of weather-productivity models of construction labour for tropics","authors":"Anas Al-Refaie, A. Alashwal, Zulkiflee Abdul-Samad, H. Salleh, A. Elshafie","doi":"10.1108/bepam-03-2022-0040","DOIUrl":null,"url":null,"abstract":"PurposeWeather is one of the main factors affecting labour productivity. Existing weather-productivity models focussed on hot and cold climates paying less attention to the tropics. Many tropical countries are expected to be the most areas affected by accelerated climate change and global warming, which may have a severe impact on labour health and productivity. The purpose of this paper is to assess whether the existing models can be used to predict labour productivity based on weather conditions in the tropics.Design/methodology/approachFive models are identified from the literature for evaluation. Using real labour productivity data of a high-rise building project in Malaysia, the actual productivity rate was compared with predicted productivity rates generated using the five models. The predicted productivity rates were generated using weather variables collected from an adjusting weather station to the project.FindingsCompared with other models evaluated in this paper, the United States Army Corps of Engineers (USACE) was found to be the best model to predict productivity based on the case study data. However, the result shows only a 57% accuracy level of the USACE model indicating the need to develop a new model for the tropics for more accurate prediction.Originality/valueThe result of this study is perhaps the first to apply meteorological variables to predict productivity rates and validate them using actual productivity data in the tropics. This study is the first step to developing a more accurate productivity model, which will be useful for project planning and more accurate productivity rate estimation.","PeriodicalId":46426,"journal":{"name":"Built Environment Project and Asset Management","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Built Environment Project and Asset Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/bepam-03-2022-0040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
PurposeWeather is one of the main factors affecting labour productivity. Existing weather-productivity models focussed on hot and cold climates paying less attention to the tropics. Many tropical countries are expected to be the most areas affected by accelerated climate change and global warming, which may have a severe impact on labour health and productivity. The purpose of this paper is to assess whether the existing models can be used to predict labour productivity based on weather conditions in the tropics.Design/methodology/approachFive models are identified from the literature for evaluation. Using real labour productivity data of a high-rise building project in Malaysia, the actual productivity rate was compared with predicted productivity rates generated using the five models. The predicted productivity rates were generated using weather variables collected from an adjusting weather station to the project.FindingsCompared with other models evaluated in this paper, the United States Army Corps of Engineers (USACE) was found to be the best model to predict productivity based on the case study data. However, the result shows only a 57% accuracy level of the USACE model indicating the need to develop a new model for the tropics for more accurate prediction.Originality/valueThe result of this study is perhaps the first to apply meteorological variables to predict productivity rates and validate them using actual productivity data in the tropics. This study is the first step to developing a more accurate productivity model, which will be useful for project planning and more accurate productivity rate estimation.