An optimum metamodel for safety control of operational subway tunnel during underpass shield tunneling

Zhang, Junru; Huang, Gang; Gou, Xin‐ming

doi:10.1002/stc.2195

Cited by 11 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Markov stochastic processes, as a class of discrete-time stochastic processes with Markov properties in mathematics, provide quantitative analysis of the transitions between system states [12]. The Markov property means that the future state of the system depends only on the current state and is independent of the past state [13]. This feature gives MM a unique advantage in describing and analyzing the dynamic behavior of complex systems [14].…”

Section: Methodology 21 System Designmentioning

confidence: 99%

Intelligent early warning of highway tunnel safety monitoring data based on Markov model

Wang

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

View full text Add to dashboard Cite

Traditional highway tunnel management systems often face problems such as independent and decentralized management, inflexible monitoring, and low visualization of disease details. Especially in the face of traffic accidents, lane foreign objects, vehicle congestion, and vehicle and pedestrian congestion in tunnels, traditional warning systems often fail to provide timely and accurate warning information, posing great risks to the safe operation of tunnels. In response to the above issues, this article proposes an intelligent early warning system for highway tunnel safety monitoring data based on Markov model (MM). This system fully utilizes the advantages of MM in describing random processes, and achieves dynamic monitoring and early warning of tunnel safety status through real-time analysis of tunnel monitoring data, combined with Internet of Things (IoT) technology. The experimental results show that the intelligent early warning system for highway tunnel safety monitoring data based on MM has high accuracy and reliability. It can not only achieve real-time monitoring and early warning of tunnel safety status, but also provide rich visual information to help managers better understand the operation status of tunnels and improve the level of tunnel safety management.

show abstract

Section: Methodology 21 System Designmentioning

confidence: 99%

Intelligent early warning of highway tunnel safety monitoring data based on Markov model

Wang

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

View full text Add to dashboard Cite

show abstract

“…Due to the convenience and efficiency of metro lines, increasingly more cities are planning and constructing new metro lines. Therefore, traversing of a new tunnel traverses around existing structures is becoming common, resulting in the formation of a structural system including tunnels, underground stations, building foundations, and so on (Chen et al, 2016; Guo, 2011; Huang et al, 2021; Li et al, 2022; Liang et al, 2016; Liu, Fang, et al, 2022; Marshall et al, 2010; Meng et al, 2022; Shi et al, 2015; Zhang et al, 2018). However, considering the high density of population and architectures in the urban area, the effect arising from new tunnels on the existing structure needs to be controlled below a certain level; otherwise, there may be irreversible deformation and damage to the existing structure in the form of cracks and leakage.…”

Section: Introductionmentioning

confidence: 99%

Mechanical responses of underground carriageway structures due to construction of metro tunnels beneath the existing structure: A case study

Han,

Ye,

Han

et al. 2023

Deep Underground Science and Engineering

View full text Add to dashboard Cite

To understand the mechanical response pattern of the existing structure and ground due to the construction of metro tunnels underneath, the finite difference method is adopted to study the torsional deformation and stress variation of the existing structure and the effect of underground carriageway structures on the surface subsidence. The curves of the maximum differential subsidence, torsion angle, and distortion of the cross‐section of the existing structure show two peaks in succession during traversing of two metro tunnels beneath it. The torsion angle of the existing structure changes when the two tunnels traverse beneath it in opposite directions. The first traversing of the shield tunnel mainly induces the magnitude variation in torsional deformation of the existing structure, but the second traversing of the subsurface tunnel may cause a dynamic change in the magnitude and form of torsional deformation in the existing structure. The shielding effect can reduce the surface subsidence caused by metro tunnel excavation to a certain extent, and the development trend of subsidence becomes slower as the excavation continues.

show abstract

“…Machine learning [ 6 – 8 ] can not only be applied to the preprocessing stage of monitoring data but also, more importantly, it is necessary to establish a learning model and actively learn the information contained in the data to evaluate the safety of the structural state and reflect the health state in time. In the face of a large amount of information generated all the time, the traditional manual method is simply unable to make efficient and rapid judgment [ 9 , 10 ]. Machine learning first appeared in the last century.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Human Exercise Health Monitoring Data of Smart Bracelet Based on Machine Learning

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The smart bracelet has become a hot-selling commodity, according to a daily consumption survey. Based on people’s interest and concern for their health, the smart bracelet, as a design and application for achieving healthy weight loss monitoring, is quickly becoming a popular new favorite. This bracelet detects fat using the near-infrared diffuse reflection principle, with the goal of assisting people in controlling and maintaining a healthy weight. A large amount of data has been accumulated in all walks of life due to the development of the Internet network and data storage technology. As a result, the emergence of machine learning plays a critical role in the data analysis of human sports health monitoring of smart bracelets. Based on machine learning, this paper investigates the data analysis of human sports health monitoring smart bracelets. When the population index reaches 50 in the analysis of health monitoring data, the average accuracy of data mining is 86.8 percent, the average accuracy of the association rule algorithm is 85.9 percent, the average accuracy of the collaborative filtering algorithm is 84.3 percent, and the average accuracy of the machine learning algorithm is 90.1 percent in this paper. Among the four algorithms, the method presented in this paper is clearly the most effective, stable, and accurate. The system’s stability and accuracy have been greatly improved by the addition of GPS-assisted and hand-up misjudgment algorithms. Because the smart bracelet is inexpensive, easy to wear, and consistent with consumer psychology, it is becoming increasingly popular to use it to monitor the human body’s sports health.

show abstract

An optimum metamodel for safety control of operational subway tunnel during underpass shield tunneling

Cited by 11 publications

References 36 publications

Intelligent early warning of highway tunnel safety monitoring data based on Markov model

Intelligent early warning of highway tunnel safety monitoring data based on Markov model

Mechanical responses of underground carriageway structures due to construction of metro tunnels beneath the existing structure: A case study

Analysis of Human Exercise Health Monitoring Data of Smart Bracelet Based on Machine Learning

Contact Info

Product

Resources

About