Research on modeling method for connectors in the train-track-bridge system based on multi-timestep explicit-implicit co-simulation

Wei, Biao; Chen, Mingyu; Jiang, Lizhong; Yu, Yujie; Xiao, Binqi; Zheng, Xianglin; Chen, Yuanjun; Lu, Andong; Chen, Jun

doi:10.1016/j.engstruct.2024.119100

Engineering Structures

2025

DOI: 10.1016/j.engstruct.2024.119100

|View full text |Cite

Research on modeling method for connectors in the train-track-bridge system based on multi-timestep explicit-implicit co-simulation

Biao Wei,

Mingyu Chen,

Lizhong Jiang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Strain distribution prediction in UHPC beams using deep learning model

Zhang,

Chen,

Zheng

et al. 2024

Structural Concrete

View full text Add to dashboard Cite

Over the last three decades, ultra‐high‐performance concrete (UHPC) has emerged as a highly innovative cementitious engineering material. This paper utilizes a distributed fiber optic sensor based on optical frequency domain reflectometry (OFDR) combined with the deep learning model to monitor and predict the strain distribution in UHPC T‐beams subjected to two point bending experiment. These sensors are deployed on the side surfaces of the UHPC T‐beams to capture the strain distribution when subjected to vertical loads, facilitating the determination of crack locations based on strain distribution peaks. While time series modeling is widely used in civil engineering to monitor potential damage using sensor data, its application in tracking and predicting known cracks is less explored. To address this gap, a Long Short‐Term Memory (LSTM) neural network model is developed to forecast the increase in the peak value of the strain distribution prior to structural damage, thus predicting crack initiation. The accuracy of the proposed LSTM model in predicting the UHPC strain distribution was thoroughly investigated. The results demonstrate excellent agreement between the predicted strain distributions and those detected by the ODISI 6000 monitoring system. The root‐mean‐square errors of the model‐predicted strains are generally below 10 με, with an average coefficient of determination (R2) reaching up to 98%, indicating a high degree of accuracy.

show abstract

Strain distribution prediction in UHPC beams using deep learning model

Zhang,

Chen,

Zheng

et al. 2024

Structural Concrete

View full text Add to dashboard Cite

show abstract

Optimized evaluation of ground motion intensity measures for the prefabricated cantilevered roadbed structure in mountainous areas

Chen,

Xie,

Xiang

et al. 2024

Structures

View full text Add to dashboard Cite

Impact of the structural system type on the seismic fragility of a multi-tower cable-stayed bridge

Jiao,

Wang,

Yan

et al. 2024

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Research on modeling method for connectors in the train-track-bridge system based on multi-timestep explicit-implicit co-simulation

Cited by 3 publications

References 45 publications

Strain distribution prediction in UHPC beams using deep learning model

Strain distribution prediction in UHPC beams using deep learning model

Optimized evaluation of ground motion intensity measures for the prefabricated cantilevered roadbed structure in mountainous areas

Impact of the structural system type on the seismic fragility of a multi-tower cable-stayed bridge

Contact Info

Product

Resources

About