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

## TL;DR

A new model using artificial neural networks improves the accuracy and efficiency of consumption quota compilation in prefabricated building installations.

## Contribution

A novel consumption quota compilation model using artificial neural networks and optimization techniques for prefabricated building installations.

## Key findings

- The model achieved low prediction errors for material consumption, labor hours, and mechanical equipment usage.
- The model outperformed traditional methods in quota compilation efficiency and cost savings.
- Validation results showed high accuracy and determination coefficients on training and testing datasets.

## Abstract

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.