• Geomechanics and Engineering
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• v.37 no.2
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• pp.179-187
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• 2024

In this paper, a unified time-dependent constitutive model of Darcy flow and non-Darcy flow is proposed. The influencing factors of flow velocity are discussed, which demonstrates that permeability coefficient is the most significant factor. Based on this, the dynamic evolution characteristics of water inrush during tunneling through fault fracture zone is analyzed under the constant permeability coefficient condition (CPCC). It indicates that the curves of flow velocity and hydrostatic pressure can be divided into typical three stages: approximate high-velocity zone inside the fault fracture zone, velocity-rising zone near the tunnel excavation face and attenuation-low velocity zone in the tunnel. Furthermore, given the variation of permeability coefficient of the fault fracture zone with depth and time, the dynamic evolution of water flow in the fault fracture zone under the variable permeability coefficient condition (VPCC) is also studied. The results show that the time-related factor (α) affects the dynamic evolution distribution of flow velocity with time, the depth-related factor (A) is the key factor to the dynamic evolution of hydrostatic pressure.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.113-119
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• 2021

With the increasing pressure of international competition, urban agglomeration cooperation and innovation had become an important means of regional economic development. This study analyzed the spatial characteristics of the Urban Cooperative Innovation Network in Guangdong-Hong Kong-Macao Greater Bay Area, found out the dynamic evolution law of innovation, provided suggestions for policy management departments, and effectively planned the industrial layout. According to the data of the State Intellectual Property Office of China, this study researched invention patents from 2005 to 2019. This paper constructed the urban cooperative innovation network, and took 11 cities in the bay area as the research objects, and used social network analysis to study the spatial structure and dynamic evolution of the urban innovation network. Every indicator reflected the urban cooperative innovation, but they all showed a certain decline in 2008-2010. And it is inferred that the innovation network space of each city will be "obvious fist advantages, significant spillover effect and weakening role of Hong Kong and Macao". This paper divided urban cooperative innovation of Guangdong-Hong Kong-Macao Greater Bay Area into three stages. Summing up the characteristics of each stage is helpful to recognize the changes of urban cooperative innovation and to do a good job in industrial layout planning.

• Smart Structures and Systems
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• v.23 no.3
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• pp.295-306
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• 2019

Shanghai Tower is a 632-meter super high-rise building located in an area with wind and active earthquake. A sophisticated structural health monitoring (SHM) system consisting of more than 400 sensors has been built to carry out a long-term monitoring for its operational safety. In this paper, a reduced-order model including 31 elements was generated from a full model of this super tall building. An iterative regularized matrix method was proposed to tune the system parameters, making the dynamic characteristic of the reduced-order model be consistent with those in the full model. The updating reduced-order model can be regarded as a benchmark model for further analysis. A long-term monitoring for structural dynamic characteristics of Shanghai Tower under different construction stages was also investigated. The identified results, including natural frequency and damping ratio, were discussed. Based on the data collected from the SHM system, the dynamic characteristics of the whole structure was investigated. Compared with the result of the finite element model, a good agreement can be observed. The result provides a valuable reference for examining the evolution of future dynamic characteristics of this super tall building.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.2 s.140
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• pp.73-79
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• 2005

The characteristics of a tilt sensor utilizing the resistance change of an electrolyte associated with inclination is investigated, and a dynamic compensation modeling is proposed to make the real-time measurement of the absolute slope possible even with sporadically dynamic motion. Although the proposed system is small, economical and accurate for quasi-steady slope measurement, since it contains a freesurface the evolution of the liquid surface that has no direct relation to the real slope must be excluded for any rapid rotations or translations. For various artificial motions the response of the sensor is analyzed and simplified modeling equations are proposed.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.439-451
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• 2018

