Predicting tunnel squeezing with incomplete data using Bayesian networks

Feng, Xianjin; Jimenez, Rafael E.

doi:10.1016/j.enggeo.2015.06.017

Cited by 74 publications

(23 citation statements)

References 42 publications

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“…Note that only 56 case histories in our database reported specific values of K and the remaining provided information allowing us to compute the values of K using the methods described below [20]:…”

Section: Database Descriptionmentioning

confidence: 99%

“…Recently, soft computational methods, such as artificial neural networks (ANNs) and support vector machines (SVMs), have been proposed to predict tunnel squeezing (or convergence) [1,[17][18][19][20] because ANN and SVM do not require prior knowledge of a particular model form and possess a flexible nonlinear modeling capability [21]. For instance, Shafiei et al [1] proposed an SVM classifier, which yields a higher accuracy than the commonly used empirical method to predict tunnel squeezing based on the Q tunneling index and the buried depth of the tunnel (H).…”

Section: Introductionmentioning

confidence: 99%

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Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Sun

Feng

Yang

2018

Advances in Civil Engineering

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Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. erefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. is study presents a modified application of a multiclass support vector machine (SVM) to predict tunnel squeezing based on four parameters, that is, diameter (D), buried depth (H), support stiffness (K), and rock tunneling quality index (Q). We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. e proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

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Section: Database Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Sun

Feng

Yang

2018

Advances in Civil Engineering

Self Cite

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“…Thus, our bottleneck warning algorithm is mainly proposed to improve this problem outside a certain cluster. Bayesian networks (BNs) [ 49 , 50 ] are probabilistic graphical models used for classification and are useful for predicting complex engineering problems, such as traffic congestion [ 51 ]. Because of their powerful reasoning ability, they are introduced to evaluate the bottlenecks in our study.…”

Section: Routing Optimization and Bottleneck Warningmentioning

confidence: 99%

“…The BN use Bayesian methodology to quantify the changes in the node CPT values for introducing new evidence or updating old evidence. This procedure is known as “uncertainty propagation” or “belief updating”, and an existing algorithm allows its efficient computation [ 49 ].…”

Section: Routing Optimization and Bottleneck Warningmentioning

confidence: 99%

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks

Zou

Shao

Feng

et al. 2016

Sensors

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Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner.

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“…These merits make Bayesian network a more suitable tool in analyzing occurrence probability. Bayesian network has been used in risk analysis of tunnel construction (Zhang et al., ; Feng and Jimenez, ; Wu et al., ). Nevertheless, these studies are unable to obtain risk occurrence probability in each excavation cycle by fully considering geology, design, construction, and management conditions and their mutual relationship, which are regarded as the main causes leading to tunnel accidents (Sousa, ; Zhang et al., ).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Risk Analysis of Diversion Tunnel Construction Simulation

Zhong

Ren

et al. 2017

Computer aided Civil Eng

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Comprehensive and effective risk analysis is significant for studying construction simulation of diversion tunnel. Existing tunnel risk simulation approaches mainly analyze ordinary risk factors, and cannot quantitatively study risk events considering their causes. Additionally, in other tunnel probabilistic risk analysis methods, although some studies have made full probabilistic estimates of tunnel schedule, risk factors are unable to be studied considering cyclic construction characteristics and occurrence probability of risk events cannot be determined quantitatively. To address the issues, a probabilistic risk analysis approach of diversion tunnel construction simulation is proposed. Based on hierarchical simulation model, risk factors can be analyzed at operation level of tunnel construction. Moreover, Bayesian network is embedded into simulation program to quantitatively calculate probability of risk events in each simulation cycle, considering geology, design, construction, and management conditions and their mutual dependence.C 2017 Computer-Aided Civil and Infrastructure Engineering.

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Predicting tunnel squeezing with incomplete data using Bayesian networks

Cited by 74 publications

References 42 publications

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks

Probabilistic Risk Analysis of Diversion Tunnel Construction Simulation

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