Hongyuan Guo , Ruiwei Feng , You Dong , Paolo Gardoni
{"title":"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","authors":"Hongyuan Guo , Ruiwei Feng , You Dong , Paolo Gardoni","doi":"10.1016/j.strusafe.2024.102523","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"111 ","pages":"Article 102523"},"PeriodicalIF":5.7000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Life-cycle seismic resilience prediction of sea-crossing bridge piers exposed to chloride-induced corrosion in marine environments\",\"authors\":\"Hongyuan Guo , Ruiwei Feng , You Dong , Paolo Gardoni\",\"doi\":\"10.1016/j.strusafe.2024.102523\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":21978,\"journal\":{\"name\":\"Structural Safety\",\"volume\":\"111 \",\"pages\":\"Article 102523\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Safety\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167473024000948\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167473024000948","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
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.
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