• Raj Shende
• Harshwardhan Atkare
• Atharv Bhadange
• Prajwal Toundakar

This research employed artificial neural networks (ANNs) to analyze the intricate relationships between various input factors and the cost and duration of construction projects. The dataset for the study was generated based on six input factors: Project Type, Project Size, Location, Complexity, Number of Floors, Start Date, End_Date, Construction Cost, Material Cost, Labor Cost, Equipment Cost, Permit Cost, Contingency Cost, Overhead Cost, Number of Workers, Weather Conditions, Previous Project Completion Time, Previous Project Cost, and two output parameters (duration and cost). These factors were identified through a thorough review of relevant literature, expert opinions, and an extensive field survey. The research revealed that insufficient technological advancements and a lack of skilled personnel for effective project coordination contribute to cost overruns and extended durations, particularly in projects awarded to sole and mini-contractors compared to medium and multi-company firms. Clients with greater financial capacity, such as corporate organizations and government entities, tended to engage and negotiate with multi- and medium-sized businesses.

Construction management · Project cost and duration · Artificial neural network (ANN) · MATLAB

"Construction Progress Prediction and Cost and Time Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.j85-j90, March-2024, Available :http://www.jetir.org/papers/JETIR2403915.pdf

"Construction Progress Prediction and Cost and Time Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppj85-j90, March-2024, Available at : http://www.jetir.org/papers/JETIR2403915.pdf