Soil mixing is a procedure that has proven to be effective for loose or soft compressible soils. The method stabilizes the soil in-place using specialized augers, tillers, or paddles that inject grout or dry cementitious powders as part of the mixing process. The Federal Highway Administration design manual for soil mixing helps to estimate the required amount of cementitious binder to produce a target design strength. However, it is biased towards inorganic soils and only mentions caution when confronting organic soils which usually come with a high water table, moisture content and void volume. The Swedish Deep Stabilization Research Centre cited studies with highly organic soils in regards to soil mixing and suggested that organic soils may need to reach a ‘threshold’ of cement content before strength gain can occur. The University of South Florida also conducted a study on highly organic soils and was able to confirm this concept. USF also proposed a threshold selection curve based on the organic content. This thesis extends this concept to the bench scale testing of multiple full scale field studies. This thesis will conclude with the presentation of new threshold curves based on the new data from the added field case studies. Given that there were variable binders and soil types used in the data analyzed, these threshold curves are dependent upon soil type and binder type, thus expanding upon the curve previously suggested.

No description available.

Piles have been used since prehistoric times in areas with weak subsurface conditions either to reinforce existing ground, create new ground for habitation or trade, and support bridges and buildings. Originally piles were composed of timber and driven with drop hammers using very heavy ram weights. As technology improved so did the materials that piles are composed of as well as the equipment itself. Currently, piling is a multibillion dollar a year industry, thus the need to develop more accurate prediction methods can potentially represent a significant savings in cost, material, and man power. Multiple predictive methods have been developed to estimate developed pile capacity. These range from static theoretical formulae based on geotechnical investigation prior to pile driving even occurring using specific pile and hammer types to semi empirically based dynamic formulae used during actual driving operations to more recently developed computer modeling and signal matching programs which are calibrated with site condition during initial geotechnical investigations or test piling to full scale static load tests where piles are loaded to some predetermined value or failure condition. In this thesis, dynamic formulae are used to predict pile capacity from those installed by drop and diesel hammers and are compared to the results from pile load tests, which are taken as the true measure of developed bearing capacity. The dynamic formulae examined are the Engineering News Record (ENR), Gates, Federal Highway Administration (FHWA) modified Gates, Hiley, and Ontario Ministry of Transportation (MTO) modified Hiley formulae. Methods of investigation include calculating pile capacities from the formulae as they are, omitting the factors of safety, revising the formulae with averaged coefficients and conducting multi regression analysis to solve for one or two coefficients simultaneously and revising the dynamic formula to determine if more accurate bearing capacity prediction are possible. To objectively determine which formulae provide the most accurate bearing capacities, the predicted capacities will be compared to results obtained from static pile load tests and simple statistics on the resulting data set will be calculated including regression analysis, standard deviations, coefficients of variation, coefficients of determination, and correlation values.

Piles have been used since prehistoric times in areas with weak subsurface conditions either to reinforce existing ground, create new ground for habitation or trade, and support bridges and buildings. Originally piles were composed of timber and driven with drop hammers using very heavy ram weights. As technology improved so did the materials that piles are composed of as well as the equipment itself. Currently, piling is a multibillion dollar a year industry, thus the need to develop more accurate prediction methods can potentially represent a significant savings in cost, material, and man power. Multiple predictive methods have been developed to estimate developed pile capacity. These range from static theoretical formulae based on geotechnical investigation prior to pile driving even occurring using specific pile and hammer types to semi empirically based dynamic formulae used during actual driving operations to more recently developed computer modeling and signal matching programs which are calibrated with site condition during initial geotechnical investigations or test piling to full scale static load tests where piles are loaded to some predetermined value or failure condition. In this thesis, dynamic formulae are used to predict pile capacity from those installed by drop and diesel hammers and are compared to the results from pile load tests, which are taken as the true measure of developed bearing capacity. The dynamic formulae examined are the Engineering News Record (ENR), Gates, Federal Highway Administration (FHWA) modified Gates, Hiley, and Ontario Ministry of Transportation (MTO) modified Hiley formulae. Methods of investigation include calculating pile capacities from the formulae as they are, omitting the factors of safety, revising the formulae with averaged coefficients and conducting multi regression analysis to solve for one or two coefficients simultaneously and revising the dynamic formula to determine if more accurate bearing capacity prediction are possible. To objectively determine which formulae provide the most accurate bearing capacities, the predicted capacities will be compared to results obtained from static pile load tests and simple statistics on the resulting data set will be calculated including regression analysis, standard deviations, coefficients of variation, coefficients of determination, and correlation values.

