Investigating the dynamic response of a double-box utility tunnel buried in calcareous sand against ground surface explosion

Pan, Yahao; Zong, Zhouhong; Li, Jun; Qian, Haimin; Wu, Chengqing

doi:10.1016/j.tust.2024.105636

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…For the experimental studies, based on the in-field explosion experiments, the protective performance of utility tunnels under a ground accidental explosion has been studied, which includes two types of utility tunnels (steel bars-reinforced concrete utility tunnel and basalt fiber reinforced polymer bars-reinforced utility tunnel) [3,4]. By a similar ground surface explosion experiment, the structural dynamic responses of a double-box-reinforced concrete utility tunnel buried in calcareous sand have been analyzed [5]. Moreover, based on a quasi-static experiment, the cross-sectional mechanical behavior of a prefabricated multi-cabin-reinforced concrete utility tunnel under free-field racking deformation by earthquake action has been studied [6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning

Gao,

Ge,

Gao

et al. 2024

Applied Sciences

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The underground utility tunnel in a soft foundation is generally affected by the serious disturbance of the vehicle load during the operation period. Therefore, in this study, for the typical utility tunnel engineering in Suqian City of Jiangsu Province, China, field tests were conducted to monitor the performance of the utility tunnel structure in a soft foundation affected by the ground traffic loads during the operation period. Based on the test results, the datasets whose number is 15,376, composed of the five main disturbance factors (four vehicle operating load parameters and one operating time parameter), and the corresponding two main structure responses (displacement and stress) have been constructed. Based on the obtained datasets, using the proposed new deep learning model called WO-DBN, in which the seven hyperparameters of a deep belief network (DBN) are determined by the whale optimization algorithm (WOA), the safety responses of the utility tunnel structure have been predicted. The results show that for the prediction results, the average absolute error for the displacement is 0.1604, and for the stress, it is 12.3726, which are not significant and can meet the requirement of the real engineering. Therefore, the deep learning model can accurately predict the performance of the utility tunnel structure under a vehicle load and other disturbances, and the model has good applicability.

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