An Experimental Validation of a Statistical-Based Damage Detection Approach, January 2011

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.

Optimization of Snow Drifting Mitigation and Control Methods for Iowa Conditions Part II, TR-626, 2015

Blowing and drifting of snow is a major concern for transportation efficiency and road safety in regions where their development is common. One common way to mitigate snow drift on roadways is to install plastic snow fences. Correct design of snow fences is critical for road safety and maintaining the roads open during winter in the US Midwest and other states affected by large snow events during the winter season and to maintain costs related to accumulation of snow on the roads and repair of roads to minimum levels. Of critical importance for road safety is the protection against snow drifting in regions with narrow rights of way, where standard fences cannot be deployed at the recommended distance from the road. Designing snow fences requires sound engineering judgment and a thorough evaluation of the potential for snow blowing and drifting at the construction site. The evaluation includes site-specific design parameters typically obtained with semi-empirical relations characterizing the local transport conditions. Among the critical parameters involved in fence design and assessment of their post-construction efficiency is the quantification of the snow accumulation at fence sites. The present study proposes a joint experimental and numerical approach to monitor snow deposits around snow fences, quantitatively estimate snow deposits in the field, asses the efficiency and improve the design of snow fences. Snow deposit profiles were mapped using GPS based real-time kinematic surveys (RTK) conducted at the monitored field site during and after snow storms. The monitored site allowed testing different snow fence designs under close to identical conditions over four winter seasons. The study also discusses the detailed monitoring system and analysis of weather forecast and meteorological conditions at the monitored sites. A main goal of the present study was to assess the performance of lightweight plastic snow fences with a lower porosity than the typical 50% porosity used in standard designs of such fences. The field data collected during the first winter was used to identify the best design for snow fences with a porosity of 50%. Flow fields obtained from numerical simulations showed that the fence design that worked the best during the first winter induced the formation of an elongated area of small velocity magnitude close to the ground. This information was used to identify other candidates for optimum design of fences with a lower porosity. Two of the designs with a fence porosity of 30% that were found to perform well based on results of numerical simulations were tested in the field during the second winter along with the best performing design for fences with a porosity of 50%. Field data showed that the length of the snow deposit away from the fence was reduced by about 30% for the two proposed lower-porosity (30%) fence designs compared to the best design identified for fences with a porosity of 50%. Moreover, one of the lower-porosity designs tested in the field showed no significant snow deposition within the bottom gap region beneath the fence. Thus, a major outcome of this study is to recommend using plastic snow fences with a porosity of 30%. It is expected that this lower-porosity design will continue to work well for even more severe snow events or for successive snow events occurring during the same winter. The approach advocated in the present study allowed making general recommendations for optimizing the design of lower-porosity plastic snow fences. This approach can be extended to improve the design of other types of snow fences. Some preliminary work for living snow fences is also discussed. Another major contribution of this study is to propose, develop protocols and test a novel technique based on close range photogrammetry (CRP) to quantify the snow deposits trapped snow fences. As image data can be acquired continuously, the time evolution of the volume of snow retained by a snow fence during a storm or during a whole winter season can, in principle, be obtained. Moreover, CRP is a non-intrusive method that eliminates the need to perform man-made measurements during the storms, which are difficult and sometimes dangerous to perform. Presently, there is lots of empiricism in the design of snow fences due to lack of data on fence storage capacity on how snow deposits change with the fence design and snow storm characteristics and in the estimation of the main parameters used by the state DOTs to design snow fences at a given site. The availability of such information from CRP measurements should provide critical data for the evaluation of the performance of a certain snow fence design that is tested by the IDOT. As part of the present study, the novel CRP method is tested at several sites. The present study also discusses some attempts and preliminary work to determine the snow relocation coefficient which is one of the main variables that has to be estimated by IDOT engineers when using the standard snow fence design software (Snow Drift Profiler, Tabler, 2006). Our analysis showed that standard empirical formulas did not produce reasonable values when applied at the Iowa test sites monitored as part of the present study and that simple methods to estimate this variable are not reliable. The present study makes recommendations for the development of a new methodology based on Large Scale Particle Image Velocimetry that can directly measure the snow drift fluxes and the amount of snow relocated by the fence.

