A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Near-fault pulse seismic ductility spectra for bridge columns based on machine learning
https://orcid.org/0000-0002-5998-250X
Abstract The seismic ductility spectra (SDS) method is a crucial tool to quickly evaluate the ductility demand of bridge columns with varied constitutive models. However, conventional SDS methods usually do not consider the characteristics of pulse-like excitations, which tend to cause severe structural damage. To address this challenge, near-fault pulse seismic ductility spectra (NFPSDS) based on Machine learning (ML) are developed in this study, where two ML models, i.e., the Random Forest (RF) and the artificial neural network (ANN), are utilized to map the relationship between seismic demand and a pulse-structure coupled index α 1-p (the structural fundamental period (T 1) relative to pulse period (T p)). Then, the influence of column parameters (such as fundamental period and longitudinal reinforcement ratio) and pulse parameters (such as pulse period and peak pulse velocity) on NFPSDS is quantitatively investigated. Thus, by employing the NFPSDS method, the reasonable design range of longitudinal reinforcement ratio can be obtained under different pulse periods and pulse velocities. Overall, the NFPSDS method significantly benefits the practice for seismic design of structures in near-fault regions.
Highlights The near-fault pulse seismic ductility spectra method considering the characteristic of pulse-like excitations is proposed. The effect of some structural and pulse parameters on proposed spectra is quantitatively investigated. The proposed spectra is applied to obtain the suitable design range of structural parameters with the change of pulse period.
Near-fault pulse seismic ductility spectra for bridge columns based on machine learning
https://orcid.org/0000-0002-5998-250X
Abstract The seismic ductility spectra (SDS) method is a crucial tool to quickly evaluate the ductility demand of bridge columns with varied constitutive models. However, conventional SDS methods usually do not consider the characteristics of pulse-like excitations, which tend to cause severe structural damage. To address this challenge, near-fault pulse seismic ductility spectra (NFPSDS) based on Machine learning (ML) are developed in this study, where two ML models, i.e., the Random Forest (RF) and the artificial neural network (ANN), are utilized to map the relationship between seismic demand and a pulse-structure coupled index α 1-p (the structural fundamental period (T 1) relative to pulse period (T p)). Then, the influence of column parameters (such as fundamental period and longitudinal reinforcement ratio) and pulse parameters (such as pulse period and peak pulse velocity) on NFPSDS is quantitatively investigated. Thus, by employing the NFPSDS method, the reasonable design range of longitudinal reinforcement ratio can be obtained under different pulse periods and pulse velocities. Overall, the NFPSDS method significantly benefits the practice for seismic design of structures in near-fault regions.
Highlights The near-fault pulse seismic ductility spectra method considering the characteristic of pulse-like excitations is proposed. The effect of some structural and pulse parameters on proposed spectra is quantitatively investigated. The proposed spectra is applied to obtain the suitable design range of structural parameters with the change of pulse period.
Near-fault pulse seismic ductility spectra for bridge columns based on machine learning
https://orcid.org/0000-0002-5998-250X
Abstract The seismic ductility spectra (SDS) method is a crucial tool to quickly evaluate the ductility demand of bridge columns with varied constitutive models. However, conventional SDS methods usually do not consider the characteristics of pulse-like excitations, which tend to cause severe structural damage. To address this challenge, near-fault pulse seismic ductility spectra (NFPSDS) based on Machine learning (ML) are developed in this study, where two ML models, i.e., the Random Forest (RF) and the artificial neural network (ANN), are utilized to map the relationship between seismic demand and a pulse-structure coupled index α 1-p (the structural fundamental period (T 1) relative to pulse period (T p)). Then, the influence of column parameters (such as fundamental period and longitudinal reinforcement ratio) and pulse parameters (such as pulse period and peak pulse velocity) on NFPSDS is quantitatively investigated. Thus, by employing the NFPSDS method, the reasonable design range of longitudinal reinforcement ratio can be obtained under different pulse periods and pulse velocities. Overall, the NFPSDS method significantly benefits the practice for seismic design of structures in near-fault regions.
Highlights The near-fault pulse seismic ductility spectra method considering the characteristic of pulse-like excitations is proposed. The effect of some structural and pulse parameters on proposed spectra is quantitatively investigated. The proposed spectra is applied to obtain the suitable design range of structural parameters with the change of pulse period.
Near-fault pulse seismic ductility spectra for bridge columns based on machine learning
Yang, Tao (author) / Yuan, Xinzhe (author) / Zhong, Jian (author) / Yuan, Wancheng (author)
2022-10-06
Article (Journal)
Electronic Resource
English
Safety of ductility demand based seismic design for bridge columns
| British Library Conference Proceedings | 2008
Pulse-Like Near-Fault Ground Motion Effects on the Ductility Requirement of Bridge Bents
| British Library Conference Proceedings | 2009
Residual drift spectra for RC bridge columns subjected to near-fault earthquakes
| Springer Verlag | 2021
Torsional Ductility of Circular Concrete Bridge Columns
| British Library Conference Proceedings | 2007
Torsional Ductility of Circular Concrete Bridge Columns
| ASCE | 2007