Diagnosis and Prediction Model of Tunnel Lining Cracks Based on Multi-source Data

Bingzhen Wu; Xulin Chen; Weidong Lei

doi:10.1145/3653081.3653170

Diagnosis and Prediction Model of Tunnel Lining Cracks Based on Multi-source Data

Bingzhen Wu
, Xulin Chen
, Weidong Lei^*

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The current study is focused on the prediction and diagnosis model for the disease of the tunnel lining crack based on multiple source data. The damage index and the safety rating of cracks are obtained by combining the monitoring data and practical engineering experience based on AHP-Variable Fuzzy Theory. After that, the influencing factors on the continuous expansion of cracks are analyzed and normalized, and the cumulative model of crack damage is constructed, where the posterior distribution of parameters is calculated based on the Bayesian method, and the weight of each influencing factor is quantified by the covariate coefficient of the model. According to the time-dependent curve of crack diseases predicted by the model, the future development of the diseases could be depicted.

Original language	English
Title of host publication	Proceedings of 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023
Publisher	Association for Computing Machinery
Pages	534-538
Number of pages	5
ISBN (Electronic)	9798400716485
DOIs	https://doi.org/10.1145/3653081.3653170
State	Published - 24 Nov 2023
Externally published	Yes
Event	5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023 - Nanchang, China Duration: 24 Nov 2023 → 26 Nov 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023
Country/Territory	China
City	Nanchang
Period	24/11/23 → 26/11/23

Keywords

Cracks in the tunnel lining
Weibull distribution
damage accumulation model
fuzzy analytical hierarchy process

Access to Document

10.1145/3653081.3653170

Cite this

Wu, B., Chen, X., & Lei, W. (2023). Diagnosis and Prediction Model of Tunnel Lining Cracks Based on Multi-source Data. In Proceedings of 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023 (pp. 534-538). (ACM International Conference Proceeding Series). Association for Computing Machinery . https://doi.org/10.1145/3653081.3653170

@inproceedings{924e7705c2df400192aeabc668e73e98,

title = "Diagnosis and Prediction Model of Tunnel Lining Cracks Based on Multi-source Data",

abstract = "The current study is focused on the prediction and diagnosis model for the disease of the tunnel lining crack based on multiple source data. The damage index and the safety rating of cracks are obtained by combining the monitoring data and practical engineering experience based on AHP-Variable Fuzzy Theory. After that, the influencing factors on the continuous expansion of cracks are analyzed and normalized, and the cumulative model of crack damage is constructed, where the posterior distribution of parameters is calculated based on the Bayesian method, and the weight of each influencing factor is quantified by the covariate coefficient of the model. According to the time-dependent curve of crack diseases predicted by the model, the future development of the diseases could be depicted.",

keywords = "Cracks in the tunnel lining, Weibull distribution, damage accumulation model, fuzzy analytical hierarchy process",

author = "Bingzhen Wu and Xulin Chen and Weidong Lei",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023 ; Conference date: 24-11-2023 Through 26-11-2023",

year = "2023",

month = nov,

day = "24",

doi = "10.1145/3653081.3653170",

language = "英语",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

pages = "534--538",

booktitle = "Proceedings of 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023",

address = "美国",

}

Wu, B, Chen, X & Lei, W 2023, Diagnosis and Prediction Model of Tunnel Lining Cracks Based on Multi-source Data. in Proceedings of 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023. ACM International Conference Proceeding Series, Association for Computing Machinery , pp. 534-538, 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023, Nanchang, China, 24/11/23. https://doi.org/10.1145/3653081.3653170

Diagnosis and Prediction Model of Tunnel Lining Cracks Based on Multi-source Data. / Wu, Bingzhen; Chen, Xulin; Lei, Weidong.
Proceedings of 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023. Association for Computing Machinery , 2023. p. 534-538 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Diagnosis and Prediction Model of Tunnel Lining Cracks Based on Multi-source Data

AU - Wu, Bingzhen

AU - Chen, Xulin

AU - Lei, Weidong

PY - 2023/11/24

Y1 - 2023/11/24

N2 - The current study is focused on the prediction and diagnosis model for the disease of the tunnel lining crack based on multiple source data. The damage index and the safety rating of cracks are obtained by combining the monitoring data and practical engineering experience based on AHP-Variable Fuzzy Theory. After that, the influencing factors on the continuous expansion of cracks are analyzed and normalized, and the cumulative model of crack damage is constructed, where the posterior distribution of parameters is calculated based on the Bayesian method, and the weight of each influencing factor is quantified by the covariate coefficient of the model. According to the time-dependent curve of crack diseases predicted by the model, the future development of the diseases could be depicted.

AB - The current study is focused on the prediction and diagnosis model for the disease of the tunnel lining crack based on multiple source data. The damage index and the safety rating of cracks are obtained by combining the monitoring data and practical engineering experience based on AHP-Variable Fuzzy Theory. After that, the influencing factors on the continuous expansion of cracks are analyzed and normalized, and the cumulative model of crack damage is constructed, where the posterior distribution of parameters is calculated based on the Bayesian method, and the weight of each influencing factor is quantified by the covariate coefficient of the model. According to the time-dependent curve of crack diseases predicted by the model, the future development of the diseases could be depicted.

KW - Cracks in the tunnel lining

KW - Weibull distribution

KW - damage accumulation model

KW - fuzzy analytical hierarchy process

UR - https://www.scopus.com/pages/publications/85192862431

U2 - 10.1145/3653081.3653170

DO - 10.1145/3653081.3653170

M3 - 会议稿件

AN - SCOPUS:85192862431

T3 - ACM International Conference Proceeding Series

SP - 534

EP - 538

BT - Proceedings of 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023

PB - Association for Computing Machinery

T2 - 5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023

Y2 - 24 November 2023 through 26 November 2023

ER -

Diagnosis and Prediction Model of Tunnel Lining Cracks Based on Multi-source Data

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this