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Mesoscopic properties of dense granular materials: An overview
Qicheng SUN, Feng JIN, Guohua ZHANG
Frontiers of Structural and Civil Engineering. 2013, 7 (1): 1-12.
https://doi.org/10.1007/s11709-013-0184-z
A granular material is a conglomeration of discrete solid particles. It is intrinsically athermal because its dynamics always occur far from equilibrium. In highly excited gaseous states, it can safely be assumed that only binary interactions occur and a number of kinetic theories have been successfully applied. However, for granular flows and solid-like states, the theory is still poorly understood because of the internally correlated structures, such as particle clusters and force networks. The current theory is that the mesoscale characteristics define the key differences between granular materials and homogeneous solid materials. Widespread interest in granular materials has arisen among physicists, and significant progress has been made, especially in understanding the jamming phase diagram and the characteristics of the jammed phase. In this paper, the underlying physics of the mesoscale structure is discussed in detail. A multiscale framework is then proposed for dense granular materials.
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Characterization on jointed rock masses based on PFC2D
Peitao WANG, Tianhong YANG, Qinglei YU, Honglei LIU, Penghai ZHANG
Frontiers of Structural and Civil Engineering. 2013, 7 (1): 32-38.
https://doi.org/10.1007/s11709-013-0187-9
Geometrical parameters of discontinuities, such as spacing, length, dip and fault throw between joints have a great influence on the mechanical behavior of jointed rock masses. Accurate characterization for discontinuities is important for investigate the stability of rock masses. In this paper, the PFC2D is combined with joint network generation method to examine the mechanical behaviors of jointed mass. Taking Miaogou Open-pit Mine as an example, the information and statistical distributions of discontinuities of the slope rock masses are measured by ShapeMetriX3D measuring tool. Then, the automatic generation algorithm of random joints network based on the Monte-Carlo method is proposed using the programming language (FISH) embedded within PFC2D. This algorithm could represent the discontinuities compared with the geological surveys. In simulating the compression test of a jointed rock sample, the mechanical behavior and crack propagation were investigated. The results reveal that the failure mode and crack propagation of the jointed rock are dominated by the distribution of joints in addition to the intact rock properties. The simulation result shows the feasibility of the joints generating method in application to jointed rock mass.
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Study of bond strength between various grade of Ordinary Portland Cement (OPC) and Portland Pozzolane Cement (PPC) mixes and different diameter of TMT bars by using pullout test
A D POFALE, S P WANJARI
Frontiers of Structural and Civil Engineering. 2013, 7 (1): 39-45.
https://doi.org/10.1007/s11709-013-0193-y
Since last two decades, the Portland Pozzolane Cement (PPC) is extensively used in structural concrete. But, till to date, a few literature is available on bond strength of concrete using PPC mixes. There are many literatures available on bond strength of concrete mixes using Ordinary Portland Cement (OPC). Hence, a comparative study was conducted on bond strength between OPC and PPC mixes. In the present investigation, total 24 samples consisting of M20, M35 and M50 grades of concrete and 16 and 25 mm diameter of TMT bar were tested for 7 and 28 days. The pullout bond test was conducted on each specimen as per IS: 2770-1967/1997 [1] and the results were observed at 0.25 mm slip at loaded end called as critical bond stress and at maximum bond load called as maximum bond stress. It was observed that the critical bond strength of PPC mixes is 10% higher than OPC mixes. Whereas, marginal improvement was noticed in maximum bond strength of PPC mixes. Hence, based on these findings, it could be concluded that development length for PPC mixes could be reduced by 10% as compared with same grade of OPC mixes.
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Impacts of opening baffle of city road tunnels on natural ventilation performance
Weixiao YANG, Jincheng XING, Jianxing LI, Jihong LING, Haixian HAO, Zhiqiang YAN
Frontiers of Structural and Civil Engineering. 2013, 7 (1): 55-61.
https://doi.org/10.1007/s11709-013-0194-x
Based on the opening baffle mode for natural ventilation of city road tunnels, this paper studies the impacts of opening baffle on natural ventilation performance by verifying numerical simulation through model tests. By analyzing the impacts of installation angle, dimension, location, and quantity of opening baffle on ventilation performance, the paper reached the conclusions as follows: 1) When installation angle is larger than 45° and tunnel ventilation is well operated, the baffle exhaust could increase by at least 30% compared to when there is no baffle. 2) The baffle reaches its optimal performance when the length of the baffle is equal to the width of the city road tunnels. 3) Baffle exhaust could increase by 30% when it is installed in the downstream of openings. 4) The performance of a single baffle is better than that of multiple baffles.
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Liquefaction prediction using support vector machine model based on cone penetration data
Pijush SAMUI
Frontiers of Structural and Civil Engineering. 2013, 7 (1): 72-82.
https://doi.org/10.1007/s11709-013-0185-y
A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance (qc) and cyclic stress ratio (CSR), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters (qc and maximum horizontal acceleration amax), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor (C) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.
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