Explainable machine learning (XML) framework for seismic assessment of structures using Extreme Gradient Boosting (XGBoost)

Research article (Engineering Structures, 2025) · cited 39× · AI/ML
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Explainable machine learning (XML) framework for seismic assessment of structures using Extreme Gradient Boosting (XGBoost)

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Explainable machine learning (XML) framework for seismic assessment of structures using Extreme Gradient Boosting (XGBoost) is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Explainable machine learning (XML) framework for seismic assessment of structures using Extreme Gradient Boosting (XGBoost). Retrieved May 24, 2026, from https://4ort.xyz/entity/explainable-machine-learning-xml-framework-for-seismic-assessment-of-structures-using-extreme-gradient-boosting-xgboost
MLA “Explainable machine learning (XML) framework for seismic assessment of structures using Extreme Gradient Boosting (XGBoost).” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/explainable-machine-learning-xml-framework-for-seismic-assessment-of-structures-using-extreme-gradient-boosting-xgboost.
BibTeX @misc{4ortxyz_explainable-machine-learning-xml-framework-for-seismic-assessment-of-structures-using-extreme-gradient-boosting-xgboost_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Explainable machine learning (XML) framework for seismic assessment of structures using Extreme Gradient Boosting (XGBoost)}}, year = {2026}, url = {https://4ort.xyz/entity/explainable-machine-learning-xml-framework-for-seismic-assessment-of-structures-using-extreme-gradient-boosting-xgboost}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Explainable machine learning (XML) framework for seismic assessment of structures using Extreme Gradient Boosting (XGBoost) — https://4ort.xyz/entity/explainable-machine-learning-xml-framework-for-seismic-assessment-of-structures-using-extreme-gradient-boosting-xgboost (retrieved 2026-05-24)

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