Abstract:
Supply chain management in the construction industry is very critical phenomenon.
However, challenges in the supply chain often led to delays, cost overruns, and quality
issues in projects. This research focuses on addressing the significant challenges faced
by the construction industry in Pakistan's Supply Chain Management, particularly in the
realm of material estimation, procurement inefficiencies, and resource allocation. The
study adopted a quantitative iterative experimental approach by utilizing Machine
Learning AI, namely YOLO V8 and python coding to automate the process of Cost
Estimation in construction supply chain. The study aims to serve as a proof for concept
that “Detection using ML algorithms” can be used to automate Cost Estimation. The
YOLOv8 model was trained on 5 blueprint components (walls, floor, columns, windows,
and doors). Python codes were used to extract bounding box coordinates which were
further used for cost estimation. The research findings show data that yielded high
accuracy percentages, establishing that with better ML training sets, 100% accuracy of
cost estimations is achievable.
