Leveraging automated field data capturing technologies and Building Information Modeling
(BIM) for site status analysis would play a more significant role in advancing decision …

Based on the limitations of traditional procurement, this study uses analytical network
process (ANP) for contractor selection. Using extensive literature review, best value (BV) …

Purpose The study is aimed to compare the prediction success of basic machine learning
and ensemble machine learning models and accordingly create novel prediction models by …

Construction industries are among the most hazardous and high-risk occupations that
involve mild-to-severe accidents and bodily injuries. The aim of this study is to explore the …

The factors that affect productivity are a major focus in construction. This article proposes a
machine learning–based approach to predict task productivity by using a subjective …

Construction organizations strive to increase their competitiveness by reducing the project
costs especially labor cost. Optimizing labor productivity will ensure value for money. In the …

Construction contractors do not have a clear understanding of sustainability, especially in
developing countries. However, they welcome higher productivity as a determinant …

Lean construction (LC) and BIM system are two innovations that increase labor productivity.
The study aims to quantitatively measure the impact of synergies between LC and BIM …

Time-cost trade-off is an important optimization problem for contractors, because its optimum
solution minimizes the total project cost. Formation of the time-cost trade-off problem causes …

The pre-planning phase prior to construction is crucial for ensuring an effective and efficient
project delivery. Realistic productivity rates forecasted during pre-planning are essential for …