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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 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.
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 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.
Residential Building Duration Prediction Based on Mean Clustering and Neural Network
Fanrong Ji (author) / Yunquan Nan (author) / Aifang Wei (author) / Peiyan Fan (author) / Zhaoyuan Luo (author) / Xiaoqing Song (author)
2024
Article (Journal)
Electronic Resource
Unknown
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