Geoscience Frontiers· 2019Q1· Review
State-of-the-art review of soft computing applications in underground excavations
- 479citations
- Q1SCImago
- 2019year
Short summary
Soft computing techniques like XGBoost, MARS, ANN, and SVM outperform traditional methods in predicting lateral wall deflection in braced excavations.
AI-generated from the title and abstract; the full text is not read.
Key points
- Soft computing techniques offer superior predictive capacity for complex geotechnical engineering systems.
- XGBoost, MARS, ANN, and SVM were compared for estimating maximum lateral wall deflection in braced excavations.
- Soft computing methods showed better performance than traditional approaches in the case study.
- The review discusses the merits and limitations of various soft computing techniques.
AI-generated from the title and abstract; the full text is not read.
Abstract
Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations . A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available.
The authors' abstract, as published at the source. Geoscience Frontiers, 2019 · DOI ↗
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