Development of LRFD Procedures for Bridge Pile Foundations in Iowa Volume III: Recommended Resistance Factors with Consideration of Construction Control and Setup, TR-584, 2012

The Federal Highway Administration (FHWA) mandated utilizing the Load and Resistance Factor Design (LRFD) approach for all new bridges initiated in the United States after October 1, 2007. As a result, there has been a progressive move among state Departments of Transportation (DOTs) toward an increased use of the LRFD in geotechnical design practices. For the above reasons, the Iowa Highway Research Board (IHRB) sponsored three research projects: TR-573, TR-583 and TR-584. The research information is summarized in the project web site (http://srg.cce.iastate.edu/lrfd/). Two reports of total four volumes have been published. Report volume I by Roling et al. (2010) described the development of a user-friendly and electronic database (PILOT). Report volume II by Ng et al. (2011) summarized the 10 full-scale field tests conducted throughout Iowa and data analyses. This report presents the development of regionally calibrated LRFD resistance factors for bridge pile foundations in Iowa based on reliability theory, focusing on the strength limit states and incorporating the construction control aspects and soil setup into the design process. The calibration framework was selected to follow the guidelines provided by the American Association of State Highway and Transportation Officials (AASHTO), taking into consideration the current local practices. The resistance factors were developed for general and in-house static analysis methods used for the design of pile foundations as well as for dynamic analysis methods and dynamic formulas used for construction control. The following notable benefits to the bridge foundation design were attained in this project: 1) comprehensive design tables and charts were developed to facilitate the implementation of the LRFD approach, ensuring uniform reliability and consistency in the design and construction processes of bridge pile foundations; 2) the results showed a substantial gain in the factored capacity compared to the 2008 AASHTO-LRFD recommendations; and 3) contribution to the existing knowledge, thereby advancing the foundation design and construction practices in Iowa and the nation.

Embankment Quality and Assessment of Moisture Control Implementation, TR-677, 2016

A specification for contractor moisture quality control (QC) in roadway embankment construction has been in use for approximately 10 years in Iowa on about 190 projects. The use of this QC specification and the development of the soils certification program for the Iowa Department of Transportation (DOT) originated from Iowa Highway Research Board (IHRB) embankment quality research projects. Since this research, the Iowa DOT has applied compaction with moisture control on most embankment work under pavements. This study set out to independently evaluate the actual quality of compaction using the current specifications. Results show that Proctor tests conducted by Iowa State University (ISU) using representative material obtained from each test section where field testing was conducted had optimum moisture contents and maximum dry densities that are different from what was selected by the Iowa DOT for QC/quality assurance (QA) testing. Comparisons between the measured and selected values showed a standard error of 2.9 lb/ft3 for maximum dry density and 2.1% for optimum moisture content. The difference in optimum moisture content was as high as 4% and the difference in maximum dry density was as high as 6.5 lb/ft3 . The difference at most test locations, however, were within the allowable variation suggested in AASHTO T 99 for test results between different laboratories. The ISU testing results showed higher rates of data outside of the target limits specified based on the available contractor QC data for cohesive materials. Also, during construction observations, wet fill materials were often observed. Several test points indicated that materials were placed and accepted at wet of the target moisture contents. The statistical analysis results indicate that the results obtained from this study showed improvements over results from previous embankment quality research projects (TR-401 Phases I through III and TR-492) in terms of the percentage of data that fell within the specification limits. Although there was evidence of improvement, QC/QA results are not consistently meeting the target limits/values. Recommendations are provided in this report for Iowa DOT consideration with three proposed options for improvements to the current specifications. Option 1 provides enhancements to current specifications in terms of material-dependent control limits, training, sampling, and process control. Option 2 addresses development of alternative specifications that incorporate dynamic cone penetrometer or light weight deflectometer testing into QC/QA. Option 3 addresses incorporating calibrated intelligent compaction measurements into QC/QA.