{"title":"Reviewer's Recognition","authors":"","doi":"10.1115/1.4064645","DOIUrl":"https://doi.org/10.1115/1.4064645","url":null,"abstract":"","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reviewer's Recognition","authors":"","doi":"10.1115/1.4064645","DOIUrl":"https://doi.org/10.1115/1.4064645","url":null,"abstract":"","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Within the realm of structural reliability analysis, the uncertainties tied to resistance and loads are conventionally embodied as random variables possessing established cumulative distribution functions (CDFs). Nevertheless, real-world scenarios often involve cases where the CDFs of random variables are unknown, necessitating the probabilistic traits of these variables solely through statistical moments. In this study, for the purpose of integrating random variables characterized by an unknown CDF into the framework of Monte Carlo simulation (MCS), a linear moments (L-moments)-based method is proposed. The random variables marked by an unknown CDF are rendered as a straightforward function of a standard normal random variable, and the formulation of this function is determined by utilizing the L-moments, which are typically attainable from the statistical data of the random variables. By employing the proposed approach, the generation of random numbers associated with variables with unknown CDFs becomes a straightforward process, utilizing those derived from a standard normal random variable constructed by using Box-Muller transform. A selection of illustrative examples is presented, in which the efficacy of the technique is scrutinized. This examination reveals that despite its simplicity, the method demonstrates a level of precision that qualifies it for incorporating random variables characterized by unspecified CDFs within the framework of MCS for purposes of structural reliability analysis.
{"title":"Linear Moments-based Monte Carlo Simulation for Reliability Analysis with Unknown Probability Distributions","authors":"Long-Wen Zhang, Yan-Gang Zhao","doi":"10.1115/1.4064702","DOIUrl":"https://doi.org/10.1115/1.4064702","url":null,"abstract":"\u0000 Within the realm of structural reliability analysis, the uncertainties tied to resistance and loads are conventionally embodied as random variables possessing established cumulative distribution functions (CDFs). Nevertheless, real-world scenarios often involve cases where the CDFs of random variables are unknown, necessitating the probabilistic traits of these variables solely through statistical moments. In this study, for the purpose of integrating random variables characterized by an unknown CDF into the framework of Monte Carlo simulation (MCS), a linear moments (L-moments)-based method is proposed. The random variables marked by an unknown CDF are rendered as a straightforward function of a standard normal random variable, and the formulation of this function is determined by utilizing the L-moments, which are typically attainable from the statistical data of the random variables. By employing the proposed approach, the generation of random numbers associated with variables with unknown CDFs becomes a straightforward process, utilizing those derived from a standard normal random variable constructed by using Box-Muller transform. A selection of illustrative examples is presented, in which the efficacy of the technique is scrutinized. This examination reveals that despite its simplicity, the method demonstrates a level of precision that qualifies it for incorporating random variables characterized by unspecified CDFs within the framework of MCS for purposes of structural reliability analysis.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Within the realm of structural reliability analysis, the uncertainties tied to resistance and loads are conventionally embodied as random variables possessing established cumulative distribution functions (CDFs). Nevertheless, real-world scenarios often involve cases where the CDFs of random variables are unknown, necessitating the probabilistic traits of these variables solely through statistical moments. In this study, for the purpose of integrating random variables characterized by an unknown CDF into the framework of Monte Carlo simulation (MCS), a linear moments (L-moments)-based method is proposed. The random variables marked by an unknown CDF are rendered as a straightforward function of a standard normal random variable, and the formulation of this function is determined by utilizing the L-moments, which are typically attainable from the statistical data of the random variables. By employing the proposed approach, the generation of random numbers associated with variables with unknown CDFs becomes a straightforward process, utilizing those derived from a standard normal random variable constructed by using Box-Muller transform. A selection of illustrative examples is presented, in which the efficacy of the technique is scrutinized. This examination reveals that despite its simplicity, the method demonstrates a level of precision that qualifies it for incorporating random variables characterized by unspecified CDFs within the framework of MCS for purposes of structural reliability analysis.
