Cracking Performance of an Operational Tunnel Lining Due to Local Construction Defects

Fu, Junwei; Xie, Jiawei; Wang, Shuying; Yang, Junsheng; Yang, Feng; Pu, Hao

doi:10.1061/(asce)gm.1943-5622.0001371

Cited by 36 publications

(10 citation statements)

References 18 publications

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“…In recent years, with the continuous increase in the number of tunnel projects, the treatment of tunnel lining defects has gradually become a hotspot of social concerns (N. Zhang et al., 2019; D. P. Zhao et al., 2014). Due to the joint action of internal and external factors, such as long‐term cyclic loads, groundwater, and improper construction, various tunnel lining defects have occurred inevitably during tunnel construction and operation (Fu et al., 2019; Ye et al., 2021). Among them, crack (Gong et al., 2017; S. Zhao et al., 2021), water leakage (Ding et al., 2017; Lei, Zhu, et al., 2021), and rebar‐exposed (H. Wang et al., 2018; Xue et al., 2022) are the main factors affecting the service life of tunnels.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Zhou

Zhang

Gong

2022

Computer aided Civil Eng

135

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Aiming to solve the challenges of low detection accuracy, poor anti-interference ability, and slow detection speed in the traditional tunnel lining defect detection methods, a novel deep learning-based model, named You Only Look Once network v4 enhanced by EfficientNet and depthwise separable convolution (DSC; YOLOv4-ED), is proposed. In the YOLOv4-ED, EfficientNet is used as the backbone to improve the identification accuracy of indistinguishable defect targets in complex tunnel background and light conditions. Furthermore, DSC block is introduced to reduce the storage space of the model and thereby enhance the detection efficiency. The experimental results indicate that the mean average precision, F1 score, Model Size, and FPS of YOLOv4-ED are 81.84%, 81.99%, 49.3 MB, and 43.5 f/s, respectively, which is superior to the comparison models in both detection accuracy and efficiency. Based on robust and cost-effective YOLOv4-ED, a tunnel lining defect detection platform (TLDDP) with the capacity of automated inspection of various lining defects (i.e., water leakage, crack, rebar-exposed) is built. The established TLDDP can realize the high-precision and automatic detection of multiple tunnel lining defects under different lighting and complex background conditions of the practical in-service tunnel.

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Section: Introductionmentioning

confidence: 99%

“…P. Zhao et al, 2014). Due to the joint action of internal and external factors, such as longterm cyclic loads, groundwater, and improper construction, various tunnel lining defects have occurred inevitably during tunnel construction and operation (Fu et al, 2019;Ye et al, 2021). Among them, crack (Gong et al, 2017;S.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Zhou

Zhang

Gong

2022

Computer aided Civil Eng

135

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“…If left unaddressed, significant deformation of the tunnel structure will occur and threaten the stability and safety of the wider tunnel system. Consequently, it may affect the normal operations of the urban rail transit system, and in turn result in further negative social effects (Fu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

New Damage Identification Method for Operational Metro Tunnel Based on Perturbation Theory and Fuzzy Logic

et al. 2021

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The structural health of operational metro tunnels is closely related to public safety. Prior research has focused on the locations of structural damage, but few researchers have examined both the location of damage and identifying the degree of damage, especially in metro shield tunnels. This paper proposes a new method for identifying structural damage that entails locating and detecting the degradation of tunnel performance, with a special focus on characterizing the degree of damage. First, the dynamic behaviors (modal frequencies and shapes) of different damage levels are obtained from an analytical model of the original tunnel structure. Second, a Modal Strain Energy Damage Indicator (MSEDI) is introduced to locate the damage, regardless of size. Once the location of the damage is identified using MSEDI, a fuzzy logic-based damage identification (FLBDI) method is used to determine the actual extent of the damage.Finally, a simplified model of the tunnel is created using the Euler-Bernoulli beam theory and Winkler's foundation, to further test the procedure under an incomplete modal information condition and with differing noise levels. The results reveal that the fuzzy logic-based system can identify the degree of damage and structural degradation with very high accuracy, in which the location of damage and the prediction of performance degradation is satisfactorily confirmed.

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“…1). Also, some works have assessed earth pressures induced on the retention walls in ground with multiple layers of soils and rocks (Lee and Jeon, 1993;Hong and Yoon, 1995;Jeong and Kim, 1997;Yoo, 2001;Gan et al, 2017;Zeng et al, 2018;Jeon and Lee, 2018;Fu et al, 2019). But these assessments only compared the earth pressures measured in multi-layered soils with the earth pressure proposed by Peck while the in uence of varying properties of joint (inclinations, orientations, spacing, shear conditions, cohesive strengths, number of joint sets etc) were not considered.…”

Section: Introductionmentioning

confidence: 99%

Extensive study on the impact of joint inclinations on the earth pressure in rock ground

Adedokun

Son

2021

Preprint

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The impact of different joint inclinations on the earth pressure was extensively carried out in this study, using Universal Distinct Element Code (UDEC) which is based on discrete element method. Numerical parametric investigations, which considered varying joint inclinations and rock types, were conducted after the numerical method had been verified through a physical model test. The joint angles considered ranged from 0º to 90º in the interval of 5º and the rock types are hard, slightly and moderately weathered rocks. The results of the analyses were subjected to statistical analysis using analysis of variance (ANOVA) at 5% level of significance, and compared with empirical earth pressure envelope for sand ground. The comparisons showed that earth pressures in rock ground are substantially varied from those in sand ground. The result of ANOVA revealed that joint inclinations have statistically significant effect on magnitude of the earth pressure, and practitioners should consider this factor while designing retaining structure in rock masses.

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Cracking Performance of an Operational Tunnel Lining Due to Local Construction Defects

Cited by 36 publications

References 18 publications

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

New Damage Identification Method for Operational Metro Tunnel Based on Perturbation Theory and Fuzzy Logic

Extensive study on the impact of joint inclinations on the earth pressure in rock ground

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