Tunnel lining detection and retrofitting

Jiang, Yuexiang; Wang, Lai; Zhang, Bo; Dai, Xingcan; Ye, Jun; Sun, Baoqing; Liu, Nianwu; Wang, Zhen; Zhao, Yao

doi:10.1016/j.autcon.2023.104881

Cited by 28 publications

(5 citation statements)

References 154 publications

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“…As the most familiar material in civil engineering, concrete is widely used in the construction of various infrastructure projects, especially tunnels. However, due to the fact that the main structure is located underground, the structural stress is complex, the quality assurance problem is a huge challenge, and the quality of the tunnel lining is particularly important [1,2]. According to the research statistics in recent years, about 10-30% of the tunnels built according to the existing process conditions have the phenomenon of void lining in the inspection process during operation [3], and some tunnels are accompanied by various forms of cracks [4].…”

Section: Introductionmentioning

confidence: 99%

Study on the Characteristics of Circumferential and Longitudinal Flow of Vault Concrete during Tunnel Lining Pouring Processes

Yang,

Wu,

Zhou

2024

Materials

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With a large number of railroad and highway tunnels opening for operation, the diseases caused by hidden lining defects are increasing. The study of flow characteristics of freshly mixed concrete during tunnel lining casting is the key to revealing the formation mechanism of hidden defects. This paper revealed the location of blank lining formation by investigating the circumferential and longitudinal flow characteristics of concrete in the vault during tunnel pouring to provide suggestions for improving the quality of tunnel lining pouring for the various projects. This paper adopted the method of indoor testing, selected the suitable working conditions and flow parameters, validated the accuracy of the test with a numerical simulation, and simulated the secondary lining pouring process of the tunnel arch from the circumferential direction and longitudinal direction. This revealed the flow characteristics of the freshly mixed concrete in the process of pouring the arch lining. The flow of concrete in the arch lining was basically characterized by two major features which were similar to the flow in the pumping pipe and the layered flow. It also revealed the relationship between the concrete flow rate, flow distance, and the location of the formation of the blank lining risk zone with the slump of the concrete, the pumping pressure, and the radius of the tunnel.

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Section: Introductionmentioning

confidence: 99%

Study on the Characteristics of Circumferential and Longitudinal Flow of Vault Concrete during Tunnel Lining Pouring Processes

Yang,

Wu,

Zhou

2024

Materials

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“…There are various methods for the inspection of structures, including those using microwave ( [13]), robots ( [14]), and ultrasonic waves ( [17]). In reference [15], various techniques related to nondestructive inspection of tunnel linings are presented.…”

Section: Related Workmentioning

confidence: 99%

Smart non-destractive test of a concrete wall using a hammer

Ito,

Koike,

Hibino

2024

International Journal of Electronics and Telecommunications

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Large concrete structures such as buildings, bridges, and tunnels are aging. In Japan and many other countries, those built during economic reconstruction after World War II are about 60 to 70 years old, and flacking and other problems are becoming more noticeable. Periodic inspections were made mandatory by government and ministerial ordinance during the 2013-2014 fiscal year, and inspections based on the new standards have just begun. There are various methods to check the soundness of concrete, but the hammering test is widely used because it does not require special equipment. However, long experience is required to master the hammering test. Therefore, mechanization is desired. Although the difference between the sound of a defective part and a normal part is very small, we have shown that neural network is useful in our research. To use this technology in the actual field, it is necessary to meet the forms of concrete structures in various conditions. For example, flacking in concrete exists at various depths, and it is impossible to learn about flacking in all cases. This paper presents the results of a study of the possibility of finding flacking at different depths with a single inspection learning model and an idea to increase the accuracy of a learning model when we use a rolling hammer.

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“…Due to the inherent constraints of the above methods, a commonly used method is manual tapping in practical engineering, where the tunnel lining is tapped using hand-held tapping devices, such as knocking rods, to excite acoustic signals, and the condition of the lining structure is judged by the human auditory system [10]. The artificial judgement has the disadvantage of being subject to subjective factors, less automated and prone to misjudgment [11].…”

Section: Introductionmentioning

confidence: 99%

Acoustic tunnel lining cavity detection using cepstral coefficients with optimized filter bank

Wu,

Cheng,

Yan

et al. 2024

Meas. Sci. Technol.

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Tunnels are an essential component of modern transportation infrastructure, and their structural health is critical to traffic safety, which can be seriously affected by tunnel lining cavities. In this paper, an acoustic-based detection approach for assessing the integrity of tunnel linings is studied. By tapping the tunnel lining surface, acoustic signals are sampled and analyzed using a novel feature parameter extraction algorithm—the energy-frequency cepstral coefficient, which uses wavelet packet decomposition to obtain energy distribution statistics in the frequency domain of the signal, and constructs a signal-dependent filter bank to achieve the cepstral coefficient extraction. Compared with the traditional Mel filter bank, this method can adaptively adjust the resolution of the filter bank according to the frequency characteristics of the classified samples. This allows for higher frequency resolution in regions where the energy distribution is concentrated. As a result, the extracted feature parameters achieve both dimensional compression and superior information retention. Experimental results show that the proposed energy-frequency cepstral coefficient feature outperforms the traditional Mel-frequency cepstral coefficient feature, resulting in a higher accuracy of tunnel lining detection. The convolutional neural network model achieves an accuracy of 99.2%, with a 78.9% reduction in error rate compared with the traditional Mel-frequency cepstral coefficient feature parameters. Additionally, a particle swarm optimization support vector machine model is trained to achieve an accuracy rate of 99.6% and an error rate reduction of 76.5%.

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Tunnel lining detection and retrofitting

Cited by 28 publications

References 154 publications

Study on the Characteristics of Circumferential and Longitudinal Flow of Vault Concrete during Tunnel Lining Pouring Processes

Study on the Characteristics of Circumferential and Longitudinal Flow of Vault Concrete during Tunnel Lining Pouring Processes

Smart non-destractive test of a concrete wall using a hammer

Acoustic tunnel lining cavity detection using cepstral coefficients with optimized filter bank

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