基于AT89C51的小功率调频调压电源设计

为满足钻井导向系统非接触供电单元开发设计需要,设计一种小功率调压、调频电源.采用智能控制芯片AT89C51作为控制核心部件,自振式反激变换器实现调压模块设计,全桥逆变电路实现调频模块设计,通过IR2110芯片实现驱动.控制芯片产生PWM控制信号,控制开关管的导通并在直流侧加入合适的整流电容使电压平稳输出,经隔离驱动电路实现电能的通断控制,实现电源电压、频率和幅值可调.实验结果表明:该电源具有过电流、过电压保护功能,设计结构合理,成本低廉,控制精度较高,达到设计要求.     

海上静力触探(CPT)测试技术的发展现状和应用

静力触探(CPT)是一种速度快,数据连续、再现性好、操作省力等优点的原位测试方法。随着海洋开发的迅速发展,CPT测试技术在国内外海洋工程领域的使用越来越普遍。因此,了解国外海上CPT设备和应用情况,探讨应用中的问题,对CPT测试技术在我国海上工程领域的应用具有重要意义。文章描述了国外海上静力触探(CPT)测试技术的发展现状,阐明了主要的海床CPT(Seabed CPT)设备和井下CPT(Downhole CPT)设备的特点,并对不同CPT设备在海洋工程领域的应用作了分析,这对海上CPT测试技术在我国的应用和发展具有一定的参考价值和指导作用。     

利用GPS掩星弯曲角确定对流层顶高度

利用COSMIC提供的GPS无线电掩星观测数据,从掩星弯曲角廓线出发确定对流层顶高度,并分别与对应的掩星温度廓线确定的温度最低点对流层顶(cold point tropopause,CPT)和温度递减率对流层顶(lapse rate tropopause,LRT)、无线电探空(距离掩星事件300km以内、观测时间差异3h以内)温度廓线确定的CPT和LRT进行了对比。     

长春硬塑状态老黏性土地基承载力

为提供长春硬塑状态老黏性土可靠的地基承载力依据,探讨适宜的确定地基承载力的方法,采用载荷试验、静力触探试验原位测试和室内土工试验等方法对长春老黏性土进行了测试研究,获取了地基承载力特征值（306~425 kPa）、静力触探锥尖阻力（1.9~3.3 MPa）、天然孔隙比(0.63~0.72)和液性指数(0.02~0.25)等试验数据。通过对试验数据的统计分析,得出地基承载力特征值与静力触探锥尖阻力线性相关,并与天然孔隙比和液性指数线性相关的结论。据此,提出了根据静力触探锥尖阻力计算长春老黏性土地基承载力特征值的经验公式,总结出根据室内土工试验指标确定长春老黏性土地基承载力特征值的数据表。     

CPT-Based estimation of undrained shear strength of fine-grained soils in the Huanghe River Delta

The Huanghe River（Yellow River） Delta has a wide distribution of fine-grained soils. Fluvial alluviation, erosion,and wave loads affect the shoal area, resulting complex physical and mechanical properties to sensitive fine-grained soil located at the river-sea boundary. The cone penetration test（CPT） is a convenient and effective in situ testing method which can accurately identify various soil parameters. Studies on undrained shear strength only roughly determine the fine content（FC） without ma...     

干拌水泥碎石桩加固路基现场试验研究

针对京珠高速公路漯周界K421＋642-K421＋975段路基病害问题,采用干拌水泥碎石桩予以加固。为了研究其加固效果,现场试验段分为5个区,每区6根桩,桩长6m,成孔直径150mm,桩间距1m×1m,重锤120kg,每个区桩的每层虚填料高度、夯击次数不同。通过载荷试验、动力触探试验检测结果对比分析得出以下结论：每层虚填料高度小于40cm,夯击7次时,桩体达到密实状态,单桩承载力极限值达到400kPa,满足设计要求;通过静力触探、瑞利波测试及室内试验对比,分析加固前后路基土的变化,发现加固后路基含水量降低,路基的承载力和沉降得到改善。运行路基没有再出现唧泥、翻浆、路面病害等问题。上述分析和治理方法可为类似工程病害治理提供参考。     

