The construction duration of residential projects, especially in building processes, significantly impact the business of a construction company. The balance between the planned cost, direct cost, and overheads directly depend on the precision of the implementation phase of the project. The application of the artificial neural network (ANN) to predict the duration of implementation of a residential construction project from the pre-design stage to completion is comprehensively discussed in this research. The study applies the backpropagation (BP) network made of nodes for error evaluation of the training states. Further, the proposed system illustrated that the artificial neural network (ANN) satisfy the three crucial criteria (cost, quality, and time) used for the evaluation of projects. The ANN provided accurate data for the training and estimation of, the duration of a residential construction project with adequate resources of implementation. The performance of the results for the ANN at 105 iteration shows that the prediction was 99.841% accurate for the overall system. The best fit occurred at the 99th epoch with an MSE of 0.10286.

This study was intended to theorize fresh conceptual links between two common project management studies, the PMIS and paradigms of decision-making. This research examined the pragmatic conceptual connection between project management information system and low / high decision-making. The findings indicate that the function of Project Management Information Systems negotiation information overload and design overload for decision-making has been practiced and monitored. Overall, this provides a nice review of Western theories and ideas in the distinctive eastern framework.