Residential Building Duration Prediction Based on Mean Clustering and Neural Network
The duration of a residential building project will directly influence its successful implementation; hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural ne...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/2444698 |
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| _version_ | 1832568835307208704 |
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| author | Fanrong Ji Yunquan Nan Aifang Wei Peiyan Fan Zhaoyuan Luo Xiaoqing Song |
| author_facet | Fanrong Ji Yunquan Nan Aifang Wei Peiyan Fan Zhaoyuan Luo Xiaoqing Song |
| author_sort | Fanrong Ji |
| collection | DOAJ |
| description | The duration of a residential building project will directly influence its successful implementation; hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA-BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA-BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA-BP neural network model, this research utilized an artificial bee colony (ABC)-improved K-means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC-K-means-GA-BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC-K-means-GA-BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency. |
| format | Article |
| id | doaj-art-3b5424e8563c42679cd7338459635751 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-3b5424e8563c42679cd73384596357512025-02-03T00:23:21ZengWileyAdvances in Civil Engineering1687-80942024-01-01202410.1155/2024/2444698Residential Building Duration Prediction Based on Mean Clustering and Neural NetworkFanrong Ji0Yunquan Nan1Aifang Wei2Peiyan Fan3Zhaoyuan Luo4Xiaoqing Song5School of Management EngineeringCollege of Surveying and Geo-InformaticsSchool of Design and the Built EnvironmentSchool of Management EngineeringSchool of Management EngineeringSchool of Management EngineeringThe duration of a residential building project will directly influence its successful implementation; hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA-BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA-BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA-BP neural network model, this research utilized an artificial bee colony (ABC)-improved K-means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC-K-means-GA-BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC-K-means-GA-BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency.http://dx.doi.org/10.1155/2024/2444698 |
| spellingShingle | Fanrong Ji Yunquan Nan Aifang Wei Peiyan Fan Zhaoyuan Luo Xiaoqing Song Residential Building Duration Prediction Based on Mean Clustering and Neural Network Advances in Civil Engineering |
| title | Residential Building Duration Prediction Based on Mean Clustering and Neural Network |
| title_full | Residential Building Duration Prediction Based on Mean Clustering and Neural Network |
| title_fullStr | Residential Building Duration Prediction Based on Mean Clustering and Neural Network |
| title_full_unstemmed | Residential Building Duration Prediction Based on Mean Clustering and Neural Network |
| title_short | Residential Building Duration Prediction Based on Mean Clustering and Neural Network |
| title_sort | residential building duration prediction based on mean clustering and neural network |
| url | http://dx.doi.org/10.1155/2024/2444698 |
| work_keys_str_mv | AT fanrongji residentialbuildingdurationpredictionbasedonmeanclusteringandneuralnetwork AT yunquannan residentialbuildingdurationpredictionbasedonmeanclusteringandneuralnetwork AT aifangwei residentialbuildingdurationpredictionbasedonmeanclusteringandneuralnetwork AT peiyanfan residentialbuildingdurationpredictionbasedonmeanclusteringandneuralnetwork AT zhaoyuanluo residentialbuildingdurationpredictionbasedonmeanclusteringandneuralnetwork AT xiaoqingsong residentialbuildingdurationpredictionbasedonmeanclusteringandneuralnetwork |