{"title":"Linear Moments-based Monte Carlo Simulation for Reliability Analysis with Unknown Probability Distributions","authors":"Long-Wen Zhang, Yan-Gang Zhao","doi":"10.1115/1.4064702","DOIUrl":"https://doi.org/10.1115/1.4064702","url":null,"abstract":"\u0000 Within the realm of structural reliability analysis, the uncertainties tied to resistance and loads are conventionally embodied as random variables possessing established cumulative distribution functions (CDFs). Nevertheless, real-world scenarios often involve cases where the CDFs of random variables are unknown, necessitating the probabilistic traits of these variables solely through statistical moments. In this study, for the purpose of integrating random variables characterized by an unknown CDF into the framework of Monte Carlo simulation (MCS), a linear moments (L-moments)-based method is proposed. The random variables marked by an unknown CDF are rendered as a straightforward function of a standard normal random variable, and the formulation of this function is determined by utilizing the L-moments, which are typically attainable from the statistical data of the random variables. By employing the proposed approach, the generation of random numbers associated with variables with unknown CDFs becomes a straightforward process, utilizing those derived from a standard normal random variable constructed by using Box-Muller transform. A selection of illustrative examples is presented, in which the efficacy of the technique is scrutinized. This examination reveals that despite its simplicity, the method demonstrates a level of precision that qualifies it for incorporating random variables characterized by unspecified CDFs within the framework of MCS for purposes of structural reliability analysis.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lixiang Cheng, Yan-Gang Zhao, Pei-Pei Li, Lewei Yan
The krill herd (KH) algorithm is widely used for optimizing truss structures as no gradient information is necessary, and only a few parameters require adjustment. However, when the truss structure becomes discrete and complex, KH tends to fall into a local optimum. Therefore, a novel target-oriented KH (TOKH) algorithm is proposed in this study to optimize the design of discrete truss structures. Initially, a crossover operator is established between the "best krill" and "suboptimal krill" to generate a robust "cross krill" for global exploration. Additionally, an improved local mutation and crossover (ILMC) operator is introduced to fine-tune the "center of food" and candidate solutions for local exploitation. The proposed method and other optimization approaches are experimentally compared considering 15 benchmark functions. Then, the performance of the TOKH algorithm is evaluated based on four discrete truss structure optimization problems under multiple loading conditions. The obtained optimization results indicate that the proposed method presents competitive solutions in terms of accuracy, unlike other algorithms in the literature, and avoids falling into a local minimum.
{"title":"Sizing and Shape Optimization of Discrete Truss Employing a Target-oriented Krill Herd Algorithm","authors":"Lixiang Cheng, Yan-Gang Zhao, Pei-Pei Li, Lewei Yan","doi":"10.1115/1.4064644","DOIUrl":"https://doi.org/10.1115/1.4064644","url":null,"abstract":"\u0000 The krill herd (KH) algorithm is widely used for optimizing truss structures as no gradient information is necessary, and only a few parameters require adjustment. However, when the truss structure becomes discrete and complex, KH tends to fall into a local optimum. Therefore, a novel target-oriented KH (TOKH) algorithm is proposed in this study to optimize the design of discrete truss structures. Initially, a crossover operator is established between the \"best krill\" and \"suboptimal krill\" to generate a robust \"cross krill\" for global exploration. Additionally, an improved local mutation and crossover (ILMC) operator is introduced to fine-tune the \"center of food\" and candidate solutions for local exploitation. The proposed method and other optimization approaches are experimentally compared considering 15 benchmark functions. Then, the performance of the TOKH algorithm is evaluated based on four discrete truss structure optimization problems under multiple loading conditions. The obtained optimization results indicate that the proposed method presents competitive solutions in terms of accuracy, unlike other algorithms in the literature, and avoids falling into a local minimum.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139683283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous growth of transportation demands, in-service highway bridges face greater challenges in their long-term operational lifespans, and bridge collapse accidents caused by vehicle overloading occur from time to time. Additionally, under the influence of loads and environmental factors, various wear patterns inevitably lead to degraded reinforced concrete bridges. In view of this problem, it is reasonable and feasible to limit the vehicle loads passing over highway bridges, and the key basis for limitation is determining the load limit value of a bridge. Based on a classification of vehicle types, this paper explores the load parameters of several heavy-duty vehicles with large traffic volumes through traffic flow information and summarizes the load spectra of typical heavy-duty vehicles. On the basis of the first-order second-moment method of structural reliability theory, a theory of bridge load limit value is proposed. Given the structural target reliability index, the theoretical load limit value of a bridge can be calculated. To ensure the rationality of the theory of bridge load limit value, by relying on the engineering example of a variable-section continuous girder bridge, the theoretical load limit value is calculated. By comparing actual bridge load test data with the finite element model results, the rationality of the bridge load limiting theory is verified. Finally, the paper notes that it is safer and more reliable to define the load limit value according to the bending stress state for bridges.
