Consumption quota model based on BP.
<div><p>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 co...
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2025
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| _version_ | 1852019786003775488 |
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| author | Xuwei Liu (6034592) |
| author2 | Wenting Tang (2627902) Lisha Si (21463912) Yongde Li (13544376) |
| author2_role | author author author |
| author_facet | Xuwei Liu (6034592) Wenting Tang (2627902) Lisha Si (21463912) Yongde Li (13544376) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xuwei Liu (6034592) Wenting Tang (2627902) Lisha Si (21463912) Yongde Li (13544376) |
| dc.date.none.fl_str_mv | 2025-06-02T17:29:26Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0324854.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Consumption_quota_model_based_on_BP_/29213653 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified quota compilation efficiency prediction error rates mean squared errors cost savings rates average absolute errors artificial neural networks artificial neural network electrical installation engineering consumption quota compilation mechanical equipment use mechanical equipment usage proposed model achieved 48 %, respectively 48 %, 1 21 %, 4 1 %, respectively 1 %, material consumption engineering budgeting 85 %, 25 %, research model model performance designed model 1 %. xlink "> traditional models testing sets statistical theory results showed relatively low regularization techniques prefabricated components prefabricated buildings material resources large workload labor hours economic benefits determination coefficients 8 %, 4 %. |
| dc.title.none.fl_str_mv | Consumption quota model based on BP. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_d3fbfc4ac4b8dfe1a1ffdab92664ea3f |
| identifier_str_mv | 10.1371/journal.pone.0324854.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29213653 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Consumption quota model based on BP.Xuwei Liu (6034592)Wenting Tang (2627902)Lisha Si (21463912)Yongde Li (13544376)BiophysicsBiotechnologyScience PolicySpace ScienceBiological Sciences not elsewhere classifiedquota compilation efficiencyprediction error ratesmean squared errorscost savings ratesaverage absolute errorsartificial neural networksartificial neural networkelectrical installation engineeringconsumption quota compilationmechanical equipment usemechanical equipment usageproposed model achieved48 %, respectively48 %, 121 %, 41 %, respectively1 %,material consumptionengineering budgeting85 %,25 %,research modelmodel performancedesigned model1 %.xlink ">traditional modelstesting setsstatistical theoryresults showedrelatively lowregularization techniquesprefabricated componentsprefabricated buildingsmaterial resourceslarge workloadlabor hourseconomic benefitsdetermination coefficients8 %,4 %.<div><p>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.</p></div>2025-06-02T17:29:26ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0324854.g005https://figshare.com/articles/figure/Consumption_quota_model_based_on_BP_/29213653CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292136532025-06-02T17:29:26Z |
| spellingShingle | Consumption quota model based on BP. Xuwei Liu (6034592) Biophysics Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified quota compilation efficiency prediction error rates mean squared errors cost savings rates average absolute errors artificial neural networks artificial neural network electrical installation engineering consumption quota compilation mechanical equipment use mechanical equipment usage proposed model achieved 48 %, respectively 48 %, 1 21 %, 4 1 %, respectively 1 %, material consumption engineering budgeting 85 %, 25 %, research model model performance designed model 1 %. xlink "> traditional models testing sets statistical theory results showed relatively low regularization techniques prefabricated components prefabricated buildings material resources large workload labor hours economic benefits determination coefficients 8 %, 4 %. |
| status_str | publishedVersion |
| title | Consumption quota model based on BP. |
| title_full | Consumption quota model based on BP. |
| title_fullStr | Consumption quota model based on BP. |
| title_full_unstemmed | Consumption quota model based on BP. |
| title_short | Consumption quota model based on BP. |
| title_sort | Consumption quota model based on BP. |
| topic | Biophysics Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified quota compilation efficiency prediction error rates mean squared errors cost savings rates average absolute errors artificial neural networks artificial neural network electrical installation engineering consumption quota compilation mechanical equipment use mechanical equipment usage proposed model achieved 48 %, respectively 48 %, 1 21 %, 4 1 %, respectively 1 %, material consumption engineering budgeting 85 %, 25 %, research model model performance designed model 1 %. xlink "> traditional models testing sets statistical theory results showed relatively low regularization techniques prefabricated components prefabricated buildings material resources large workload labor hours economic benefits determination coefficients 8 %, 4 %. |