Estimating spatial correlation structures based on CPT data

Spatial correlation structures are usually used while modelling inherent uncertainty associated with soil properties. To retrieve this information a sufficient number of measurements and equal intervals between measurements or samples are always required. However, in practice, it is not usual to conduct site investigation in that way subject to a limited budget. Therefore, it is of interest to understand the possible bias created by using samples with unequal intervals. This research focuses on estimating the vertical spatial correlation structures of cone penetration tests (CPT) data conducted at a liquefaction site in Taiwan. The appropriate fitting range used in calibrating the correlation model is also discussed. The results show that cone tip resistance has larger correlation distances than that of sleeve friction. The estimated correlation distances of the CPT profiles are similar while using an equal sampling interval within a certain fitting range (denoted as the critical interval of measurements in this research). The results indicate that the effect of using different curve-fitting ranges is relatively insignificant for correlation distances of sleeve friction, whereas the correlation distances of cone tip resistance are more sensitive to different curve-fitting ranges. Moreover, the mean values of correlation distances are more constant for the equal sampling interval cases than for the unequal interval cases.     

Bayesian approach for calibrating transformation model from spatially varied CPT data to regular geotechnical parameter

Cone Penetration Test (CPT) is widely utilized to gain regular geotechnical parameters such as compression modulus, cohesion coefficient and internal friction angle by transformation model in the site investigation. However, it is challenging to obtain simultaneously the unknown coefficients and error of a transformation model, given the intrinsic uncertainty (i.e., spatial variability) of geomaterial and the epistemic uncertainty of geotechnical investigation. A Bayesian approach is therefore proposed calibrating the transformation model based on spatial random field theory. The approach consists of three key elements: (1) three-dimensional anisotropic spatial random field theory; (2) classifications of measurement and error, and the uncertainty propagation diagram of geotechnical investigation; and (3) the unknown coefficients and error calibration of the transformation model given Bayesian inverse modeling method. The massive penetration resistance data from CPT, which is denoted as a spatial random field variable to account for the spatial variability of soil, are classified as type A data. Meanwhile, a few laboratory test data such as the compression modulus are defined as type B data. Based on the above two types of data, the unknown coefficients and error of the transformation model are inversely calibrated with consideration of intrinsic uncertainty of geomaterial, epistemic uncertainties such as measurement errors, prior knowledge uncertainty of transformation model itself, and computing uncertainties of statistical parameters as well as Bayesian method. Baseline studying indicates the proposed approach is applicable to calibrate the transformation model between CPT data and regular geotechnical parameter within spatial random field theory. Next, the calibrated transformation model was compared with classical linear regression in cross-validation, and then it was implemented at three-dimensional site characterization of the background project.     

CPT-based seismic stability assessment of a hazardous waste site

In areas of high seismicity, seismic stability often controls hazardous and solid waste landfill closure design. The undrained shear strength (S_u) of the waste mass is fundamental to seismic slope stability analyses. The value of S_u for hazardous waste fill is often difficult to characterize. The physical and chemical natures of the waste fill typically preclude laboratory testing of the materials. In certain cases, Cone Penetration Test (CPT) soundings can provide a viable technique for evaluation of S_u provided that the cone shear strength factor N_k can be established. If hazardous waste materials laboratory testing is not an option, N_k may be evaluated based upon results of non-intrusive in situ testing. This paper presents a case history of the seismic stability assessment of a hazardous waste site in which N_k was established from the results of non-intrusive Spectral Analysis of Surface Waves (SASW) soundings and empirical correlations to shear strength of soils. Generalization of the proposed methodology to other sites should be done with caution owing to variability among the parameters used in the analyses.     

Predicting standard penetration test N-value from cone penetration test data using artificial neural networks

Standard Penetration Test(SPT) and Cone Penetration Test(CPT) are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N-value.In contrast,CPT is becoming more popular for site investigation and geotechnical design.Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network(ANN) model was developed to predict the N6o-value from CPT data.Data used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand soils.The ANN model input variables are:CPT tip resistance(q_c),effective vertical stress(σ'_v),and CPT sleeve friction(f_s).A different set of SPT-CPT data was used to check the reliability of the developed ANN model.It was shown that ANN model either under-predicted the N_(60)-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N_(60)-value from CPT data with acceptable accuracy.