State-of-the-art review of soft computing applications in underground excavations
| Autor: | Anthony T. C. Goh, Suzanne Lacasse, Runhong Zhang, Zhongqiang Liu, Wengang Zhang, Hanlong Liu, Chongzhi Wu |
|---|---|
| Přispěvatelé: | School of Civil and Environmental Engineering |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Wall deformation
010504 meteorology & atmospheric sciences Computer science 010502 geochemistry & geophysics Machine learning computer.software_genre 01 natural sciences Soft computing method (SCM) Underground Excavations Deflection (engineering) Underground excavations 0105 earth and related environmental sciences Soft computing Soft Computing Method Multivariate adaptive regression splines Civil engineering [Engineering] Artificial neural network business.industry lcsh:QE1-996.5 Predictive capacity Excavation State of the art review Support vector machine lcsh:Geology General Earth and Planetary Sciences Artificial intelligence Lateral wall business computer |
| Zdroj: | Geoscience Frontiers, Vol 11, Iss 4, Pp 1095-1106 (2020) Geoscience Frontiers |
| ISSN: | 1674-9871 |
| Popis: | 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. Published version |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|