Civil, Structural, and Environmental Engineering

Mr. Mohammad Saeed Chizari

Amirkabir University of Technology
Iran

Abstract Title: Construction Labour Productivity: Influencing Factors, Categorisation, and Monitoring Methods

Biography:

Mohammad Saeed Chizari was born in 1999 and earned his BSc in Civil Engineering from Amirkabir University of Technology (Tehran Polytechnic) in 2022. He is currently pursuing an MSc in Construction Engineering and Management at Amirkabir, where he ranks first in his cohort with a GPA of 19.35/20. Saeed serves as a teaching assistant for both undergraduate and postgraduate courses within the Civil and Environmental Engineering, as well as the Management, Science, and Technology faculties. He also holds a professional certificate in Fundamentals of Project Management from the University of Adelaide, Australia.

Research Interest:

Productivity is an important index in evaluating project success, and labour productivity plays a key role in the economic growth of developed or developing countries. Considering the significant share of human resources and labour in the costs of a construction project, maximising project profit has a direct and strong relationship with maximising the productivity of project workers. To improve and strengthen the productivity of the project and its human resources, it is essential to identify the factors affecting productivity. Additionally, labour productivity is part of the primary information for financial estimation, project planning, progress control, and claims evaluation. In this study, by investigating previous studies and analysing them while defining productivity, stating the importance in the construction industry, and expressing quantitative calculation formulas, factors affecting the productivity of human resources, especially construction labourers, have been identified and categorized. Finally, productivity measurement and monitoring methods through activity recognition and performance measurement and monitoring are stated. This research summarises the identified effective factors influencing productivity into 58 titles and 10 different groups.
Also, discussing, summarising and concluding the methods used to recognise activities and monitor performance and productivity shows that previous studies have paid more attention to computer vision and combined it with deep learning and machine learning algorithms.

Keywords: Productivity, Construction Labour Productivity (CLP), Factors Affecting Productivity, Activity Recognition, Productivity Monitoring