Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks

Research article (Computers & Structures, 2023) · cited 18× · AI/ML
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Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks

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Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks. Retrieved May 24, 2026, from https://4ort.xyz/entity/ensemble-technique-to-predict-post-earthquake-damage-of-buildings-integrating-tree-based-models-and-tabular-neural-netwo
MLA “Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/ensemble-technique-to-predict-post-earthquake-damage-of-buildings-integrating-tree-based-models-and-tabular-neural-netwo.
BibTeX @misc{4ortxyz_ensemble-technique-to-predict-post-earthquake-damage-of-buildings-integrating-tree-based-models-and-tabular-neural-netwo_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks}}, year = {2026}, url = {https://4ort.xyz/entity/ensemble-technique-to-predict-post-earthquake-damage-of-buildings-integrating-tree-based-models-and-tabular-neural-netwo}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks — https://4ort.xyz/entity/ensemble-technique-to-predict-post-earthquake-damage-of-buildings-integrating-tree-based-models-and-tabular-neural-netwo (retrieved 2026-05-24)

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