Effects of conifer sawdust, hardwood sawdust, and peat on soil properties and a barefoot conifer seedling development

Organic amendments are commonly used to improve tree nursery soil conditions for increased seedling growth. However, few studies compare organic amendments effects on soil conditions, and fewer compare subsequent effects on seedling growth. The effects of three organic amendments on soil properties and seedling growth were investigated at the USDA Forest Service J.W. Toumey Nursery in Watersmeet, MI. Pine sawdust (red pine, Pinus resinosa), hardwood sawdust (maple, Acer spp. and aspen, Populus spp.), and peat were individually incorporated into a loamy sand nursery soil in August, 2006, and soil properties were sampled periodically for the next 14 months. Jack (Pinus banksiana), red, and white pine (Pinus strobus) were sown into test plots in June, 2007 and sampled for growth responses at the end of the growing season. It is hypothesized; pine sawdust and peat can be used as a satisfactory soil amendment to improve soil conditions and produce high quality seedlings, when compared to hardwood sawdust in bareroot nursery soils. This study has the potential to reduce nursery costs while broadening soil amendment options. The addition of peat and pine sawdust increased soil organic matter above control soil conditions after 14 months. However, hardwood sawdust-amended soils did not differ from control soils after same time period. High N concentrations in peat increased total soil N over the other treatments. Similarly, the addition of peat increased soil matric potential and available water over all other treatments. Seedlings grew tallest with the largest stem diameter, and had the largest biomass in both control soil and soil amended with peat, compared to either sawdust treatment. Seedlings grown in peat-amended soils had higher N concentrations than those grown in soils treated with pine sawdust, though neither was different from seedlings grown in control or hardwood sawdust-amended soils. Overall, peat is a well suited organic soil amendment for the enhancement of soil properties, but no amendments were able to increase one-year seedling growth over control soils.

EVALUATION OF INTELLIGENT COMPACTION CONTROL IN THE M-189 RECONSTRUCTION PROJECT AT IRON RIVER, MICHIGAN

This research evaluated an Intelligent Compaction (IC) unit on the M-189 highway reconstruction project at Iron River, Michigan. The results from the IC unit were compared to several traditional compaction measurement devices including Nuclear Density Gauge (NDG), Geogauge, Light Weight Deflectometer (LWD), Dynamic Cone Penetrometer (DCP), and Modified Clegg Hammer (MCH). The research collected point measurements data on a test section in which 30 test locations on the final Class II sand base layer and the 22A gravel layer. These point measurements were compared with the IC measurements (ICMVs) on a point-to-point basis through a linear regression analysis. Poor correlations were obtained among different measurements points using simple regression analysis. When comparing the ICMV to the compaction measurements points. Factors attributing to the weak correlation include soil heterogeneity, variation in IC roller operation parameters, in-place moisture content, the narrow range of the compaction devices measurement ranges and support conditions of the support layers. After incorporating some of the affecting factors into a multiple regression analysis, the strength of correlation significantly improved, especially on the stiffer gravel layer. Measurements were also studied from an overall distribution perspective in terms of average, measurement range, standard deviation, and coefficient of variance. Based on data analysis, on-site project observation and literature review, conclusions were made on how IC performed in regards to compaction control on the M-189 reconstruction project.

SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.