最新技术回顾:为复杂工业建筑结构开发的特殊同心支撑框架的抗震设计和性能评估

IF 1.1 4区 工程技术 Q3 CONSTRUCTION & BUILDING TECHNOLOGY International Journal of Steel Structures Pub Date : 2024-02-19 DOI:10.1007/s13296-024-00815-w
Adane Demeke Wasse, Kaoshan Dai, Jianze Wang, Reza Sharbati
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引用次数: 0

摘要

这本最新的综述全面评估了同心支撑框架 (CBF) 系统的抗震设计和性能评估,尤其侧重于特殊同心支撑框架 (SCBF)。同心支撑框架在为各类建筑(包括住宅、商业和工业建筑)提供抗震性能方面表现出了卓越的功效。然而,必须承认的是,自然灾害可能会导致重大的人员伤亡、经济损失、社会混乱和工业设施损坏。因此,本综述主要关注为复杂工业建筑开发的 SCBF 的抗震设计和性能评估。尽管在 SCBF 性能评估方面开展了大量研究工作,但在研究不规则和复杂工业结构中的 SCBF 方面,仍存在明显的综合评论空白。因此,有必要找出这一研究空白,并结合最新进展,特别是人工智能(AI)技术的整合,开展最新的综述。本研究的主要目标是评估现有的研究工作,并确定需要进一步探索的领域。此外,强烈建议采用机器学习 (ML) 技术等人工智能方法来提高 SCBF 的性能,并有效识别严重地震后受损的结构。本综述指出了在这一特定领域开展进一步研究的必要性。通过填补这些研究空白并利用人工智能的进步,可以增强工业建筑的抗灾能力,从而减轻地震事件造成的损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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State-of-the-Art Review: Seismic Design and Performance Assessment of Special Concentrically Braced Frames Developed for Complex Industrial Building Structures

This state-of-the-art review comprehensively evaluates the seismic design and performance assessment of concentrically braced frame (CBF) systems, specifically focusing on special concentrically braced frames (SCBFs). SCBFs have shown remarkable effectiveness in providing seismic resistance for various building types, including residential, commercial, and industrial structures. However, it is crucial to acknowledge that natural disasters can lead to significant losses in human lives, economic impact, social disruption, and damage to industrial facilities. Therefore, this review concentrates on the seismic design and performance assessment of SCBFs developed for complex industrial buildings. Despite significant research efforts in SCBF performance assessment, there remains a notable gap in comprehensive critical reviews focused on studying SCBFs in the context of irregular and complex industrial structures. Identifying this research gap and conducting an updated review incorporating recent advancements, particularly the integration of Artificial Intelligence (AI) techniques, becomes necessary. The major goal of this study is to assess existing research efforts and identify areas that need further inquiry. Furthermore, AI methods, such as Machine Learning (ML) techniques, are highly recommended to enhance the performance of SCBFs and effectively identify damaged structures after severe earthquakes. The review identifies the need for further investigation in this specific area. By addressing these research gaps and leveraging AI advancements, the resilience of industrial buildings can be enhanced, thereby mitigating the losses resulting from seismic events.

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来源期刊
International Journal of Steel Structures
International Journal of Steel Structures 工程技术-工程:土木
CiteScore
2.70
自引率
13.30%
发文量
122
审稿时长
12 months
期刊介绍: The International Journal of Steel Structures provides an international forum for a broad classification of technical papers in steel structural research and its applications. The journal aims to reach not only researchers, but also practicing engineers. Coverage encompasses such topics as stability, fatigue, non-linear behavior, dynamics, reliability, fire, design codes, computer-aided analysis and design, optimization, expert systems, connections, fabrications, maintenance, bridges, off-shore structures, jetties, stadiums, transmission towers, marine vessels, storage tanks, pressure vessels, aerospace, and pipelines and more.
期刊最新文献
Global Response Reconstruction of a Full-Scale 3D Structure Model Using Limited Multi-Response Data Stress Concentration Around Cutouts in Spirally Welded Steel Columns Experimental Investigation of the Effect of Welding Parameters on Material Properties of SS 316L Stainless Steel Welded Joints Experimental and Analytical Study on the Seismic Performance of PEC T-Shaped Columns Assembled Frames with ALC Open-Hole Wall A Parametric Investigation on Ultra-low Cycle Fatigue Damage of Steel Bridge Piers Under Horizontal Bi-directional Seismic Excitations
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