Tunnel-mooring-train coupled dynamic analysis for submerged floating tunnel under wave excitations

Jin, Chungkuk; Kim, Moo-Hyun

doi:10.1016/j.apor.2019.102008

Cited by 49 publications

(12 citation statements)

References 39 publications

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“…The relationship is less strong in vertical displacements under different failure scenarios since vertical motions are smaller having 60-degree mooring toe angle and relatively large BWR (=1.3), and thus less sensitive to certain mooring-line failure. If the BWR is decreased, the vertical displacements are increased [7,17], then they are to play a more important role. As a result, the distance between sensors should be short enough to acquire high detection accuracy but large enough to minimize cost and complexity.…”

Section: Failure-detection Performancementioning

confidence: 99%

“…The training data for the developed ML algorithm was produced by running the author-developed time-domain SFT simulation program [7] using the commercial software, OrcaFlex [16]. The simulations of SFT's wave-induced motions and mooring tensions were partly validated through comparisons with an independent SFT hydro-elastic simulation program [7,17] and a series of experimental results with small SFT sections [18]. To validate the developed smart monitoring system, various intact/failure scenarios and wave conditions were considered for training and testing of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Mooring-Failure Monitoring of Submerged Floating Tunnel Using Deep Neural Network

et al. 2020

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This paper presents a machine learning method for detecting the mooring failures of SFT (submerged floating tunnel) based on DNN (deep neural network). The floater-mooring-coupled hydro-elastic time-domain numerical simulations are conducted under various random wave excitations and failure/intact scenarios. Then, the big-data is collected at various locations of numerical motion sensors along the SFT to be used for the present DNN algorithm. In the input layer, tunnel motion-sensor signals and wave conditions are inputted while the output layer provides the probabilities of 21 failure scenarios. In the optimization stage, the numbers of hidden layers, neurons of each layer, and epochs for reliable performance are selected. Several activation functions and optimizers are also tested for the present DNN model, and Sigmoid function and Adamax are respectively adopted to enhance the classification accuracy. Moreover, a systematic sensitivity test with respect to the numbers and arrangements of sensors is performed to find the appropriate sensor combination to achieve target prediction accuracy. The technique of confusion matrix is used to represent the accuracy of the DNN algorithms for various cases, and the classification accuracy as high as 98.1% is obtained with seven sensors. The results of this study demonstrate that the DNN model can effectively monitor the mooring failures of SFTs utilizing real-time sensor signals.

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Section: Failure-detection Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mooring-Failure Monitoring of Submerged Floating Tunnel Using Deep Neural Network

et al. 2020

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“…Due to the complex external environmental conditions of SFTs, there are several dynamic load types during operation period which wave and current around SFT, water flow excitation loads of anchor cables and vehicle loads inside the structure are the most common dynamic loads. In existing research data, the dynamic responses of SFTs under wave and current load [3][4][5], earthquake excitation [6][7][8] and anchor cable flow excitation [9,10] have been investigated through mathematical and physical models [11,12], while the dynamic responses of SFT under vehicle load has rarely been studied. Tariverdilo et al [13] performed simulation analyses on vehicle loads in SFTs considering them as concentrated forces moving longitudinally along pipe body.…”

Section: Introductionmentioning

confidence: 99%

Research on the Dynamic Response of Submerged Floating Tunnels to Wave Currents and Traffic Load

Jiang¹,

Wu²,

Ji³

et al. 2023

Fluid Dynamics &Amp; Materials Processing

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Submerged floating tunnel (SFTs) are typically subjected to complex external environmental and internal loads such as wave currents and traffic load. In this study, this problem is investigated through a finite element method able to account for fluid-structure interaction. The obtained results show that increasing the number of vehicles per unit length enhances the transverse vibrational displacements of the SFT cross sections. Under ultimate traffic load condition, one-way and two-way syntropic distributions can promote the dynamic responses of SFTs whereas two-way reverse distributions have the opposite effect.

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“…Lu et al [21] analyzed SFT dynamics when going through tether slacking and the related snap force under wave conditions. Jin et al [22] developed a time-domain coupled hydroelastic dynamics model to solve the tunnel-mooringtrain interaction under wave excitations for investigating the interaction between the tunnel and the train. Luo et al [23] established a model of the SFT impacted by the submarine based on the principles of the smoothed particle hydrodynamics and finite element method to analyze the variation relationship of related physical quantities of the submarine and tunnel.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Dynamic Response of Submerged Floating Tunnel under Regular Wave Conditions

Luo

Huang

Yao

et al. 2022

Shock and Vibration

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A submerged floating tunnel (SFT) is considered an innovative alternative to conventional bridges and underground or immersed tunnels for passing through deep water. Assessment of hydrodynamic performance of SFT under regular wave loading is one of the important factors in the design of SFT structure. In this paper, a theoretical hydrodynamic model is developed to describe the coupled dynamic response of an SFT and mooring lines under regular waves. In this model, wave-induced hydrodynamic loads are estimated by the Morison equation for a moving object, and the simplified governing differential equation of the tunnel with mooring cables is solved using the fourth-order Runge–Kutta and Adams numerical method. The numerical results are successfully validated by direct comparison against published experimental data. On this basis, the effects of the parameters such as the cable length, buoyancy-weight ratio, wave period, wave steepness, and water/submergence depth on the dynamic response of the SFT under wave loading are studied. The results show that tunnel motions and cable tensions grow with wave height and period and decrease with submergence depth. The resonance of the tunnel will be triggered when the wave period is close to its natural vibration period, and the estimation formula of wave period corresponding to tunnel resonance is proposed in this paper.

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Tunnel-mooring-train coupled dynamic analysis for submerged floating tunnel under wave excitations

Cited by 49 publications

References 39 publications

Mooring-Failure Monitoring of Submerged Floating Tunnel Using Deep Neural Network

Mooring-Failure Monitoring of Submerged Floating Tunnel Using Deep Neural Network

Research on the Dynamic Response of Submerged Floating Tunnels to Wave Currents and Traffic Load

Numerical Simulation of Dynamic Response of Submerged Floating Tunnel under Regular Wave Conditions

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