气候变化领域集成服务门户(CCPortal): Integrating physics-informed machine learning with resonance effect for structural dynamic performance modeling

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DOI	10.1016/j.jobe.2024.108627
	Integrating physics-informed machine learning with resonance effect for structural dynamic performance modeling
	Zhang, Jiaxin; Lei, Xiaoming; Chan, Pak-wai; Dong, You
发表日期	2024
EISSN	2352-7102
起始页码	84
卷号	84
英文摘要	Accurate prediction the dynamic response of structures under various loads is a complex and challenging task due to the uncertainties and intricacies of loading scenarios and analysis methods. Traditional approaches to structural analysis and modeling often rely on physics-based simulations, which can be computationally expensive and time-consuming. Over the years, Machine Learning (ML) methods have proven to be a formidable tool for data-driven modeling, based on in-situ monitoring data. However, these ML models are generally purely data-driven, lacking consideration for relevant physical information such as dynamic characteristics. Hence, this paper proposes a Physics-Informed Machine Learning (PIML) approach for modeling structural dynamic performance. This method embeds resonance effect, a crucial piece of physical information in real-world engineering, into the design of the multilayer perceptron (MLP) loss function. The model is validated through numerical simulations of forced vibrations in a singledegree-of-freedom system and a vehicle-bridge coupled system. The results indicate that the proposed PIML can model the system dynamic response effectively and simulate resonance effect at key frequency-domain locations accurately. Subsequently, features are extracted from the realworld monitoring data of a high-rise building dynamic response during Typhoon Hato, and PIML is applied for training. The results reveal that PIML significantly outperforms pure ML algorithms, even with a small dataset, and can effectively capture resonance effect in wind-induced vibrations. Using a well-trained PIML, a parameter analysis is conducted, comparing the dynamic performance under different wind speeds and frequency conditions. Lastly, considering the variation in wind parameters due to climate change, PIML is employed to analyze long-term structural fragility. The findings indicate significant changes in fragility from 1985 to 2045. In conclusion, the proposed PIML algorithm offers several advantages over conventional methods, such as reduced computational costs, improved accuracy, and the ability to capture complex nonlinear behaviors and resonance effect of interest, and efficiently serve in analyses of structural reliability and fragility.
英文关键词	Structural health monitoring; Physics -informed machine learning (PIML); Resonance effect; Response prediction; Climate change; Fragility analysis
语种	英语
WOS研究方向	Construction & Building Technology ; Engineering
WOS类目	Construction & Building Technology ; Engineering, Civil
WOS记录号	WOS:001180952100001
来源期刊	JOURNAL OF BUILDING ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/290956
作者单位	Hong Kong Polytechnic University
推荐引用方式 GB/T 7714	Zhang, Jiaxin,Lei, Xiaoming,Chan, Pak-wai,et al. Integrating physics-informed machine learning with resonance effect for structural dynamic performance modeling[J],2024,84.
APA	Zhang, Jiaxin,Lei, Xiaoming,Chan, Pak-wai,&Dong, You.(2024).Integrating physics-informed machine learning with resonance effect for structural dynamic performance modeling.JOURNAL OF BUILDING ENGINEERING,84.
MLA	Zhang, Jiaxin,et al."Integrating physics-informed machine learning with resonance effect for structural dynamic performance modeling".JOURNAL OF BUILDING ENGINEERING 84(2024).