AI-based Cost Estimation from HVAC symbols
Executive Summary
Architectural plans for large-scale construction projects often include intricate HVAC (Heating, Ventilation, and Air Conditioning) layouts. These plans contain numerous symbols representing different HVAC components, and manually analyzing them to estimate material costs is a time-consuming and error-prone process. Traditionally, a team of professionals would manually review these plans, identify the symbols, and calculate the total cost of components, making the process inefficient and prone to oversight.
To address these challenges, we developed an AI-driven solution that automates the detection of HVAC symbols in architectural plans. By leveraging deep learning models trained specifically for architectural schematics, our system identifies different classes of symbols and provides an accurate cost estimate based on predefined pricing. This significantly reduces the time required for cost estimation and minimizes human errors, leading to greater efficiency and accuracy in project planning.
About Our Client
Client Name: Confidential
Industry: Construction, Software
Location: United States
Technologies
YOLOv8, SAHI, Python, FastAPI, AWS S3, AWS EC2
