Detail Publikasi
Abstrak
Artificial Intelligence (AI) has become a prominent technology in recent years because of the growing need for automated systems that operate without human intervention. Artificial intelligence is flourishing right now, with a plethora of new models appearing in research and development phases across a range of areas, including science, economics, and engineering. Project management (PM) is a professional service that uses specific project \management techniques to manage, control, and execute the planning, design, and construction of a project, from its beginning until its finale. The goal of PM is to control a project's schedule, cost, safety, and quality, this paper addresses the application of ANNs in project tasks relating to estimation of costs, risk, safety, as well as labor productivity, and optimal work environment. This review asserts that ANN was extremely effective in predicting the best potential solution. However, the accuracy of the data and the skill of the user are key factors in obtaining the precise solution. This paper serves as a starting point for scholars interested in project leadership and management in the exciting realm of artificial intelligence. This article addressed artificial intelligence-based systems, from their definition through their uses. Based on these studies, a three-stage model of input, process, and output was created. Three stages model of the AI model in the intensive unit was completed. Input included Cost Data, Evaluation data, productivity, Safety data). Output (Decision Making, Solutions, Risk Stratification, Prognosis, Management). The process included artificial intelligence (artificial neural network, machine learning, deep learning, and expert systems). The output, which was decision-making, included diagnosis, Solutions, risk stratification, prognosis, and management. Efforts of the management system. The aim o f this study in project management is to keep a project's schedule, cost, safety, and quality under control as a result, the artificial neural network method is particularly effective in this scenario because it employs discrete and insufficient data to arrive at the best feasible solution. This research examines the use of artificial neural networks (ANNs) in construction activities related to cost, risk, and safety, as well as labor productivity and the optimal work environment. According to this study, ANN was extremely effective in predicting the best probable option.