For deep tunnel projects,selecting an appropriate initial support distance is critical to improving the self-supporting capacity of surrounding rock.In this work,an intuitive method for determining the tunnel’s initi...For deep tunnel projects,selecting an appropriate initial support distance is critical to improving the self-supporting capacity of surrounding rock.In this work,an intuitive method for determining the tunnel’s initial support distance was proposed.First,based on the convergence-confinement method,a three-dimensional analytical model was constructed by combining an analytical solution of a non-circular tunnel with the Tecplot software.Then,according to the integral failure criteria of rock,the failure tendency coefficients of hard surrounding rock were computed and the spatial distribution plots of that were constructed.On this basis,the tunnel’s key failure positions were identified,and the relationship between the failure tendency coefficient at key failure positions and their distances from the working face was established.Finally,the distance from the working face that corresponds to the critical failure tendency coefficient was taken as the optimal support distance.A practical project was used as an example,and a reasonable initial support distance was successfully determined by applying the developed method.Moreover,it is found that the stability of hard surrounding rock decreases rapidly within the range of 1.0D(D is the tunnel diameter)from the working face,and tends to be stable outside the range of 1.0D.展开更多
Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensembl...Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensemble learning is an effective method of reducing the classifmation error of the classifier, this paper proposes a double-layer Bayesian classifier ensembles （DLBCE） algorithm based on frequent itemsets. DLBCE constructs a double-layer Bayesian classifier （DLBC） for each frequent itemset the new instance contained and finally ensembles all the classifiers by assigning different weight to different classifier according to the conditional mutual information. The experimental results show that the proposed algorithm outperforms other outstanding algorithms.展开更多
In order to study the stress characteristics of the initial support and secondary lining of the large section tunnel and to solve the problem of secondary lining cracking during operation. Taking the large section tun...In order to study the stress characteristics of the initial support and secondary lining of the large section tunnel and to solve the problem of secondary lining cracking during operation. Taking the large section tunnel in Zihong village, Qi County as the research object, a numerical simulation method was used to establish a finite element model of the large section tunnel. So as to simulate and analyze the stress characteristics of the support structure of this tunnel. Through the simulation of the initial support and second lining of this large section tunnel in terms of displacement, stress, plastic zone damage and anchor shaft force, the results show that as the excavation progresses, the stress and displacement on the surface of the newly excavated tunnel profile is faster, especially at the side walls and arch footings, the stress and displacement values are slightly larger than other characteristic points, but the final values are stable and converge, and are basically consistent with the field monitoring results, which indicates that this support system is basically in stable state. Therefore, during the tunnel excavation and support process, special attention should be paid to the stability of the sidewalls and footings, and the results of this study will be of great practical significance for tunnel construction and maintenance.展开更多
基金Project(2021JLM-49) supported by Natural Science Basic Research Program of Shaanxi-Joint Fund of Hanjiang to Weihe River Valley Water Diversion Project,ChinaProject(42077248) supported by the National Natural Science Foundation of China
文摘For deep tunnel projects,selecting an appropriate initial support distance is critical to improving the self-supporting capacity of surrounding rock.In this work,an intuitive method for determining the tunnel’s initial support distance was proposed.First,based on the convergence-confinement method,a three-dimensional analytical model was constructed by combining an analytical solution of a non-circular tunnel with the Tecplot software.Then,according to the integral failure criteria of rock,the failure tendency coefficients of hard surrounding rock were computed and the spatial distribution plots of that were constructed.On this basis,the tunnel’s key failure positions were identified,and the relationship between the failure tendency coefficient at key failure positions and their distances from the working face was established.Finally,the distance from the working face that corresponds to the critical failure tendency coefficient was taken as the optimal support distance.A practical project was used as an example,and a reasonable initial support distance was successfully determined by applying the developed method.Moreover,it is found that the stability of hard surrounding rock decreases rapidly within the range of 1.0D(D is the tunnel diameter)from the working face,and tends to be stable outside the range of 1.0D.
基金supported by National Natural Science Foundation of China (Nos. 61073133, 60973067, and 61175053)Fundamental Research Funds for the Central Universities of China(No. 2011ZD010)
文摘Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensemble learning is an effective method of reducing the classifmation error of the classifier, this paper proposes a double-layer Bayesian classifier ensembles （DLBCE） algorithm based on frequent itemsets. DLBCE constructs a double-layer Bayesian classifier （DLBC） for each frequent itemset the new instance contained and finally ensembles all the classifiers by assigning different weight to different classifier according to the conditional mutual information. The experimental results show that the proposed algorithm outperforms other outstanding algorithms.
文摘In order to study the stress characteristics of the initial support and secondary lining of the large section tunnel and to solve the problem of secondary lining cracking during operation. Taking the large section tunnel in Zihong village, Qi County as the research object, a numerical simulation method was used to establish a finite element model of the large section tunnel. So as to simulate and analyze the stress characteristics of the support structure of this tunnel. Through the simulation of the initial support and second lining of this large section tunnel in terms of displacement, stress, plastic zone damage and anchor shaft force, the results show that as the excavation progresses, the stress and displacement on the surface of the newly excavated tunnel profile is faster, especially at the side walls and arch footings, the stress and displacement values are slightly larger than other characteristic points, but the final values are stable and converge, and are basically consistent with the field monitoring results, which indicates that this support system is basically in stable state. Therefore, during the tunnel excavation and support process, special attention should be paid to the stability of the sidewalls and footings, and the results of this study will be of great practical significance for tunnel construction and maintenance.
