Hyperdimensional Computing for Lightweight Modal-Based Damage Classification in Concrete Structures

Structural Health Monitoring (SHM) systems increasingly require efficient and scalable methods for identifying structural damage under dynamic loading. Traditional learning-based SHM models often rely on high-dimensional features or deep architectures, which may be computationally intensive and diff...

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書誌詳細

主要な著者:	Xiao-Ling Lin, Stefano Mariani
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2025-11-01
シリーズ:	Engineering Proceedings
主題:	structural health monitoring (SHM) hyperdimensional computing (HDC) impact-induced damage modal frequency lightweight classification concrete-filled steel tube
オンライン･アクセス:	https://www.mdpi.com/2673-4591/118/1/47
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