Xin Guo

Dr. Xin Guo

School of civil engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China
orcid

Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 April 2025 in Reliability Engineering & System Safety
Verified
Journal Article
Closed access
Xin GuoXin Guo
Xin Guo
HLHongnan Li
Hongnan Li
HZHao Zhang
Hao Zhang

24 January 2024 in Journal of Constructional Steel Research
Verified
Journal Article
Closed access
Xin GuoXin Guo
Xin Guo
HZHao Zhang
Hao Zhang
HLHongnan Li
Hongnan Li
XYXiaoling Yuan
Xiaoling Yuan
YDYiming Ding
Yiming Ding


30 August 2023 in Applied Sciences
Verified
Journal Article
Open access
PDF file
Xin GuoXin Guo
Xin Guo
Hong-Nan LiHong-Nan Li
Hong-Nan Li
HZHao Zhang
Hao Zhang
Qi WangQi Wang
Qi Wang
JXJiran Xu
Jiran Xu
Probabilistic seismic hazard analysis (PSHA) has been recognized as a reasonable method for quantifying seismic threats. Traditionally, this method ignores the effect of the focal depth, in which the ground motion prediction equations (GMPEs) are applied to estimate the probability distribution associated with the possible motion levels induced by the site earthquakes, but it is limited by the unclear geological conditions, which makes it difficult to provide a uniform equation, and these equations cannot express the non-linear relationship under geological conditions. Hence, this paper proposed a method to consider the seismic focal depth for the PSHA with the example of California and used a back propagation neural network (BPNN) to predict the peak ground acceleration (PGA) instead of the GMPEs. Firstly, the measured PGA and unknown PGA seismic data applicable to this method were collected separately. Secondly, the unknown PGA data were supplemented by applying the BPNN based on the measured PGA data. Lastly, based on the full-probability equation, PSHA considering the focal depth was completed and compared with the current California seismic zoning results. The results showed that using the BPNN in the PSHA can ensure computational accuracy and universality, making it more suitable for regions with unclear geological structures and providing the possibility of adding other parameters to be considered for the influence of the PSHA.

8 February 2023 in SCIENTIA SINICA Technologica
Verified
Journal Article
Closed access
HLHongNan LI
HongNan LI
Xin GuoXin Guo
Xin Guo
HZHao Zhang
Hao Zhang
WTWei Tian
Wei Tian
随着对工程结构可持续发展要求的提高,多灾害作用下的工程结构研究越来越受到学者们和工程师的共同关注。强风和地震作为两种主要灾害荷载,目前仍不确定同时发生是否为偶然事件?为了明确两种灾害同时发生的可能性,本文分别从强风引发地震和地震引起强风两个方面,总结了目前强风和地震能够同时发生的研究现状,从多方面因素搜集了部分地区其同时发生的数据,为强风和地震同时发生这一耦合现象提供了数据支撑,评述了目前强风和地震同时作用下工程结构的相关研究进展。结果表明:①强风能够通过影响海浪和地质构造引发微震、慢地震和普通地震,地震能够通过引起大气温度和电磁波变化而引发强风,强风和地震之间存在耦合现象;②不同地区强风和地震同时发生的概率区别较大,通过风速曲面拟合,能够更好的满足搜集强风和地震同时发生数据的时间和空间关系;③强风和地震同时作用下的工程结构更易出现损伤和破坏,应调整结构设计标准或安装耗能减震装置来提高其性能。因此,仍需要完善强风和地震同时发生的相关数据及其作用机理,综合考虑强风和地震同时作用下对工程结构的影响是今后本领域的重要研究方向。

13 April 2025 in Applied Sciences
Verified
Journal Article
Open access
PDF file
Qi WangQi Wang
Qi Wang
LZLipeng Zhang
Lipeng Zhang
Hong-Nan LiHong-Nan Li
Hong-Nan Li
Xin GuoXin Guo
Xin Guo
Originally proposed by the authors, the spring pendulum pounding-tuned mass damper (SPPTMD)—a novel nonlinear damping system comprising a spring pendulum (SP) and motion limiter that dissipates energy through spring resonance amplification and controlled mass-limiter impacts—was theoretically validated for structural vibration control. To experimentally verify its efficacy, a two-story, lightly damped steel frame was subjected to sinusoidal excitation and historical earthquake excitations under both uncontrolled and SPPTMD-controlled conditions. The results demonstrated (1) significant vibration attenuation through SPPTMD implementation and (2) enhanced control effectiveness in soft soil environments compared to stiff soil conditions. Additionally, a numerical model of the SPPTMD–structure system was developed, with computational results showing excellent correlation to experimental data, thereby confirming modeling accuracy.