锦屏电站地下厂房卸荷岩体参数研究

It is a basic work to ascertain the parameters of rock mass for evaluation about stability of the engineering. Anisotropism、inhomogeneity and discontinuity characters of the rock mass arise from the existing of the structural plane. Subjected to water、weathering effect、off-loading, mechanical characters of the rock mass are greatly different from rock itself, Determining mechanical parameters of the rock mass becomes so difficult because of structure effect、dimension effect、rheological character, ‘Can’t give a proper parameter’ becomes one of big problems for theoretic analysis and numerical simulation. With the increment of project scale, appraising the project rock mass and ascertaining the parameters of rock mass becomes more and more important and strict. Consequently, researching the parameters of rock mass has important theoretical significance and actual meaning. The Jin-ping hydroelectric station is the first highest hyperbolic arch dam in the world under construction, the height of the dam is about 305m, it is the biggest hydroelectric station at lower reaches of Yalong river. The length of underground factory building is 204.52m, the total height of it is 68.83m, the maximum of span clearance is 28.90m. Large-scale excavation in the underground factory of Jin-ping hydroelectric station has brought many kinds of destructive phenomenon, such as relaxation、spilling, providing a precious chance for study of unloading parameter about rock mass. As we all know, Southwest is the most important hydroelectric power base in China, the construction of the hydroelectric station mostly concentrate at high mountain and gorge area, basically and importantly, we must be familiar with the physical and mechanical character of the rock mass to guarantee to exploit safely、efficiently、quickly, in other words, we must understand the strength and deformation character of the rock mass. Based on enough fieldwork of geological investigation, we study the parameter of unloading rock mass on condition that we obtain abundant information, which is not only important for the construction of Jin-ping hydroelectric station, but also for the construction of other big hydroelectric station similar with Jin-ping. This paper adopt geological analysis、test data analysis、experience analysis、theory research and Artificial Neural Networks （ANN） brainpower analysis to evaluate the mechanical parameter, the major production is as follows: (1)Through the excavation of upper 5-layer of the underground powerhouse and the statistical classification of the main joints fractures exposed, We believe that there are three sets of joints, the first group is lay fracture, the second group and the fourth group are steep fracture. These provide a strong foundation for the following calculation of and analysis; (2)According to the in-situ measurement about sound wave velocity、displacement and anchor stress, we analyses the effects of rock unloading effect，the results show a obvious time-related character and localization features of rock deformation. We determine the depth of excavation unloading of underground factory wall based on this. Determining the rock mass parameters according to the measurement about sound wave velocity with characters of low- disturbing、dynamic on the spot, the result can really reflect the original state, this chapter approximately the mechanical parameters about rock mass at each unloading area; (3)Based on Hoek-Brown experienced formula with geological strength index GSI and RMR method to evaluate the mechanical parameters of different degree weathering and unloading rock mass about underground factory, Both of evaluation result are more satisfied; (4)From the perspective of far-field stress, based on the stress field distribution ideas of two-crack at any load conditions proposed by Fazil Erdogan (1962)，using the strain energy density factor criterion (S criterion) proposed by Xue changming(1972)，we establish the corresponding relationship between far-field stress and crack tip stress field, derive the integrated intensity criterion formula under the conditions of pure tensile stress among two line coplanar intermittent jointed rock，and establish the corresponding intensity criterion for the exploratory attempt; (5)With artificial neural network, the paper focuses on the mechanical parameters of rock mass that we concerned about and the whole process of prediction of deformation parameters, discusses the prospect of applying in assessment about the parameters of rock mass，and rely on the catalog information of underground powerhouse of Jinping I Hydropower Station, identifying the rock mechanics parameters intellectually，discusses the sample selection, network design, values of basic parameters and error analysis comprehensively. There is a certain significance for us to set up a set of parameters evaluation system，which is in construction of large-scale hydropower among a group of marble mass.

工作幸福感的结构、测量及作用机制

With the emergence and development of positive psychology, happiness has been the focus of academia and business. However, there is no uniform measure of happiness, because of many different theories of happiness, which are not compatible with others. It bounds the further development of happiness theory. It is also the same with the research of work well-being, which refers to the emotional experience and quality of psychological functioning of employee in the workplace. Subjective well-being (SWB) and psychological well-being (PWB) are two major theories of happiness. Prior research has demonstrated the integration of these two theories theoretically, but still needs more empirical support. Besides, in line with the development of positive psychology, a body of knowledge about positive leadership is advocated. Transformational leadership is treated as one kind of positive leadership, since it emphasizes the leader’s motivational and elevating effect on followers. But the extent to which the transformational leadership can enhance work well-being, and what the mechanism is, these are the questions need to be explored. Based on the integration of SWB and PWB, this research tried to investigate the structure, measurement and mechanism of work well-being, and combining with the theory of transformational leadership, this study also tried to investigate the relationship between transformational leadership and work well-being. The structure and measurement of work well-being, the relationships between work well-being and job characteristics (including job resources and job demands), the relationships among transformational leadership, job resources, work well-being and corresponding outcomes, the relationships among transformational leadership, job demands, work well-being and corresponding outcomes, and the relationships among transformational leadership, group job characteristics, group work well-being and corresponding group outcomes were explored by using content analysis, Subject Matter Experts (SMEs) discussion, and structural questionnaire surveys. More than 7000 subjects were surveyed, and Explore Factor Analysis (EFA), Confirm Factor Analysis (CFA), Structural Equation Modeling (SEM), Hierarchical Linear Modeling (HLM) and other statistics methods were used. The following is the major conclusions. Firstly, work well-being is a two high-order factors structure, which includes affective well-being (AWB) and cognitive well-being (CWB). AWB is similar to SWB, and CWB is similar to PWB. Besides, the construct of AWB includes sub-dimensions of positive emotional experience and negative emotional experience. And the construct of CWB consists of work autonomy, personal growth, work competent, and work significance. Secondly, the relationships between job characteristics and AWB and CWB are different. On one hand job demands are directly related to AWB, and are indirectly related to CWB through the full mediation of AWB, on the other job resources are directly related to CWB, and are indirectly related to AWB through the full mediation of CWB, which means AWB and CWB reciprocally influences each other in the model of job demands-resources. These results were concluded as the process model of work well-being. Thirdly, AWB and CWB are positively related to many workplace outcomes, including job satisfaction, group satisfaction, organizational commitment, turnover intention, job performance, organizational citizenship behavior (OCB), and general psychological health and general physiological health. Fourthly, transformational leadership is indirectly related to CWB through the full mediation of job resources, and is related to AWB through the partial mediation of job demands. Meanwhile, transformational leadership is related to many workplace outcomes through the mediation of job characteristics and work well-being. These results implied that transformational leadership is indeed one kind of positive leadership. Fifthly, in the group level, transformational leadership is indirectly related to group CWB through the full mediation of group job resources, and is related to group AWB through the full mediation of group job demands. Group AWB has positive influence on group CWB, but not vice versa. Group job characteristics and group work well-being fully mediate the relationships between transformational leadership and intragroup cooperation and group performance.