Civil Engineering Faculty Publications
Document Type
Article
Publication Date
3-19-2026
Abstract
The prediction of building settlement around foundation pit is of vital importance to ensure the safety and stability of urban construction projects. However, current predictions of buildings surrounding foundation pit face numerous challenges. Therefore, this study proposes a framework that integrates Optuna-based hyperparameter optimization, variational mode decomposition (VMD), and machine learning (ML) for accurate and timely settlement prediction in practical engineering scenarios, referred to as the Optuna–VMD–ML framework. Optuna is employed to tune the hyperparameters of both VMD and the ML models; the Optuna-tuned VMD decomposes the settlement time series into mode components, which are then used to train the ML predictors to forecast settlement. By comparing the settlement prediction performance of four different ML models at 12 monitoring points around the Moulding Building in Taizhou, the results show that the long short-term memory model yields the best prediction accuracy under the proposed framework, with average R2 = 0.927, MSE = 0.013 mm2, RMSE = 0.107 mm, MAE = 0.087 mm, and MAPE = 1.539% across all monitoring points. More importantly, the predicted settlement displacement and velocity are used as core evaluation indicators to construct a comprehensive safety risk assessment framework for buildings around the foundation pit. The proposed method in this paper can effectively realize accurate prediction of settlement around foundation pit buildings, while accurately identifying high-risk areas for structural safety of surrounding buildings. It provides important reference for dynamic monitoring and engineering safety based on settlement prediction.
Recommended Citation
Zhang, Jing, Zhenlin Wang, Shiyu Sheng, et al. 2026. “A Novel Optuna-VMD-ML Framework for Enhanced Settlement Prediction of Buildings around Foundation Pits.” Measurement Science and Technology 37 (11): 116102. https://doi.org/10.1088/1361-6501/ae4f0b.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Measurement Science and Technology
DOI
10.1088/1361-6501/ae4f0b
Comments
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