Commonly Used Methods for Construction Labour Performance Monitoring and Productivity Assessment

Given the considerable share of human resources and labour in a construction project's overall costs, there is a direct and substantial link between maximising project profits and enhancing labour productivity. Furthermore, labour productivity is a crucial component of the foundational data used for financial forecasting, project planning, progress monitoring, and claims evaluation. In the construction industry, productivity is commonly measured by comparing the actual progress of work with the expected progress detailed in the approved project schedule. Regular and continuous measurement of construction labour performance is critical for enhancing efficiency and effectiveness. Evaluating productivity involves recognising activities and tracking labour's efforts. This article aims to survey and identify the methods employed to monitor the activities and performance of construction labour and equipment in order to assess their productivity. It includes categorising the identified methods and approaches, an analysis of the frequency with which these methods appear in recent scientific studies and articles, and a determination of the most commonly used methods and tools. This study presents a comprehensive categorisation of methods and approaches based on the reviewed articles. The investigation of methods and tools reveals that the utilisation of computer vision (CV), often combined with deep learning (DL) and machine learning (ML), is more prevalent in recent studies focused on activity recognition, performance monitoring, and the assessment of construction labour productivity.

کلیدواژه ها:

Construction Labour Productivity ، Activity Recognition ، Performance Monitoring ، Construction Project Management ، Computer Vision ، Machine Learning