The Interactive Highway Safety Design Model (IHSDM) is a suite of software developed by the Federal Highway Administration (FHWA) for monitoring and analyzing two-lane rural highways in the United States. As IHSDM is a fairly "young" program a limited amount of research has been conducted to evaluate its practicability and reliability. To determine if IHSDM can be adopted into the engineering decision making process in Utah, a study was conducted under the supervision of the Utah Department of Transportation (UDOT) to evaluate its applicability to audit safety of two-lane rural highways in Utah. IHSDM consists of six modules: Policy Review Module (PRM), Crash Prediction Module (CPM), Design Consistency Module (DCM), Traffic Analysis Module (TAM), Intersection Review Module (IRM), and Driver/Vehicle Module (DVM) (still under construction). Among the six modules, two were chosen for evaluation because of their applicability to audit safety of the two-lane rural highways in Utah, namely CPM and IRM. For the evaluation of the CPM, three two-lane rural highway sections were selected. The results of this evaluation show that the CPM can produce reasonably reliable crash predictions if appropriate input data, especially alignment data, reflect the existing conditions at reasonable accuracy and engineering judgment is used. Using crash records available from UDOT's crash database and CPM's crash prediction capability, UDOT's traffic and safety engineers can locate "hot spots" for detailed safety audit, thus making the safety audit task more focused and effective. Unlike the CPM, the outputs of the IRM are qualitative and include primarily suggestions and recommendations. They will help the traffic and safety engineers identify what to look for as they visit the sites, such as a lack of stopping sight distance and a lack of passing sight distance. The interpretation of the IRM requires knowledge of various aspects of highway design, familiarity with A Policy on Geometric Design of Highways and Streets by the American Association of State Highway and Transportation Officials (AASHTO), and experience in traffic engineering. Based on the findings of the study, it is concluded that the CPM and IRM of IHSDM could be a useful tool for engineering decision-making during safety audits of two-lane rural highways. But the outputs from these modules demand knowledge and experience in highway design. It is recommended that the other modules of IHSDM be tested to fully appreciate the capability of IHSDM. The software can be a knowledgebased program that can help novice engineers to learn how to design safe two-lane rural highways.

No description available.

<div>This research analyzes over 60,000 awarded highway contracts from 18 states throughout the United States. Analysis was performed on the state and aggregate level. The contracts were awarded from the years 2008 through 2018. Statistical analysis utilizing Pearson's Correlation and Ordinary Least Squares regression for each sample was performed to identify each variables relationship between the budget and awarded values.</div><div>The research examined effects of economic indicators, contractor descriptors and yearly/seasonal adjustments These variables included DBE Participation Goal, Number of Bidders, Project Dollar Value, Project Duration, Unemployment Rate, S&P 500 Index, Volatility Index, quarter, and year of project award. The results were examined by using a combination of simple statistical summaries and econometric coefficients called a cost vector. <br></div><div>Summary statistics observed Bid Difference at 8.5% below the Engineer's Estimate. The study observed DBE Participation Goals averaged 3.74% of the value of contracts, with an observed average of 4.5 bidders per contract. <br></div><div>The research determined that 55% of observed states had a positive significant correlation with DBE Participation Goal and Bid Difference. This correlation translated to nearly $80 million in additional cost. In addition, the research determined that all 19 groups in this study had a negative significant correlation with the Number of Bidders. The correlation translated to a savings of nearly $500 million. <br></div>