The stress path characteristics of surrounding rock in the formation of gob were analysed and the unloading was solved. Taking Chengchao Iron Mine as the engineering background, the model for analysing the instability of deep gob was established based on the mechanism of stress relief in deep mining. The energy evolution law was investigated by introducing the local energy release rate index (LERR), and the energy criterion of instability of surrounding rock was established based on the cusp catastrophe theory. The results showed that the evolution equation of the local energy release energy of the surrounding rock was quartic function with one unknown and the release rate increased gradually during the mining. The calculation results showed that the gob was stable. The LERR per unit volume of the bottom structure was relatively smaller, which mean the stability was better. The LERR distribution showed that there was main energy release in the horizontal direction and energy concentration in the vertical direction which meet the characteristics of deep mining. In summary, this model could effectively calculate the stability of surrounding rock in the formation of gob. The LERR could reflect the dynamic process of energy release, transfer and dissipation which provided an important reference for the study of the stability of deep mined out area.

• Geomechanics and Engineering
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• v.29 no.4
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• pp.451-461
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• 2022

The roadways surrounded by rock and coal will lose their stability or even collapse under rock burst. Rock burst mainly involves an evolution of dynamic loading which behaves quite differently from static or quasi-static loading. To compare the dynamic response of coal and rocks with different static strengths, three different rocks and bituminous coal were selected for testing at three different dynamic loadings. It's found that the dynamic compression strength of rocks and bituminous coal is much greater than the static compression strength. The dynamic compression strength and dynamic increase factor of the rocks both increase linearly with the increase of the strain rate, while those of the bituminous coal are irregular due to the characteristics of multi-fracture and heterogeneity. Moreover, the absorbed energy of the rocks and bituminous coal both increase linearly with an increase in the strain rate. And the ratio of absorbed energy to the total energy of bituminous coal is greater than that of rocks. With the increase of dynamic loading, the failure degree of the sample increases, with the increase of the static compressive strength, the damage degree also increases. The static compassion strength of the bituminous coal is lower than that of rocks, so the number of small-scale fragments was the largest after bituminous coal rupture.

• Smart Structures and Systems
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• v.19 no.5
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• pp.523-538
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• 2017

A series of field vibration tests are conducted on the Runyang Suspension Bridge during both the construction and operational stages. The purpose of this study is devoted to the analysis of the dynamic characteristics of the suspension tower. After the tower was erected, an array of accelerometers was deployed to study the evolution of its modal parameters during the construction process. Dynamic tests were first performed under the freestanding tower condition and then under the tower-cable condition after the superstructure was installed. Based on the identified modal parameters, the effect of the pile-soil-structure interaction on dynamic characteristics of the suspension tower is investigated. Moreover, the stiffness of the pile foundation is successfully identified using a probabilistic finite model updating method. Furthermore, challenges of identifying the dynamic properties of the tower from the coupled responses of the tower-cable system are discussed in detail. It's found that compared with the identified results from the freestanding tower, the longitudinal and torsional natural frequencies of the tower in the tower-cable system have changed significantly, while the lateral mode frequencies change slightly. The identified modal results from measurements by the structural health monitoring system further confirmed that the vibrations of the bridge subsystems (i.e., the tower, the suspended deck and the main cable) are strongly coupled with one another.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.207-210
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• 1997

The drastic structural changes in telecommunications industry are imposing new strains on operators, regulators and customers. Many researchers have offered diverse frameworks for the changes from the perspectives of sociology, technology, and/or economics. However, there have been few attempts to document the competitive phenomena from management perspectives because of the technological complexities and dynamism in the fundamental transition of competition. In this study, we examine competitive environment in telecommunications industry, and identified five structural elements; telecom operator, competitors, regulation, suppliers, and customer demand. The suggested framework is used to provide a basis for explaining the changes in the characteristics of individual elements and the interactions among them. The dynamic industry-specific changes will be explained through an evolutionary model. We specify the characteristics of progressive stages and the determinants of the evolution. Changes are reviewed in terms of five criteria; regulations and competitions, value chain, technology, customers demand, and internal operations. The result of this study will be useful in analyzing and predicting industry structure and major participants' strategic behaviors and decision patterns. This study can alse be extended to other industries facing dynamic structural changes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.