{"title":"Theory and Empirical Analysis of Bridge Load Limitation Under the Action of Typical Heavy-Duty Vehicles","authors":"Qingfei Gao, Haonan Jiang, Haoran Wang, Binqiang Guo, Zaiyang Jiang, Chuan Wang","doi":"10.1115/1.4064643","DOIUrl":"https://doi.org/10.1115/1.4064643","url":null,"abstract":"\u0000 With the continuous growth of transportation demands, in-service highway bridges face greater challenges in their long-term operational lifespans, and bridge collapse accidents caused by vehicle overloading occur from time to time. Additionally, under the influence of loads and environmental factors, various wear patterns inevitably lead to degraded reinforced concrete bridges. In view of this problem, it is reasonable and feasible to limit the vehicle loads passing over highway bridges, and the key basis for limitation is determining the load limit value of a bridge. Based on a classification of vehicle types, this paper explores the load parameters of several heavy-duty vehicles with large traffic volumes through traffic flow information and summarizes the load spectra of typical heavy-duty vehicles. On the basis of the first-order second-moment method of structural reliability theory, a theory of bridge load limit value is proposed. Given the structural target reliability index, the theoretical load limit value of a bridge can be calculated. To ensure the rationality of the theory of bridge load limit value, by relying on the engineering example of a variable-section continuous girder bridge, the theoretical load limit value is calculated. By comparing actual bridge load test data with the finite element model results, the rationality of the bridge load limiting theory is verified. Finally, the paper notes that it is safer and more reliable to define the load limit value according to the bending stress state for bridges.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianhong Liang, Li-Ping Wang, Guang Yu, Jun Wu, Dong Wang, Lin Song
Spindle rotation error directly correlates with the quality of mechanical processing. Currently, the error was mainly converted through measuring the distance information of standard component installed at the tool position, and it can't complete the normal machining because the tool is occupied. Therefore, a novel self-adaptive supervised learning method through easy-collected vibration signal that don't affect the machining to indirect predict the error. This method includes three steps: Firstly, the original vibration signal is decomposed by LMD method to obtain two critical components; Subsequently, the two components are fused as a signal by a weighted-average approach; Finally, the fused signal and corresponding error are self-adaptive supervised trained by the setting termination condition to modify fusion coefficient and network parameters. The method is used to analyze the data-set of spindle platform, which has collected the experimental data at speeds 1000, 2000, 3000, and 4000 more than 170 groups, and the indirect prediction accuracy reached 94.12%, 92.35%, 97.68% and 90.59% respectively. Additionally, the experimental results were compared and demonstrated by three aspects with current different algorithms.
