摘要
近断层地震动严重威胁重要结构的安全,然而地震动波形复杂导致近断层地震动识别困难,经典Baker识别方法因采用小波技术,而存在母小波与潜在脉冲差异过大,致使识别产生误差。针对该问题,利用具有自适应噪声的完全集成经验模式分解(ICEEMDAN)对Baker方法进行改进,改进方法利用白噪声对信号分解过程进行辅助,从而增强了信号分析方法对地震动这类复杂信号的应对能力。为提高改进方法的识别效率和质量,对太平洋工程抗震中心数据库中的3 655条地震动进行识别。结果表明,由于ICEEMDAN技术的加入改进后的地震动识别方法共识别出192条近断层地震动,比经典Baker方法则识别出的163条近断层地震动多识别出17.79%的近断层地震动。为进一步探讨识别出的近断层地震动的近断层特性,从地震反应谱和地震损伤入手进行分析,两种方式获得近断层地震动反应谱长周期反应剧烈、峰值周期高,有明显的近断层特征;在地震损伤方面,以一典型钢管混凝土拱桥为案例,对两种方式识别出的近断层地震动及远场地震动进行地震响应计算。结果表明,两种方法获得的近断层地震动有着极为相似的地震损伤规律,且均较远场地震动的损伤有明显的增加。综上,两种方法识别出的近断层地震动均有明显的近断层特性,而基于ICEEMDAN技术的改进方法增加了17.79%的近断层地震动数量,将ICEEMDAN技术引入可以利用该技术较强的鲁棒性,从而对波形较为复杂的速度脉冲进行识别,进而在不降低近断层地震动识别质量的前提下,提升Baker识别方法的识别效率和准确性。
Near-fault ground motion represents an exceptionally hazardous seismic activity characterized by high-energy velocity pulses.These pulses rapidly input a substantial amount of seismic energy into structures,causing significant damage in a very short time.With the expanding urban and transportation infrastructures,an increasing number of large-scale structures find themselves within the proximity of near-fault zones,subjecting them to the impact of near-fault ground motions.This phenomenon significantly compromises the safety of crucial structures.Identify-ing near-fault ground motions is challenging due to the intricate waveforms of ground motion.Currently,Baker’s wavelet-based method is widely used for recognition.This method leverages the local focusing characteristics of wavelets to extract high-energy velocity pulses in near-fault ground motions.The velocity amplitude and energy extracted are then employed to determine whether the ground motion is near-fault.Baker’s method offers advantages such as rapid recognition and high accuracy.However,it relies on wavelet technology,leading to identification errors due to the significant difference between the mother wavelet and potential pulses.To address this limitation,an improved approach incorporates the use of Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN).This enhancement introduces white noise to assist in the signal decomposition process,enhancing the method's responsiveness to complex signals like ground motions.The en-hanced method eliminates the reliance on the basis function in wavelet technology,allowing for a more efficient extraction of velocity pulses with diverse waveforms in complex ground motions.Consequently,the extracted velocity pulse is more comprehensive,enhancing the overall recogni-tion efficiency of the near-fault ground motion recognition method.To enhance the efficiency and quality of this improved method,3655 ground motions from the Pacific Engineering Earthquake Resistance Center’s database we
作者
刘震
吕均琳
马兴亮
LIU Zhen;LU Junlin;MA Xingliang(School of Management Sci.and Eng.,Shandong Technol.and Business Univ.,Yantai 200215,China;School of Civil Eng.and Architecture,Changzhou Inst.of Technol.,Changzhou 213032,China)
出处
《工程科学与技术》
EI
CAS
CSCD
北大核心
2024年第2期217-227,共11页
Advanced Engineering Sciences
基金
国家自然科学基金项目(52208461)。
关键词
近断层地震动
识别方法
经验模态分解
小波分解
地震损伤
near fault ground motion
identification method
empirical mode decomposition
wavelet decomposition
earthquake damage
