高强钢筋增强UHPC-NC组合柱抗震性能研究

为探讨高强钢筋增强UHPC-NC组合柱抗震性能,基于大型有限元程序ABAQUS,结合UHPC、NC和高强钢筋材料本构关系,校准损伤塑性模型中相关参数,建立高强钢筋增强UHPC-NC组合柱抗震有限元模型。通过与3个NC柱和3个UHPC柱拟静力试验结果对比,验证分析模型的有效性。在此基础上,进一步探讨轴压比、纵筋直径、纵筋强度、箍筋间距和UHPC高度等敏感参数对高强钢筋增强UHPC-NC组合柱抗震性能的影响。结果表明,高强钢筋增强UHPC-NC组合柱位移延性系数随轴压比、纵筋直径和箍筋间距的增大而降低,随纵筋强度和UHPC高度的增加表现出先增大后逐渐平缓的趋势,合适的UHPC替换高度能充分发挥高强钢筋和UHPC材料特性并取得良好的经济性。     

Developing a novel hybrid model for seismic loss prediction of regional-scale buildings

Bulletin of Earthquake Engineering - Seismic loss prediction of regional-scale buildings provides key information for disaster management. However, previous seismic loss prediction models ignored...     

江陵凹陷新生代玄武岩水-岩反应模拟及其对富钾卤水成因的指示

关于江陵凹陷富钾卤水的物质来源和成因机理仍未查明. 对凹陷新生代玄武岩开展矿物学、岩石学、地球化学研究以及水-岩反应模拟实验,重点研究时间、温度、流体成分等对水-岩反应的影响. 结果表明,卤水具有高锂低镁的特征,说明其在成因上受到火山活动的影响;研究区岩浆发生不同程度的分异,玄武岩蚀变作用强烈,表明地下热液的交代作用强烈,玄武岩通过水-岩反应为富钾卤水矿提供了物质来源;温度是流体对元素的淋滤能力的主要控制因素,高盐度流体是各成矿元素主要的迁移载体,水-岩反应是卤水形成的重要过程.      

Influence of climate factors on spatial distribution of Texas cattle breeds

This paper investigates the spatial distribution of cattle breeders in Texas to quantify how climate factors influence cattle breed selection. A multivariate probit model is employed to examine the county-level binary choices of Bos taurus, Bos indicus and composite breeds derived from cattle breed association membership data. The estimation results suggest that summer heat stress is a significant factor for breed selection: positive for Bos indicus and negative for Bos taurus and composite breeds, with the average marginal effects on breed membership probability being 9.7 %, ?26.5 % and ?7.9 %, respectively. The intensity of the summer heat impacts can lead to noteworthy changes in spatial distributions of Texas cattle breeds in the event of climate change.     

Climate change influences on agricultural research productivity

This paper investigates the impacts of climate change on US returns to research investments on agricultural productivity. We examine this using a historical data set in a panel time-series econometric model of state agricultural productivity. The fitted model allows derivation of the rate of return to research investments and the effects of climate change thereon. We find climate change is altering the rate of return to public agricultural research in a spatially heterogeneous manner. Increases in precipitation raise returns to research, while the impact of higher temperatures varies by region, are negative in Southern areas, particularly the Southern Plains, and positive in northern areas. We simulate the impact of projected climate change and find cases where agricultural productivity is reduced, for example in the Southern Plains. Finally, we consider the amount of research investment that is needed to adapt to overcome the impacts of climate change on agricultural productivity. Under the 2100 scenario, a 7–17 % increase in total US research investment is needed to adapt, but effects by region differ greatly—some requiring little changes and the Southern Plain requiring an increase as high as 57 %.     

Assessing the institutional capacity to adapt to climate change: a case study in the Cambodian health and water sectors

Institutional capacity is an important element for climate change adaptation (CCA) and the development of such capacity is a great challenge in a Least Developed Country like Cambodia where resources are limited. An important first step to increasing capacity is via an understanding of the level of existing capacity; future priorities can then be subsequently identified. This study aimed to assess the capacity of organizations to implement climate change activities in Cambodia in order to provide such a basis for building capacity. Four elements of capacity were investigated in this research: (1) financial resources, (2) cooperation and coordination of stakeholders, (3) availability and quality of information on vulnerability and adaptation to climate change, and (4) the level of understanding of climate change vulnerability and adaptation. The data were collected through semistructured interviews with a wide range of government and non-government informants across a number of sectors. Results of the study showed that informants perceived capacity for CCA to be very constrained, especially in terms of financial resources and cooperation, and addressing these factors was ranked as the highest climate change capacity priority. Institutional capacity constraints were considered to relate more generally to weak governance of CCA. In light of our research findings, the absence of local higher education institutions in CCA activities should be addressed. The support of such institutions would provide an important mechanism to progress both capacity development as well as partnerships and coordination between different types of organizations and relevant sectors.

Policy relevance

Capacity for CCA within Cambodian health and water sectors was perceived to be very constrained across a range of interdependent factors. Increasing funding was ranked as the highest priority for building capacity for CCA; however, governance factors such as ‘improved cooperation’ were also ranked highly. Improving stakeholders' awareness of the availability of adaptation funds and resources, and their responsiveness to funding criteria, is an important implication of our research, as is improving the mobilization of local resources and the private sector. To address the issue of weak cooperation among stakeholders, improving the coordination function of the National Climate Change Committee (NCCC) regarding stakeholder engagement and capacity building is crucial. Ensuring that CCA activities are based on sound information and knowledge from across different disciplines and, importantly, include the perspectives of vulnerable people themselves, ultimately underpins and supports the realization of the above priorities.     

Eagle Nebula Pillars: From Models to Observations

Over the past few years, our group has been developing hydrodynamic models to simulate formation of the Eagle Nebula pillars. The true test of any model is, of course, how well it can reproduce the observations. Here, we discuss how we go about testing our models against observations. We describe the process by which we “observe” the model data to create synthetic maps. We show an example of this technique using one of our model runs and compare the resultant synthetic map to the real one.