A physics-data-driven method for predicting surface and building settlement induced by tunnel construction

Surface and surrounding building settlement is frequently caused by soil disturbance during subway tunnel construction, significantly impacting construction safety and structural stability. Traditional machine learning models have shown some effectiveness in settlement prediction but often fail to c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers and geotechnics Jg. 179; S. 107020
Hauptverfasser: Wang, You, Fan, Qianjun, Dai, Fang, Wang, Rui, Ding, Bosong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2025
Schlagworte:
Metaheuristic optimization algorithms
Pasternak foundation model
Physics informed optimized extreme learning machine (PIOELM)
Physics-data-driven
Shield tunnelling
Metaheuristic optimization algorithms
Pasternak foundation model
Shield tunnelling
Physics informed optimized extreme learning machine (PIOELM)
Physics-data-driven
ISSN:0266-352X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Surface and surrounding building settlement is frequently caused by soil disturbance during subway tunnel construction, significantly impacting construction safety and structural stability. Traditional machine learning models have shown some effectiveness in settlement prediction but often fail to capture the underlying physical mechanisms. This study proposed a novel physics-informed optimized extreme learning machine (PIOELM) to enhance prediction accuracy and physical interpretability. Based on the extreme learning machine (ELM), the model integrated the chaos adaptive sparrow search algorithm (CASSA) for parameter optimization and incorporated the Pasternak foundation model using automatic differentiation. The model’s accuracy was validated using precise engineering data and compared against the physics-informed neural network (PINN), physics-informed extreme learning machine (PIELM), and traditional data-driven models. The results show that the PIOELM model outperforms others in handling extreme values and maintains high accuracy across various scales of data prediction. Prediction accuracy improved by up to 85.29%, with a minimum improvement of 30.68%, demonstrating strong stability and generalization capabilities.
ISSN:0266-352X
DOI:10.1016/j.compgeo.2024.107020