一种基于改进的一致性算法数据融合技术

在重大工程结构健康监测中,随着研究对象复杂程度的提高,往往需要获得大量观测数据才能对结构进行有效的评估,因此采用多种或多个传感器进行测量已成为必然趋势。数据融合技术就是将多个传感器的测量结果进行综合处理,从而得出比单个传感器更为准确可靠的结果。本文基于一致性算法,提出一种改进的多传感器数据融合技术,该数据融合技术属于数据级融合,它克服了一致性算法中两传感器在测量精度不同时置信距离不同的缺点,并对支持矩阵进行模糊化处理,避免了人为定义阈值而产生的主观误差。文中通过算例,验证了此方法可获得较好的结果,并且能够有效地减小由于扰动因素造成的测量数据的变化。     

一般工程中深孔爆破技术的初步探讨

中深孔爆破技术在一般工程中得到最为广泛的推广和应用,合理地确定中深孔爆破的各项技术参数和有效地控制爆破中产生的有害效应,能够取得理想的工程效益.中深孔爆破的各项技术参数之间存在着相互关联性,寻找他们之间的内在联系,可以合理地优化爆破方案.中深孔爆破产生的有害效应有规律可寻,只要掌握和合理地应用,能够降低和消除中深孔爆破产生的有害效应.     

军工危险性建筑物外部安全距离美国和我国标准对比分析

针对军工危险性建筑物外部安全距离确定时药量覆盖不全面、新产品无依据、厂房用地与城市发展用地矛盾等问题,对美国标准和我国标准中安全距离的分类、确定原则、确定方法进行了对比分析,提出了对我国标准修订的建议,为我国军工危险性建筑物外部安全距离标准的制修订提供借鉴。     

基于不同指标无量纲化方法的岩爆预测模型优选

为研究不同指标无量纲化方法对岩爆等级预测模型精度的影响,提高岩爆预测准确率,选取应力系数、脆性系数和弹性能量指数作为预测指标。基于104组岩爆实例大样本数据,采用统一极差处理法、差异化极差处理法、平均化处理法和归一化处理法4种指标无量钢化方法,对预测指标的原始数据进行处理,建立不同的岩爆预测距离判别模型并进行工程实例应用。研究结果表明:基于平均化处理法的岩爆预测模型的回判准确率高达97.1%;对不同矿山、隧道和水电站的6个工程实例的预测结果符合实际情况,说明其是一种准确率高、方便实用的岩爆预测模型。     

自动扶梯(人行道)安全距离测量专用工具箱的设计

在自动扶梯(人行道)的检验过程中,关于安全距离和尺寸的测量项目众多,特别是针对防夹装置、防护挡板以及扶手带周围距离等项目的检验,烦琐复杂且费时费力.本文简要讨论了国内自动扶梯典型事故、自动扶梯(人行道)安全距离和尺寸的检验难点以及目前市场上的解决方案,开发出专用工具箱,从而有效提高检验准确率和效率.     

Climate Change Impacts on Water Resources in the Upper Blue Nile River Basin,Ethiopia1

Kim, Ungtae and Jagath J. Kaluarachchi, 2009. Climate Change Impacts on Water Resources in the Upper Blue Nile River Basin, Ethiopia. Journal of the American Water Resources Association (JAWRA) 45(6):1361‐1378. Abstract: Climate change affects water resources availability of international river basins that are vulnerable to runoff variability of upstream countries especially with increasing water demands. The upper Blue Nile River Basin is a good example because its downstream countries, Sudan and Egypt, depend solely on Nile waters for their economic development. In this study, the impacts of climate change on both hydrology and water resources operations were analyzed using the outcomes of six different general circulation models (GCMs) for the 2050s. The outcomes of these six GCMs were weighted to provide average future changes. Hydrologic sensitivity, flow statistics, a drought index, and water resources assessment indices (reliability, resiliency, and vulnerability) were used as quantitative indicators. The changes in outflows from the two proposed dams (Karadobi and Border) to downstream countries were also assessed. Given the uncertainty of different GCMs, the simulation results of the weighted scenario suggested mild increases in hydrologic variables (precipitation, temperature, potential evapotranspiration, and runoff) across the study area. The weighted scenario also showed that low‐flow statistics and the reliability of streamflows are increased and severe drought events are decreased mainly due to increased precipitation. Joint dam operation performed better than single dam operation in terms of both hydropower generation and mean annual storage without affecting the runoff volume to downstream countries, but enhancing flow characteristics and the robustness of streamflows. This study provides useful information to decision makers for the planning and management of future water resources of the study area and downstream countries.     

Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho1

Stratton, Benjamin T., Venakataramana Sridhar, Molly M. Gribb, James P. McNamara, and Balaji Narasimhan, 2009. Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho. Journal of the American Water Resources Association (JAWRA) 45(6):1390‐1408. Abstract: The distributed Soil Water Assessment Tool (SWAT) hydrologic model was applied to a research watershed, the Dry Creek Experimental Watershed, near Boise Idaho to investigate its water balance components both temporally and spatially. Calibrating and validating SWAT is necessary to enable our understanding of the water balance components in this semiarid watershed. Daily streamflow data from four streamflow gages were used for calibration and validation of the model. Monthly estimates of streamflow during the calibration phase by SWAT produced satisfactory results with a Nash Sutcliffe coefficient of model efficiency 0.79. Since it is a continuous simulation model, as opposed to an event‐based model, it demonstrated the limited ability in capturing both streamflow and soil moisture for selected rain‐on‐snow (ROS) events during the validation period between 2005 and 2007. Especially, soil moisture was generally underestimated compared with observations from two monitoring pits. However, our implementation of SWAT showed that seasonal and annual water balance partitioning of precipitation into evapotranspiration, streamflow, soil moisture, and drainage was not only possible but closely followed the trends of a typical semiarid watershed in the intermountain west. This study highlights the necessity for better techniques to precisely identify and drive the model with commonly observed climatic inversion‐related snowmelt or ROS weather events. Estimation of key parameters pertaining to soil (e.g., available water content and saturated hydraulic conductivity), snow (e.g., lapse rates, melting), and vegetation (e.g., leaf area index and maximum canopy index) using additional field observations in the watershed is critical for better prediction.     

Impact of Watershed Subdivision and Soil Data Resolution on SWAT Model Calibration and Parameter Uncertainty1

Abstract: Impact of watershed subdivision and soil data resolution on Soil Water Assessment Tool (SWAT) model calibration and parameter uncertainty is investigated by creating 24 different watershed model configurations for two study areas in northern Indiana. SWAT autocalibration tool is used to calibrate 14 parameters for simulating seven years of daily streamflow records. Calibrated parameter sets are found to be different for all 24 watershed configurations, however in terms of calibrated model output, their effect is minimal. In some cases, autocalibration is followed by manual calibration to correct for low flows, which were underestimated during autocalibration. In addition to normal validation using four years of streamflow data for each calibrated parameter set, cross‐validation (using a calibrated parameter set from one model configuration to validate observations using another configuration) is performed to investigate the effect of different model configurations on streamflow prediction. Results show that streamflow output during cross‐validation is not affected, thus highlighting the non‐unique nature of calibrated parameters in hydrologic modeling. Finally, parameter uncertainty is investigated by extracting good parameter sets during the autocalibration process. Parameter uncertainty analysis suggests that significant parameters show very narrow range of uncertainty across different watershed configurations compared with nonsignificant parameters. Results from recalibration of some configurations using only six significant parameters were comparable to that from calibration using 14 parameters, suggesting that including fewer significant parameters could reduce the uncertainty arising from model parameters, and also expedite the calibration process.     

Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1

Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross‐validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3‐fold cross‐validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.     

城市空气质量模糊评价新方法

在权广义距离概念的基础上提出一种新的城市空气质量模糊评价方法,评价过程能充分利用实际数据所包含的各种信息.与模糊综合评判模型的同一应用实例结果进行比较,表明文章提出的评价方法更为有效.