The predictive performance of machine learning (ML) models is challenged when trained on
class imbalance real-world construction datasets, reducing the accuracy of relevant …

Purpose: Several productivity prediction models have been developed for the prediction of
construction productivity and enhancing the effectiveness of resource allocation, workflow …

The reliability of risk assessment is crucial for designing effective construction safety
management strategies. Construction safety prediction using machine learning models is …

This paper explores the application of automated machine learning (AutoML) techniques to
the construction industry, a sector vital to the global economy. Traditional ML model …

Predicting construction productivity is challenging because of the complexity involved in the
construction process and the variability in factors that regularly affect these projects …

This research proposes a reliable model for identifying different construction materials with
the highest accuracy, which is exploited as an advantageous tool for a wide range of …

Artificial intelligence algorithms employed for classifying excavator-related activities
predominantly rely on sensors embedded within individual machinery or computer vision …

Construction materials undergo appearance and textural changes during the construction
process. Accurate recognition of these changes is critical for effectively understanding the …

This paper aims to propose a novel deep learning-integrated framework for deriving reliable
simulation input models through incorporating multi-source information. The framework …

Buildings consume huge amounts of energy to create a comfortable and healthy built
environment for people. The building engineering industry has benefitted from the advances …