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To address the problems of limited sample size， strong variable coupling， and difficulty in adaptive ensemble weight allocation in carbon emission prediction for asphalt pavement maintenance construction， an ensemble prediction method based on Gaussian mixture model (GMM) data augmentation and improved non-dominated sorting genetic algorithm II (NSGA-II) weight optimization is proposed. Using 17 actual maintenance projects as samples， a seven-variable energy-consumption feature system is constructed. GMM augmentation is applied only to the training set， expanding the number of training samples from 12 to 70， and support vector machine (SVM)， least absolute shrinkage and selection operator (LASSO)， and gradient boosting decision tree (GBDT) are selected as base learners. The results show that the augmented samples are mainly distributed within the neighborhood of the original training samples. Under the current test-set conditions， the mean absolute error (MAE)， root mean square error (RMSE)， mean absolute percentage error (MAPE)， and coefficient of determination (R2) of the proposed model are 4.673， 6.291， 9.009%， and 0.934， respectively. In the Bootstrap resampling analysis based on the fixed test set， the proposed method exhibited lower overall errors than the averaging and empirical weighting methods. The proposed method can provide a reference for carbon emission prediction and low-carbon scheme comparison in similar asphalt pavement maintenance projects.
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Basic Information:
China Classification Code:TP18;U418.6;U416.217
Citation Information:
[1]LIAO Huanwang,JIN Jiayu,XIAO Qing ,et al.Carbon Emission Prediction for Asphalt Pavement Maintenance Construction Based on Meta-Learner Parameter Optimization[J].Journal of Nantong University (Natural Science Edition)().
Fund Information:
江西省交通运输厅科技项目（2025ZG004）
2026-11-04
2026-11-04
2026-11-04