Construction Labor Productivity Modeling and Use of Neural Networks: A Bibliometric Survey

Productivity of a project has a major impact on its cost and profitability. In spite of construction being labor intensive field with labor cost adding up to 30% to 50% of overall project cost, the productivity of labor is one of the least studied areas in the construction industry. It requires to be given due attention to the issues affecting labor productivity and design solution using soft computing techniques to improve the overall performance of the industry. This research paper aims to conduct a bibliographic survey of the literature available in the domain of Labor Productivity (LP) as well as the application of Neural Network (NN) for the prediction of labor productivity in the construction sector. Time span considered for the survey is from the year 1996 to 2020. This bibliographic survey mainly focuses on literature from Scopus database. It provides statistics of publications by journals, countries, authors, and citations till date. The study intends to highlight the quantum of research in the selected domains and compare the two to derive a suitable conclusion. The outcome of this survey not only emphasizes the need for research in labor productivity assessment using NN but also throws light on the urge to promote and conducting research by Indian researchers. The study is a first of its kind bibliometric analysis for labor productivity in construction projects. It explores the particulars of previous studies carried out in the selected domain and is helpful in designing methodology for carrying out further research in the domain