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Explainable AI models for predicting liquefaction-induced lateral spreading

Autor(en):


Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Frontiers in Built Environment, , v. 10
DOI: 10.3389/fbuil.2024.1387953
Abstrakt:

Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by capturing complex soil characteristics and site conditions. However, the “black box” nature of ML models can hinder their adoption in critical decision-making.

Method: This study addresses this limitation by using SHapley Additive exPlanations (SHAP) to interpret an eXtreme Gradient Boosting (XGB) model for lateral spreading prediction, trained on data from the 2011 Christchurch Earthquake.

Result: SHAP analysis reveals the factors driving the model's predictions, enhancing transparency and allowing for comparison with established engineering knowledge. Notably, the SHAP values expose an unexpected behavior in the PGA feature. Moreover, the results demonstrate that the XGB model successfully identifies the importance of soil characteristics derived from Cone Penetration Test (CPT) data in predicting lateral spreading, validating its alignment with domain understanding.

Discussion: This work highlights the value of explainable machine learning for reliable and informed decision-making in geotechnical engineering and hazard assessment.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.3389/fbuil.2024.1387953.
  • Über diese
    Datenseite
  • Reference-ID
    10789910
  • Veröffentlicht am:
    20.06.2024
  • Geändert am:
    20.06.2024
 
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