The purpose of this paper is to propose a data-driven approach for preparation of
Construction Labour Productivity (CLP) models from influencing labour factors. Two state-of …

This paper presents a novel approach, using hybrid feature selection (HFS), machine
learning (ML), and particle swarm optimization (PSO) to predict and optimize construction …

Abstract Construction Labour Productivity (CLP) is important to the construction industry as it
has a direct impact on the competitiveness of small and medium enterprises. This article's …

Purpose This paper aims to model different causal relations among factors interacting with
labour productivity in order to recognize the most important factors influencing and …

The construction industry is mainly dependent on human resources, and labour costs are
significant. Although many researchers have investigated construction labour productivity …

The current practice to estimate construction labor productivity (CLP) lacks a systematic
approach to measure and estimate it. Moreover, modeling CLP is challenging because, in …

Complex variability is a significant problem in predicting construction crew productivity.
Neural Networks using supervised learning methods like Feed Forward Back Propagation …

Construction sector productivity is of great importance for the government and policymakers
because it determines a nation's future living standards and creates a competitive business …

Poor construction labour productivity is a major issue within the construction industry
because it directly contributes to cost and schedule overrun. Although considerable …

Purpose Despite long-term, sustained research and industry practice, predicting
construction labour productivity (CLP) using existing factor and activity modelling …