Peter E.D. Love

TL;DR: In this paper, the authors describe how changes and their actions or effects otherwise known as dynamics can impact the project management system and the major factors influencing a project's performance are observed.

TL;DR: The results reveal that the developed hybrid model (CNN + LSTM) is able to accurately detect safe/unsafe actions conducted by workers on-site and exceeds the current state-of-the-art descriptor-based methods for detecting points of interest on images.

TL;DR: Findings from a questionnaire survey sought to examine the approaches used by 126 construction organisations to evaluate and justify their IT investments, as well as the benefits and costs that they have experienced due to IT implementation.

TL;DR: This analysis presents conflicting perspectives surrounding the scope and sensitivity of traditional appraisal methods and presents taxonomies of IS benefit types and associated natures to discuss the resulting implications of using traditional appraisal techniques during the IS planning and decision-making process.

TL;DR: An automated computer vision-based method that uses two convolutional neural network models to determine if workers are wearing their harness when performing tasks while working at heights can be used by construction and safety managers to proactively identify unsafe behavior and take immediate action to mitigate the likelihood of a FFH occurring.