Explainable Machine-Learning Model for Rapid Damage Assessment of CFST Columns after Close-In Explosion

IF 2.3 3区 工程技术 Q2 CONSTRUCTION & BUILDING TECHNOLOGY Journal of Performance of Constructed Facilities Pub Date : 2024-06-01 DOI:10.1061/jpcfev.cfeng-4592
Jie Li, Yanfen Pang, Kansheng Wang, Xuejie Zhang, Ning Wang
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用于近距离爆炸后 CFST 柱快速损坏评估的可解释机器学习模型
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来源期刊
CiteScore
5.10
自引率
4.00%
发文量
99
审稿时长
6 months
期刊介绍: The Journal of Performance of Constructed Facilities attempts to improve the quality of the constructed product through interdisciplinary communication. Papers examine the causes and consequences of failures and other performance problems. The principal purpose of the Journal is to disseminate information on failures and performance deficiencies of constructed facilities. The term “failures” in this context may mean catastrophic events, but also includes any performance deficiency from which a significant lesson can be learned, such as serviceability problems. Both the technical causes and procedural causes of failures are of interest. Procedural causes include human errors in design, construction, and/or operation that allow the failures to occur. The Journal welcomes manuscripts that deal with failures, methods of investigation of failures, special techniques for failure investigations, reconstruction and repair, and issues of ethics.
期刊最新文献
Explainable Machine-Learning Model for Rapid Damage Assessment of CFST Columns after Close-In Explosion Using Nonlinear-Elastic Stiffness at Small Displacement to Identify Damage in a Full-Scale Reinforced Concrete Building Discussion of “Case Study of Ground Subsidence Caused by the Drying Effect of a Group of Australian Native Eucalypts” Closure to “Case Study of Ground Subsidence Caused by the Drying Effect of a Group of Australian Native Eucalypts” Application of Multidisciplinary Community Resilience Modeling to Reduce Disaster Risk: Building Back Better
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