Research on profit distribution mechanism of green supply chain for precast buildings

Abstract In prefabricated building construction, efficient profit distribution within the supply chain is vital. This study leverages machine learning models decision trees (DT), random forests, k-nearest neighbors, and deep neural networks to classify task groups, including “Design Team,” “Quality,” “Safety,” and “Site Management.” Our analysis evaluates these models’ using accuracy, precision, recall, and F-1 score, revealing high performance, particularly for DT and DNN models, which achieved 97% accuracy. The strong results across all models highlight their reliability for optimizing resource allocation, enhancing project efficiency, and providing key insights into broader construction industry applications.