<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>At present, in the assembly building construction practice, the relevant subjects lack the concept of building quality management and awareness of responsibility, and the rights and duties of the subjects of each link are not clear in the division of responsibility for building quality. In this paper, based on the deep confidence network, for the problem that the accuracy of the traditional DBN model will gradually decrease, a genetic algorithm is introduced to optimize the conventional restricted Morzmann machine, and the number of nodes in the hidden layer of the genetic algorithm optimization DBN node number process is obtained after the improvement. The optimal method for planning building quality assessment is selected based on the comparison results of the established DBN algorithm function. Then, the optimal building quality assessment model is constructed. Then design the evaluation index system for quality influencing factors and verify it with structural equations. Finally, the model is used to quantify the degree of influence of assembly building quality. The study concludes that all path coefficients affecting the quality of assembled buildings are greater than 0.5, the P-value is less than 0.001, and the five proposed hypotheses are all valid. In the assessment of the quality of residential projects, the final results of excellent, good, moderate, and qualified accounted for 0.1952, 0.2299, 0.3086, and 0.2663, respectively, and the quality of the project’s construction was evaluated as a good grade. This study provides a new method for improving the awareness of quality responsibility among relevant subjects in the construction industry and guaranteeing the level of building quality.</p>
</abstract>

At present, in the assembly building construction practice, the relevant subjects lack the concept of building quality management and awareness of responsibility, and the rights and duties of the subjects of each link are not clear in the division of responsibility for building quality. In this paper, based on the deep confidence network, for the problem that the accuracy of the traditional DBN model will gradually decrease, a genetic algorithm is introduced to optimize the conventional restricted Morzmann machine, and the number of nodes in the hidden layer of the genetic algorithm optimization DBN node number process is obtained after the improvement. The optimal method for planning building quality assessment is selected based on the comparison results of the established DBN algorithm function. Then, the optimal building quality assessment model is constructed. Then design the evaluation index system for quality influencing factors and verify it with structural equations. Finally, the model is used to quantify the degree of influence of assembly building quality. The study concludes that all path coefficients affecting the quality of assembled buildings are greater than 0.5, the P-value is less than 0.001, and the five proposed hypotheses are all valid. In the assessment of the quality of residential projects, the final results of excellent, good, moderate, and qualified accounted for 0.1952, 0.2299, 0.3086, and 0.2663, respectively, and the quality of the project’s construction was evaluated as a good grade. This study provides a new method for improving the awareness of quality responsibility among relevant subjects in the construction industry and guaranteeing the level of building quality.

Analysis of assembly building quality influencing factors based on deep confidence network

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

At present, in the assembly building construction practice, the relevant subjects lack the concept of building quality management and awareness of...

Analysis of assembly building quality influencing factors based on deep confidence network

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 08 mar 2024

Przyjęty: 02 cze 2024

DOI: https://doi.org/10.2478/amns-2024-1612

Słowa kluczowe
Deep confidence network, RBM, Genetic algorithm, Assembly building

© 2024 Jin Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of assembly building quality influencing factors based on deep confidence network

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 08 mar 2024

Przyjęty: 02 cze 2024

DOI: https://doi.org/10.2478/amns-2024-1612

Słowa kluczoweDeep confidence network, RBM, Genetic algorithm, Assembly building

© 2024 Jin Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep confidence network, RBM, Genetic algorithm, Assembly building