Consumption quota compilation based on BP artificial neural network algorithm in mechanical and electrical installation engineering of prefabricated buildings

• Published in: PloS one Vol. 20; no. 6; p. e0324854
• Main Authors: Liu, Xuwei, Tang, Wenting, Si, Lisha, Li, Yongde
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.06.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Biology and Life Sciences; Building information modeling; Carbon; Civil engineering; Computer and Information Sciences; Construction costs; Construction Materials; Consumption; Data collection; Earth Sciences; Electrical engineering; Electrical installations; Energy consumption; Energy efficiency; Engineering and Technology; Factories; Green buildings; Humans; Installation; Labor; Machine learning; Manpower; Mathematical optimization; Mechanical engineering; Mechanical equipment; Methods; Neural networks; Neural Networks, Computer; Optimization; Pareto optimum; Particle swarm optimization; Physical Sciences; Prefabricated buildings; Production processes; Regularization; Research and Analysis Methods; Return on investment; Social Sciences; Statistical analysis; Statistical models; Training; Urban planning; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0324854

The traditional quota compilation method has a large workload and requires a lot of manpower and material resources, making it difficult to apply to the consumption quota compilation in mechanical and electrical installation engineering of prefabricated buildings. Therefore, a consumption quota compilation model on the basis of artificial neural network is built. On the basis of the traditional quota formulation model based on statistical theory, artificial neural networks are introduced, and regularization techniques and particle swarm optimization algorithms are taken to optimize the model performance. The experiment was validated using project datasets covering different regions, scales, and types of prefabricated components. The results showed that the mean squared errors on the training and testing sets were 1.2% and 1.1%, and the average absolute errors were 8.3% and 8.1%, respectively. In addition, the determination coefficients on the training and testing sets were 95.1% and 92.8%, and the accuracy was 92.3% and 91.4%. Further case analysis also showed that the prediction error rates of the research model for material consumption, labor hours, and mechanical equipment usage were relatively low, not exceeding 2.48%, 1.25%, and 4.1%, respectively. In addition, in terms of quota compilation efficiency and economic benefits, the proposed model achieved a quota compilation efficiency value of 90.1%. The return on investment in material consumption, labor hours, and mechanical equipment use was 5.03, 6.09, and 5.92, respectively, and the cost savings rates were 6.21%, 4.85%, and 5.48%, respectively, all of which were better than traditional models. Overall, the designed model can optimize the accuracy of engineering budgeting and the ability to control costs.