Soft Computing to Predict Earthquake-Induced Soil Liquefaction via CPT Results
Auteur(s): |
Ali Reza Ghanizadeh
Ahmad Aziminejad Panagiotis G. Asteris Danial Jahed Armaghani |
---|---|
Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Infrastructures, 31 juillet 2023, n. 8, v. 8 |
Page(s): | 125 |
DOI: | 10.3390/infrastructures8080125 |
Abstrait: |
Earthquake-induced soil liquefaction (EISL) can cause significant damage to structures, facilities, and vital urban arteries. Thus, the accurate prediction of EISL is a challenge for geotechnical engineers in mitigating irreparable loss to buildings and human lives. This research aims to propose a binary classification model based on the hybrid method of a wavelet neural network (WNN) and particle swarm optimization (PSO) to predict EISL based on cone penetration test (CPT) results. To this end, a well-known dataset consisting of 109 datapoints has been used. The developed WNN-PSO model can predict liquefaction with an overall accuracy of 99.09% based on seven input variables, including total vertical stress (σv), effective vertical stress (σv′), mean grain size (D50), normalized peak horizontal acceleration at ground surface (αmax), cone resistance (qc), cyclic stress ratio (CSR), and earthquake magnitude (Mw). The results show that the proposed WNN-PSO model has superior performance against other computational intelligence models. The results of sensitivity analysis using the neighborhood component analysis (NCA) method reveal that among the seven input variables, qc has the highest degree of importance and Mw has the lowest degree of importance in predicting EISL. |
Copyright: | © 2023 the Authors. Licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
2.56 MB
- Informations
sur cette fiche - Reference-ID
10739788 - Publié(e) le:
02.09.2023 - Modifié(e) le:
14.09.2023