{"title":"Indirect Prediction of Spindle Rotation Error Through Vibration Signal Based On Supervised Local Mean Decomposition Filter Fusion and Bi-LSTM Classification Network","authors":"Jianhong Liang, Li-Ping Wang, Guang Yu, Jun Wu, Dong Wang, Lin Song","doi":"10.1115/1.4064642","DOIUrl":"https://doi.org/10.1115/1.4064642","url":null,"abstract":"\u0000 Spindle rotation error directly correlates with the quality of mechanical processing. Currently, the error was mainly converted through measuring the distance information of standard component installed at the tool position, and it can't complete the normal machining because the tool is occupied. Therefore, a novel self-adaptive supervised learning method through easy-collected vibration signal that don't affect the machining to indirect predict the error. This method includes three steps: Firstly, the original vibration signal is decomposed by LMD method to obtain two critical components; Subsequently, the two components are fused as a signal by a weighted-average approach; Finally, the fused signal and corresponding error are self-adaptive supervised trained by the setting termination condition to modify fusion coefficient and network parameters. The method is used to analyze the data-set of spindle platform, which has collected the experimental data at speeds 1000, 2000, 3000, and 4000 more than 170 groups, and the indirect prediction accuracy reached 94.12%, 92.35%, 97.68% and 90.59% respectively. Additionally, the experimental results were compared and demonstrated by three aspects with current different algorithms.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139867965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous growth of transportation demands, in-service highway bridges face greater challenges in their long-term operational lifespans, and bridge collapse accidents caused by vehicle overloading occur from time to time. Additionally, under the influence of loads and environmental factors, various wear patterns inevitably lead to degraded reinforced concrete bridges. In view of this problem, it is reasonable and feasible to limit the vehicle loads passing over highway bridges, and the key basis for limitation is determining the load limit value of a bridge. Based on a classification of vehicle types, this paper explores the load parameters of several heavy-duty vehicles with large traffic volumes through traffic flow information and summarizes the load spectra of typical heavy-duty vehicles. On the basis of the first-order second-moment method of structural reliability theory, a theory of bridge load limit value is proposed. Given the structural target reliability index, the theoretical load limit value of a bridge can be calculated. To ensure the rationality of the theory of bridge load limit value, by relying on the engineering example of a variable-section continuous girder bridge, the theoretical load limit value is calculated. By comparing actual bridge load test data with the finite element model results, the rationality of the bridge load limiting theory is verified. Finally, the paper notes that it is safer and more reliable to define the load limit value according to the bending stress state for bridges.
{"title":"Theory and Empirical Analysis of Bridge Load Limitation Under the Action of Typical Heavy-Duty Vehicles","authors":"Qingfei Gao, Haonan Jiang, Haoran Wang, Binqiang Guo, Zaiyang Jiang, Chuan Wang","doi":"10.1115/1.4064643","DOIUrl":"https://doi.org/10.1115/1.4064643","url":null,"abstract":"\u0000 With the continuous growth of transportation demands, in-service highway bridges face greater challenges in their long-term operational lifespans, and bridge collapse accidents caused by vehicle overloading occur from time to time. Additionally, under the influence of loads and environmental factors, various wear patterns inevitably lead to degraded reinforced concrete bridges. In view of this problem, it is reasonable and feasible to limit the vehicle loads passing over highway bridges, and the key basis for limitation is determining the load limit value of a bridge. Based on a classification of vehicle types, this paper explores the load parameters of several heavy-duty vehicles with large traffic volumes through traffic flow information and summarizes the load spectra of typical heavy-duty vehicles. On the basis of the first-order second-moment method of structural reliability theory, a theory of bridge load limit value is proposed. Given the structural target reliability index, the theoretical load limit value of a bridge can be calculated. To ensure the rationality of the theory of bridge load limit value, by relying on the engineering example of a variable-section continuous girder bridge, the theoretical load limit value is calculated. By comparing actual bridge load test data with the finite element model results, the rationality of the bridge load limiting theory is verified. Finally, the paper notes that it is safer and more reliable to define the load limit value according to the bending stress state for bridges.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianhong Liang, Li-Ping Wang, Guang Yu, Jun Wu, Dong Wang, Lin Song
Spindle rotation error directly correlates with the quality of mechanical processing. Currently, the error was mainly converted through measuring the distance information of standard component installed at the tool position, and it can't complete the normal machining because the tool is occupied. Therefore, a novel self-adaptive supervised learning method through easy-collected vibration signal that don't affect the machining to indirect predict the error. This method includes three steps: Firstly, the original vibration signal is decomposed by LMD method to obtain two critical components; Subsequently, the two components are fused as a signal by a weighted-average approach; Finally, the fused signal and corresponding error are self-adaptive supervised trained by the setting termination condition to modify fusion coefficient and network parameters. The method is used to analyze the data-set of spindle platform, which has collected the experimental data at speeds 1000, 2000, 3000, and 4000 more than 170 groups, and the indirect prediction accuracy reached 94.12%, 92.35%, 97.68% and 90.59% respectively. Additionally, the experimental results were compared and demonstrated by three aspects with current different algorithms.
