受海洋环境中氯化物诱发腐蚀影响的跨海大桥桥墩的生命周期抗震性预测

IF 5.7 1区 工程技术 Q1 ENGINEERING, CIVIL Structural Safety Pub Date : 2024-08-28 DOI:10.1016/j.strusafe.2024.102523
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引用次数: 0

摘要

跨海公路桥梁的生命周期抗震评估在指导桥梁的长期运营、维护和修复决策方面起着至关重要的作用。由于海洋环境本身具有随机性,在评估桥梁抗震性的同时,还要考虑其整个使用寿命期间所有可能出现的环境情况,这就需要大量的计算工作,并带来了实际挑战。因此,本研究开发了一个用于预测跨海公路桥梁生命周期抗震性的三阶段框架。研究开发了海洋环境条件和桥梁耐久性的随机模型,并利用实验测量数据进行了验证。采用了改进的良好网格点局部分层抽样(GLP-PSS)方法来生成数量有限的统一样本。我们选择了一座典型的预应力混凝土跨海公路桥作为基准桥梁,并在 OpenSees 平台上使用 460 个具有代表性的环境参数样本建立了参数化数值模型。利用环境模型和材料特性,对桥梁的使用寿命进行耐久性预测。利用 120 个真实地面运动记录对每个桥梁模型进行非线性时间历程分析,从而确定不同时间间隔内的地震需求、承载能力和系统脆性的变化。随后,分别利用基于响应面法(RSM)和人工神经网络(ANN)的代用模型对桥梁的生命周期抗震能力进行预测。最后,深入讨论了抗震性随时间变化的概率特征。结果表明,人工神经网络在预测生命周期抗震能力方面具有更高的泛化能力。只关注平均抗震能力在使用时间内的变化可能会导致低估桥梁的抗震能力,因为它可能会忽略其分布的尾部,从而可能导致高估桥梁的抗震能力。此外,全球变暖可能会加速复原力的下降。
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Life-cycle seismic resilience prediction of sea-crossing bridge piers exposed to chloride-induced corrosion in marine environments

The life-cycle seismic resilience assessment of sea-crossing highway bridges plays a crucial role in guiding decisions for their long-term operation, maintenance, and rehabilitation. Due to the inherently stochastic nature of marine environments, evaluating the resilience of bridges while considering all possible environmental scenarios throughout their service life necessitates substantial computational efforts and presents practical challenges. Thus, this study develops a three-stage framework for predicting the life-cycle seismic resilience of sea-crossing highway bridges. Stochastic models for marine environmental conditions and bridge durability are developed and validated using experimental measurement data. A modified Good Lattice Point-Partially Stratified Sampling (GLP-PSS) method is employed to generate a uniform and limited number of samples. A typical prestressed concrete sea-crossing highway bridge is selected as the benchmark bridge, and parameterized numerical models are established using 460 representative environmental parameter samples on the OpenSees platform. Leveraging the environmental model and material properties, the durability of the bridge is predicted over its service life. Nonlinear time history analyses are carried out for each bridge model using 120 real ground motion records, which allow the identification of variations in seismic demands, capacities, and system fragilities at different time intervals. Subsequently, the life-cycle seismic resilience of the bridge is predicted utilizing surrogate models based on the response surface method (RSM) and artificial neural networks (ANN), respectively. Finally, the time-dependent probabilistic characteristics of seismic resilience are thoroughly discussed. Results indicate that ANN demonstrates a higher degree of generalization capability in predicting the life-cycle seismic resilience. Focusing solely on changes in mean resilience over the service time may lead to an underestimation of bridge resilience, as it may ignore the tails of its distribution, potentially resulting in an overestimation of bridge resilience. Furthermore, global warming may expedite the decline in resilience.

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来源期刊
Structural Safety
Structural Safety 工程技术-工程:土木
CiteScore
11.30
自引率
8.60%
发文量
67
审稿时长
53 days
期刊介绍: Structural Safety is an international journal devoted to integrated risk assessment for a wide range of constructed facilities such as buildings, bridges, earth structures, offshore facilities, dams, lifelines and nuclear structural systems. Its purpose is to foster communication about risk and reliability among technical disciplines involved in design and construction, and to enhance the use of risk management in the constructed environment
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