scholarly journals Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling-Induced Ground Settlements

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Cong Li ◽  
Shui-Hua Jiang ◽  
Jinhui Li ◽  
Jinsong Huang

This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. Within the framework of the proposed approach, a complex Bayesian back analysis problem is transformed into an equivalent structural reliability problem based on subset simulation. Hermite polynomial chaos expansion-based surrogate models are constructed to improve the computational efficiency of probabilistic back analysis. The reliability of tunneling-induced ground settlements is updated in the process of sequential back analyses. A real shield tunnel project of No. 1 Nanchang Metro Line in China is investigated to assess the effectiveness of the approach. The proposed approach is able to infer the posterior distributions of uncertain geomechanical parameters (i.e., Young’s moduli of surrounding soil layers and ground vehicle load). The reliability of tunneling-induced ground settlements can be updated in a real-time manner by fully utilizing the time-series monitoring data. The results show good agreement with the variation trend of field monitoring data of ground settlement and the post-event investigations.

2012 ◽  
Vol 204-208 ◽  
pp. 196-201 ◽  
Author(s):  
Jian Cong Xu ◽  
Yi Wei Xu

The parabolic-apex numerical back-analysis method (PNBM) was proposed to obtain such physical-mechanics parameters as Young's modulus and lateral pressure coefficient of surrounding rock by 3D FEM numerical analysis based on in-situ monitoring data. Taking Xiang-an Subsea Tunnel (located in Xiamen, Fujian Province, China) for example, adopting the PNBM using ABAQUS software, three dimensional elastic-plastic FEM-PNBM of tunnel surrounding rock was validated using in-situ monitoring data. The results show as follows: Using the PNBM, not only may high calculation precision be obtained, better meeting the demand of actual projects, but also more reasonable and reliable physical mechanics indices of surrounding rock such as Young's modulus and lateral confinement pressure coefficient, may be obtained. The applicability and the simplicity of this proposed method also support its usefulness.


2020 ◽  
Vol 39 (4) ◽  
pp. 5243-5252
Author(s):  
Zhen Lei ◽  
Liang Zhu ◽  
Youliang Fang ◽  
Xiaolei Li ◽  
Beizhan Liu

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.


2018 ◽  
Vol 44 (5) ◽  
pp. 4839-4853 ◽  
Author(s):  
Qixiang Yan ◽  
Weilie Zhang ◽  
Chuan Zhang ◽  
Hang Chen ◽  
Yongwen Dai ◽  
...  

2013 ◽  
Vol 333-335 ◽  
pp. 1509-1513 ◽  
Author(s):  
Zhi Zheng Yin

The monitoring of the settlement of the shield tunnel plays a very important role in the traffic safety of metro trains. The monitor precision and the timely are required for the settlement of the shield tunnel. The automated monitoring must be introduced to the monitoring work, and becomes powerful monitoring method. The article introduced the measurement principle of the hydrostatic leveling system (HLS), through the hydrostatic leveling system preliminary utilized in metro monitoring for construction deep excavation above shield tunnel. The monitoring data and the construction safety are analyzed. Regularities of settlement of the shield tunnel are summarized. Suggestion is made on the monitoring range.


2012 ◽  
Vol 452-453 ◽  
pp. 863-867 ◽  
Author(s):  
Shi Song Zhu ◽  
Fu Jing Zhu ◽  
Wen Hui Man

In order to solve the abnormal pattern recognition problem of the sensor monitoring data automatically, a set of method on the time series similarity measurement is used in this paper. Abnormal time series patterns clustering analysis based on the DTW distance is proposed firstly, thus the typical time series patterns can be obtained. From which the important shape indexes can be extracted and filtered based on piecewise shape measure method, then the shape index table can be established. With which a pattern recognition system can be designed used to recognize these abnormal patterns on real-time. As a case, this method has been used in a high gas coal mine and the important promotion application value has been proved in the sensor monitoring field.


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