Spatial Autocorrelation in Soil Compaction and Its Impact on Earthwork Acceptance Testing

The compaction quality of soil embankments is critical to the long-term performance of the pavements placed on them. In current quality assurance (QA) practice, state highway agencies (SHAs) rely on in-situ testing at a small number of point locations to decide whether to accept or reject the product, assuming that the samples taken at random locations are independent of each other. This assumption, however, is invalid because soil properties are spatially autocorrelated – the properties at nearby locations are correlated to each other. Consequently, if the sampling locations are close to each other, the effective number of samples is reduced, which in turn increases the risk of incorrect accept/reject decisions. This study addressed this spatial autocorrelation issue in soil acceptance testing. Soil data from the U.S Highway 31 project, collected by intelligent compaction (IC) in the format of compaction meter value (CMV), were used to prove the existence of spatial autocorrelation using the semivariogram and Moran’s I analysis. The impact of spatial autocorrelation on soil acceptance testing was assessed by comparing the testing power under two scenarios (with and without spatial autocorrelation). The results suggest that the existence of spatial autocorrelation decreases the testing power, resulting in a greater risk to the SHA. Based on these findings, this study proposed two spatial indices to mitigate the negative impact of spatial autocorrelation by controlling the spatial pattern of random samples. A web tool was also developed as an implementation to augment the random sampling process in field QA practice by incorporating the spatial pattern of samples.

Re-Thinking of Data Acquisition Rates in the Era of Expensive Data

It is well known that the mean of a sample converges at a rate of 1/N, where N is the number of samples, assuming that are samples are statistically independent. This paper will show the impact of non-independent samples using real data as well as investigating the efficacy of methods to determine the effective number of samples with non-independent samples are acquired.

Selecting a Cost-Effective Number of Samples To Use at Preselected Locations

In the control of environmental contaminants, it is often useful to sample at preselected locations to determine concentrations and their means. These locations might be on a surface, throughout a room, or outdoors. Applications include air and water pollution control, industrial hygiene, and contamination control in industry. Contamination is a major cause of yield and reliability losses in the microelectronics industry. Sampling the cleanroom environment or sampling the product surfaces can help diagnose and prevent contamination problems, but sampling is becoming increasingly expensive. One wants to use sampling resources effectively to achieve desired low levels of uncertainly. We assume that the locations to be sampled have been selected, perhaps as described by Cooper et al. We show how to calculate the optimal number of samples to be taken at each location so as to minimize the uncertainty in the mean over the entire region under study, subject to a cost constraint. We consider two distinet criteria for measuring this uncertainty. We also address the optimal allocation for minimizing the cost, subject to an upper bound on the standard error. We also discuss the differences between these approximate solutions and the true solutions, which are integers.