{"title":"Indirect Prediction of Spindle Rotation Error Through Vibration Signal Based On Supervised Local Mean Decomposition Filter Fusion and Bi-LSTM Classification Network","authors":"Jianhong Liang, Li-Ping Wang, Guang Yu, Jun Wu, Dong Wang, Lin Song","doi":"10.1115/1.4064642","DOIUrl":"https://doi.org/10.1115/1.4064642","url":null,"abstract":"\u0000 Spindle rotation error directly correlates with the quality of mechanical processing. Currently, the error was mainly converted through measuring the distance information of standard component installed at the tool position, and it can't complete the normal machining because the tool is occupied. Therefore, a novel self-adaptive supervised learning method through easy-collected vibration signal that don't affect the machining to indirect predict the error. This method includes three steps: Firstly, the original vibration signal is decomposed by LMD method to obtain two critical components; Subsequently, the two components are fused as a signal by a weighted-average approach; Finally, the fused signal and corresponding error are self-adaptive supervised trained by the setting termination condition to modify fusion coefficient and network parameters. The method is used to analyze the data-set of spindle platform, which has collected the experimental data at speeds 1000, 2000, 3000, and 4000 more than 170 groups, and the indirect prediction accuracy reached 94.12%, 92.35%, 97.68% and 90.59% respectively. Additionally, the experimental results were compared and demonstrated by three aspects with current different algorithms.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advantages of a small footprint, wide under-bridge view, and beautiful appearance, single-column pier bridges are widely used in urban bridge networks. However, single-column pier bridges are prone to damage during earthquakes or heavy vehicle use, which can seriously affect normal operations and post-disaster recoveries. Therefore, there is an urgent need to carry out the seismic resilience assessment of single-column pier bridges and formulate disaster prevention and mitigation measures from the aspects of design, maintenance, and post-earthquake recovery. This paper first establishes a resilience assessment framework for the single-column pier bridge and optimizes a functionality recovery model after an earthquake. Then, a numerical model of a sample bridge is built for resilience fragility analysis. Nonlinear dynamic time history analysis is performed to build a probabilistic seismic demand model, and moment-curvature analysis is performed to build a probabilistic seismic capacity model. Finally, a seismic resilience assessment of the single-column pier bridge is obtained based on the seismic fragility, and a sensitivity analysis is carried out for the pier height, pier section dimension, span and vehicle load level to improve the resilience of the single-column pier bridge.
{"title":"Fragility Analysis and Resilience Assessment of the Single-Column Pier Steel-Concrete Composite Bridge Subjected to Seismic Loads","authors":"Tong Wang, Q. Gao, Yidian Dong, Hao Xu, Yang Liu","doi":"10.1115/1.4064647","DOIUrl":"https://doi.org/10.1115/1.4064647","url":null,"abstract":"\u0000 With the advantages of a small footprint, wide under-bridge view, and beautiful appearance, single-column pier bridges are widely used in urban bridge networks. However, single-column pier bridges are prone to damage during earthquakes or heavy vehicle use, which can seriously affect normal operations and post-disaster recoveries. Therefore, there is an urgent need to carry out the seismic resilience assessment of single-column pier bridges and formulate disaster prevention and mitigation measures from the aspects of design, maintenance, and post-earthquake recovery. This paper first establishes a resilience assessment framework for the single-column pier bridge and optimizes a functionality recovery model after an earthquake. Then, a numerical model of a sample bridge is built for resilience fragility analysis. Nonlinear dynamic time history analysis is performed to build a probabilistic seismic demand model, and moment-curvature analysis is performed to build a probabilistic seismic capacity model. Finally, a seismic resilience assessment of the single-column pier bridge is obtained based on the seismic fragility, and a sensitivity analysis is carried out for the pier height, pier section dimension, span and vehicle load level to improve the resilience of the single-column pier bridge.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：