Vollständiger Abstract
Worum geht es in dieser Arbeit?
This paper presents an integrated engineering framework that combines Structural Health Monitoring (SHM), Finite Element Analysis (FEA), and generative topology optimization for the design and lifecycle management of construction hoist counterbalance systems. Building upon an industrially validated redesign of a conventional brick counterweight system with a building-integrated structural arrangement, we synthesize current research across structural monitoring, computational mechanics, and optimization to establish a coherent methodology. The framework demonstrates how classical mechanical design principles can be enhanced through continuous structural assessment and computational material redistribution to achieve simultaneously optimized structures that are both mechanically efficient and continuously monitored throughout their operational lifecycle. A comprehensive literature review of peer-reviewed studies establishes the maturity of individual technologies while identifying the comparative lack of integrated approaches specifically targeting construction lifting equipment. The proposed framework, validated against established engineering principles, provides a foundation for future experimental and numerical investigations into intelligent construction hoist systems that meet the emerging demands for sustainability, reliability, and predictive maintenance under Industry 4.0 paradigms.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Reyhan Raj Sahana, Mikita Parikh, Hemangini Bhatt
- Quelle
- Journal of Intelligent Decision Making and Information Science
- Publikation
- 2026-08-21
- Band / Ausgabe
- 3 / 8s
- Seiten
- 541-554
- ISSN / ISBN
- 3079-0875
- Zitationen
- 0 laut Crossref
- Referenzen
- 0 hinterlegt
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Zitierfähiger Nachweis
Reyhan Raj Sahana, Mikita Parikh, Hemangini Bhatt (2026). Integrating Structural Health Monitoring and Generative Topology Optimization for Smart Construction Hoist Counterbalance Systems: A Literature-Based Engineering Framework. Journal of Intelligent Decision Making and Information Science, 3 (8s), 541-554. https://doi.org/10.59543/jidmis.v3.2079
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