M. H. Momade, S. Durdyev, Saurav Dixit, Shamsuddin Shahid, Abubakar Kori Alkali
{"title":"Modeling labor costs using artificial intelligence tools","authors":"M. H. Momade, S. Durdyev, Saurav Dixit, Shamsuddin Shahid, Abubakar Kori Alkali","doi":"10.1108/ijbpa-05-2022-0084","DOIUrl":null,"url":null,"abstract":"PurposeConstruction projects in Malaysia are often delayed and over budget due to heavy reliance on labor. Linear regression (LR) models have been used in most labor cost (LC) studies, which are less accurate than machine learning (ML) tools. Construction management applications have increasingly used ML tools in recent years and have greatly impacted forecasting. The research aims to identify the most influential LC factors using statistical approaches, collect data and forecast LC models for improved forecasts of LC.Design/methodology/approachA thorough literature review was completed to identify LC factors. Experienced project managers were administered to rank the factors based on importance and relevance. Then, data were collected for the six highest ranked factors, and five ML models were created. Finally, five categorical indices were used to analyze and measure the effectiveness of models in determining the performance category.FindingsWorker age, construction skills, worker origin, worker training/education, type of work and worker experience were identified as the most influencing factors on LC. SVM provided the best in comparison to other models.Originality/valueThe findings support data-driven regulatory and practice improvements aimed at improving labor issues in Malaysia, with the possibility for replication in other countries facing comparable problems.","PeriodicalId":44905,"journal":{"name":"International Journal of Building Pathology and Adaptation","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Building Pathology and Adaptation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijbpa-05-2022-0084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 1
Abstract
PurposeConstruction projects in Malaysia are often delayed and over budget due to heavy reliance on labor. Linear regression (LR) models have been used in most labor cost (LC) studies, which are less accurate than machine learning (ML) tools. Construction management applications have increasingly used ML tools in recent years and have greatly impacted forecasting. The research aims to identify the most influential LC factors using statistical approaches, collect data and forecast LC models for improved forecasts of LC.Design/methodology/approachA thorough literature review was completed to identify LC factors. Experienced project managers were administered to rank the factors based on importance and relevance. Then, data were collected for the six highest ranked factors, and five ML models were created. Finally, five categorical indices were used to analyze and measure the effectiveness of models in determining the performance category.FindingsWorker age, construction skills, worker origin, worker training/education, type of work and worker experience were identified as the most influencing factors on LC. SVM provided the best in comparison to other models.Originality/valueThe findings support data-driven regulatory and practice improvements aimed at improving labor issues in Malaysia, with the possibility for replication in other countries facing comparable problems.
期刊介绍:
The International Journal of Building Pathology and Adaptation publishes findings on contemporary and original research towards sustaining, maintaining and managing existing buildings. The journal provides an interdisciplinary approach to the study of buildings, their performance and adaptation in order to develop appropriate technical and management solutions. This requires an holistic understanding of the complex interactions between the materials, components, occupants, design and environment, demanding the application and development of methodologies for diagnosis, prognosis and treatment in this multidisciplinary area. With rapid technological developments, a changing climate and more extreme weather, coupled with developing societal demands, the challenges to the professions responsible are complex and varied; solutions need to be rigorously researched and tested to navigate the dynamic context in which today''s buildings are to be sustained. Within this context, the scope and coverage of the journal incorporates the following indicative topics: • Behavioural and human responses • Building defects and prognosis • Building adaptation and retrofit • Building conservation and restoration • Building Information Modelling (BIM) • Building and planning regulations and legislation • Building technology • Conflict avoidance, management and disputes resolution • Digital information and communication technologies • Education and training • Environmental performance • Energy management • Health, safety and welfare issues • Healthy enclosures • Innovations and innovative technologies • Law and practice of dilapidation • Maintenance and refurbishment • Materials testing • Policy formulation and development • Project management • Resilience • Structural considerations • Surveying methodologies and techniques • Sustainability and climate change • Valuation and financial investment