Importance sampling based algorithm for efficient reliability analysis of axially loaded piles

In reliability analysis, the crude Monte Carlo method is known to be computationally demanding. To improve computational efficiency, this paper presents an importance sampling based algorithm that can be applied to conduct efficient reliability evaluation for axially loaded piles. The spatial variability of soil properties along the pile length is considered by random field modeling, in which a mean, a variance, and a correlation length are used to statistically characterize a random field. The local averaging subdivision technique is employed to generate random fields. In each realization, the random fields are used as inputs to the well-established load transfer method to evaluate the load–displacement behavior of an axially loaded pile. Failure is defined as the event where the vertical movement at the pile top exceeds the allowable displacement. By sampling more heavily from the region of interest and then scaling the indicator function back by a ratio of probability densities, a faster rate of convergence can be achieved in the proposed importance sampling algorithm while maintaining the same accuracy as in the crude Monte Carlo method. Two examples are given to demonstrate the accuracy and the efficiency of the proposed method. It is shown that the estimate based on the proposed importance sampling method is unbiased. Furthermore, the size of samples can be greatly reduced in the developed method.     

Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.     

Accuracy and precision of different sampling strategies and flux integration methods for runoff water: comparisons based on measurements of the electrical conductivity

Because of their fast response to hydrological events, small catchments show strong quantitative and qualitative variations in their water runoff. Fluxes of solutes or suspended material can be estimated from water samples only if an appropriate sampling scheme is used. We used continuous in‐stream measurements of the electrical conductivity of the runoff in a small subalpine catchment (64 ha) in central Switzerland and in a very small (0·16 ha) subcatchment. Different sampling and flux integration methods were simulated for weekly water analyses. Fluxes calculated directly from grab samples are strongly biased towards high conductivities observed at low discharges. Several regressions and weighted averages have been proposed to correct for this bias. Their accuracy and precision are better, but none of these integration methods gives a consistently low bias and a low residual error. Different methods of peak sampling were also tested. Like regressions, they produce important residual errors and their bias is variable. This variability (both between methods and between catchments) does not allow one to tell a priori which sampling scheme and integration method would be more accurate. Only discharge‐proportional sampling methods were found to give essentially unbiased flux estimates. Programmed samplers with a fraction collector allow for a proportional pooling and are appropriate for short‐term studies. For long‐term monitoring or experiments, sampling at a frequency proportional to the discharge appears to be the best way to obtain accurate and precise flux estimates. Copyright © 2006 John Wiley & Sons, Ltd.     

Sensitivity analysis of geologic computer models: A formal procedure based on Latin hypercube sampling

Mathematical models in which a response variableY is calculated, usually via a computer program, as a function of input variablesX ₁ ,X ₂, ...,X _k are encountered frequently in earth sciences literature. In many cases, uncertainty in the knowledge of the correct values of the input variables exists, leading to uncertainty in the correct value of the response. Sensitivity analysis procedures can be used to identify which input variable uncertainties contribute most to uncertainty in the response variable.This paper presents the technique of Latin hypercube sampling, a structured, formal sampling process used in the sensitivity analysis procedure. Output resulting from the sample is analyzed using stepwise regression analysis, leading to identification of key input variables. Future data gathering activities can then be done efficiently, focusing on these variables. The technique is applied to a model, developed by Sudicky and Frind, which represents the flow of a contaminant through a porous media.     

Some consequences of applying lognormal theory to pseudolognormal distributions

A logarithmic transformation may be used to improve the efficiency of estimates of the mean when observations follow the lognormal distribution. But if this transformation is applied to observations that follow another distribution, bias may be introduced. We consider some consequences of erroneously applying lognormal estimation theory and demonstrate that biased estimates may be obtained for certain classes of distributions. Illustrations of bias obtained in gold sampling are given.     

Sequential sampling to contour an uncertain function

Sampling exploration of uncertain functions to locate critical contour levels is most effective if sampling decisions are made sequentially. A simple sequential exploration strategy, based on pseudo-Bayesian second-moment analysis, is proposed and compared with non-sequential systematic sampling. Repeated application to functions simulated pseudorandomly from stationary random processes on the line and on the plane indicates uniform superiority of the sequential strategy. The method is particularly advantageous when the function of interest,h(X), has an uncertain trend, and in general when the random process that quantifies prior uncertainty onh(X) is highly correlated.     

Preliminary evaluation of the occurrence of herbicides and PAHs in the Wet Tropics region of the Great Barrier Reef, Australia, using passive samplers

The proximity of the Great Barrier Reef (GBR) Marine Park to areas of intensive agriculture and increasing urbanisation places the park under potential threat of contamination by land-based pollutants. Passive samplers were deployed at inshore reef and river mouth sites in the Wet Tropics region of the GBR during a dry and a wet season to measure levels of land-based organic pollutants in this environment. Two types of passive sampling devices were deployed: (i) a polar sampler, which can be used to monitor polar herbicides and (ii) semipermeable membrane devices (SPMDs) which sequester more hydrophobic compounds (e.g. PAHs, chlorpyrifos). Herbicides (diuron, simazine, atrazine, hexazinone and/or flumeturon) were detected at low concentrations (ng L⁻¹) at all sites sampled and in both seasons. Chlorpyrifos was not detected while PAHs were present in SPMDs at levels below limits of detection. The results show that the GBR environment does contain low levels of organic pollutants and that passive sampling provides a sensitive monitoring tool for measuring waterborne organic pollutants.     

A simple method to vertically stabilize line-suspended,lightweight percussion coring equipment

An inexpensive improvement to lightweight percussion coring systems is presented. Non-stretching lines are often required to facilitate core sampling in hundreds of metres of water, unfortunately such lines are usually negatively buoyant, which creates difficulties in keeping lightweight core assemblies upright while lowering through the water column. The addition of urethane closed-cell foam to the core head creates buoyancy that vertically stabilizes the coring assembly within the water column and prevents the core barrel from contacting the sediment-water interface non-vertically.     

利用粒子滤波进行动态精密单点定位观测异常影响控制

采用粒子滤波控制观测异常影响,提高动态精密单点定位精度。粒子滤波是一种非高斯噪声分布的动态滤波,通过重点概率密度进行随机采样以获取高精度状态参数;根据观测噪声概率密度、状态噪声概率密度以及重点概率密度等因素确定粒子权值,降低受污染粒子对定位结果的影响;采用Kalman滤波进行重点采样,减缓粒子退化;采用单差无电离层固定模糊度,减少状态参数维数,进而减少粒子的选取个数。实测数据结果表明,粒子滤波有效控制了观测异常影响,提高了动态精密单点定位的精度。