Monitoring Data Fusion Model for Subsoil Layer Deformation Prediction

Predicting soil deformation is critical for the success of building construction projects. The traditional methods used for this task, which rely on theoretical calculations and numerical simulations, require detailed information on soil characteristics and geological conditions. These essential det...

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Bibliographic Details
Published inBuildings (Basel) Vol. 14; no. 7; p. 2055
Main Authors Wu, Huiguo, Wu, Yuedong, Liu, Jian, Zhang, Lei, Zhu, Yongyang, Liang, Chuanyang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2024
Subjects
Accuracy
Algorithms
Civil engineering
Construction
Construction industry
Data integration
Deep learning
Deformation
Forecasts and trends
Geology
Long short-term memory
LSTM
Monitoring
monitoring data
Neural networks
Numerical analysis
Predictions
Project engineering
Root-mean-square errors
SHAP
Simulation methods
Soil characteristics
soil deformation
Soil layers
Soil properties
Subsoils
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Summary:Predicting soil deformation is critical for the success of building construction projects. The traditional methods used for this task, which rely on theoretical calculations and numerical simulations, require detailed information on soil characteristics and geological conditions. These essential details are often challenging to obtain in practical engineering, thereby limiting the accuracy of these methods in building construction contexts. Deep learning (DL) provides a direct approach for modeling soil deformation without having a detailed understanding of the soil properties and geological conditions. However, the existing DL algorithms mainly focus on modeling deformation directly. With advancements in monitoring technology, integrating diverse monitoring data has become crucial for accurately predicting deformation, a need often overlooked in current practices. This paper introduces a monitoring data fusion (MDF) model aimed at enhancing the utilization efficiency of diverse monitoring data. Validated against real-world engineering scenarios, this model significantly outperforms traditional single-feature and multi-feature long short-term memory (LSTM) models. It achieves a mean absolute percentage error (MAPE) of approximately 2.12%, representing reductions of 30% and 63%, and a root mean square error (RMSE) of around 12.5 mm, with reductions of 36% and 77%. Additionally, the DL interpretability method, Shapley additive explanations (SHAP), is utilized to elucidate how various model features contribute to generating predictions.
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings14072055