Big Data Analysis of Structural Defects and Traffic Accidents in Existing Highway Tunnels

Ding, Hao; Liu, Shuai; Cai, Shuang; Xia, Yangyuyu

doi:10.1007/978-3-030-32029-4_17

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Secondly, for the tunnel phase, the morphological characteristics of structural defects such as lining cracking, water seepage, and insufficient thickness, and its relationship with the tunnel space position and geological conditions are some important characters of the tunnel (Ding 2019) in which the big data becomes a useful method to defect the structure and traffic accident. In another study by Pan (2019), the important factors in the tunnels have been identified and some kind of big data techniques were implemented to analyze the data.…”

Section: Big Data Applications In Highway Industrymentioning

confidence: 99%

Advances in Big Data Applications for transportation: airline, highway, and railway

Rouyendegh¹,

Nacar

2021

CEREM

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Aim: The purpose of this article is to present the latest advances in big data applications in the industries of the transportation sector such as airline, highway, and railway. It is difficult to analyze data in transportation because there is continuous real-time data flow. Since the improvements made are fast with the same logic, it is necessary to catch up with the new developments. Data should be analyzed with the big data concept because data stacks highly contain non-structural data types in transportation data. Although the mentioned industries are complementary to each other, the applications differ depending on the needs of the industry. Thus, solutions to specific problems in different industries using big data applications should be addressed. Design / Research methods: In accordance with the purpose of the study, big data studies that provide added value to the transportation sector were examined. Studies have been filtered through some criteria which are whether the application is adaptable to the industry, the study is available online in full-text, and its references are from respectable sources. Conclusions / findings: All the big data application studies in the academy are not adaptable in real-life problems or suitable for all situations. For this reason, trying all of the applications will lead to moral and material losses for firms. This study is a guideline for companies to follow the developments in the big data concept and to choose the one that suits their problems. Thus, the gap between academia and industry was tried to close. Originality / value of the article: Although studies are referring to big data applications in the transportation sector, this study differs from others in terms of specifically analyzing big data applications in different industries such as airline, highway, and railway in the transportation sector

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Section: Big Data Applications In Highway Industrymentioning

confidence: 99%

Advances in Big Data Applications for transportation: airline, highway, and railway

Rouyendegh¹,

Nacar

2021

CEREM

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“…Xue and Zhang 2019), resilience assessment (e.g Khetwal et al 2019),. structural defect identification (e.g Ding et al 2019),. tunnel face stability (e.g Hayashi et al 2019),.…”

mentioning

confidence: 99%

Machine learning to inform tunnelling operations: recent advances and future trends

Sheil

Suryasentana

Mooney

et al. 2020

Proceedings of the Institution of Civil Engineers - Smart Infra

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The proliferation of data collected by modern tunnel-boring machines (TBMs) presents a substantial opportunity for the application of machine learning (ML) to support the decision-making process on-site with timely and meaningful information. The observational method is now well established in geotechnical engineering and has a proven potential to save time and money relative to conventional design. ML advances the traditional observational method by employing data analysis and pattern recognition techniques, predicated on the assumption of the presence of enough data to describe the physics of the modelled system. This paper presents a comprehensive review of recent advances and applications of ML to inform tunnelling construction operations with a view to increasing their potential for uptake by industry practitioners. This review has identified four main applications of ML to inform tunnelling – namely, TBM performance prediction, tunnelling-induced settlement prediction, geological forecasting and cutterhead design optimisation. The paper concludes by summarising research trends and suggesting directions for future research for ML in the tunnelling space.

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Overall Instability Failure Mechanism and Monitoring Strategy Research of the Shallow Tunnel under Unsymmetrical Pressure during Operation Period

Guo

Liu

et al. 2022

Advances in Materials Science and Engineering

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Aiming at the safety of tunnel operation, based on the investigation and statistical analysis of a large number of operating tunnel diseases, comprehensively considering the interaction between the surrounding rock and the structure, this paper establishes the failure criteria for surrounding rocks and lining materials by using numerical limit analysis and theoretical analysis methods, analyzes the overall stability and instability failure mechanism on the shallow tunnel under unsymmetrical pressure during the operation period by using the strength reduction method in light of the operation safety of the tunnel, proposes the reasonable monitoring strategy, and also constructs monitoring index system. The analysis and research results show that the tunnel structure is affected by bias pressure due to the asymmetry of the terrain. The probability of disease in the bias pressure tunnel under asymmetric terrain conditions is significantly higher than that of the general terrain. The shallow tunnel under unsymmetrical pressure has experienced three development stages from shear failure to overall instability, including rock mass fracture and sliding, structural damage, and overall instability, which can be divided into eight processes. During operation, the key monitoring indicators of surface deformation are the subsidence and deformation of the slope top and the horizontal displacement of the slope itself, followed by the monitoring of the uplift and deformation at the bottom of the slope. Meanwhile, close attention should be paid to the structural stress states in the sliding direction and perpendicular to the sliding direction of the lining structure, which demonstrates that the principal compressive stress in the sliding direction is more sensitive than the principal tensile stress in perpendicular to the sliding direction and should be listed as the key monitoring and early warning indicators of the structural stress.

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Big Data Analysis of Structural Defects and Traffic Accidents in Existing Highway Tunnels

Cited by 3 publications

References 2 publications

Advances in Big Data Applications for transportation: airline, highway, and railway

Advances in Big Data Applications for transportation: airline, highway, and railway

Machine learning to inform tunnelling operations: recent advances and future trends

Overall Instability Failure Mechanism and Monitoring Strategy Research of the Shallow Tunnel under Unsymmetrical Pressure during Operation Period

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