Modeling labor costs using artificial intelligence tools

IF 2.1 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
M. H. Momade, S. Durdyev, Saurav Dixit, Shamsuddin Shahid, Abubakar Kori Alkali
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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.
使用人工智能工具建模劳动力成本
由于严重依赖劳动力,马来西亚的建设项目经常被推迟和超出预算。线性回归(LR)模型已用于大多数劳动力成本(LC)研究,其准确性低于机器学习(ML)工具。近年来,建筑管理应用越来越多地使用机器学习工具,并极大地影响了预测。本研究旨在利用统计方法识别影响最大的LC因素,收集数据并预测LC模型,以改进LC预测。设计/方法/方法完成了全面的文献综述,以确定LC因素。有经验的项目经理被要求根据重要性和相关性对这些因素进行排序。然后,收集排名最高的六个因素的数据,并创建五个ML模型。最后,利用5个分类指标分析和衡量模型在确定绩效类别方面的有效性。发现工人年龄、建筑技能、工人出身、工人培训/教育、工作类型和工人经验被确定为影响LC的最重要因素。与其他模型相比,SVM提供了最好的结果。原创性/价值研究结果支持数据驱动的监管和实践改进,旨在改善马来西亚的劳工问题,并有可能在其他面临类似问题的国家复制。
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来源期刊
CiteScore
4.80
自引率
18.20%
发文量
76
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
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