Networked control systems have gained considerable attention over the last decade as a
result of the trend towards decentralised control applications and the emergence of cyber …

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 …

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

The majority of warehouse expenses is driven by labour cost. Therefore, efficient
management of labour resources is required. To do so, workforce planning is used to match …

Many studies on construction labor productivity (CLP) have been conducted by researchers
worldwide over the years, but there has been no study to date whereby the findings from the …

Construction labour productivity (CLP) is affected by numerous variables made up of
subjective and objective factors. Thus, CLP modelling and prediction are a complex task …

This study investigates the possibility of doing probabilistic forecasting of construction labor
productivity metrics for both long-term and short-term estimates. The research aims to …

Factors affecting labor productivity in the global construction industry: a critical review,
classification and ranking | Scientia et Technica Ir al contenido principal Ir al menú de …

Purpose Construction projects in Malaysia are often delayed and over budget due to heavy
reliance on labor. Linear regression (LR) models have been used in most labor cost (LC) …

Predicting maintenance labor productivity is crucial for effective planning and decision-
making in the electricity industry. This paper aims at predicting maintenance labor …