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Indiana University-Purdue University Indianapolis (IUPUI) / Between 1890 and 1930 in the city of Indianapolis, people in charge made certain decisions regarding infrastructure—the character and condition of streets and sidewalks, the provision of sewer services and garbage collection, the location of the city’s dump, and the placement of the city’s sewage treatment plant—that resulted in long-term health and safety consequences. In Indianapolis, as in most modern American cities, some neighborhoods are less healthy for their inhabitants than others. The least healthy neighborhoods—those with the highest rates of cancer, for example—are situated on the city’s southwest side. The southwest side of Indianapolis is also the location of the landfill, the sewage treatment plant, and much heavy industry. The entire city is at the mercy of an ill-designed sewer system, a system that taxpayers are spending millions annually to repair. The years from 1890 to 1930 saw the genesis of this state of affairs. In the city of Indianapolis since 1890, infrastructure has separated people from nature and from each other on two levels: its operational level, wherein it was an objective entity that performed according to its design, and its subjective level, where it operated as a social and hygienic barrier. Streets, curbs, sewers, and sidewalks are useful and necessary elements of public health and safety. We both want and need these elements to ensure our separation from things that are dangerous, such as speeding cars and contaminated water. When government officials exercise power to declare what parts of the city street are accessible to whom, or which neighborhoods will have a wastewater treatment plant, a landfill, or heavy industry nearby, infrastructure can work to separate people.

Developments that have been taking place on Cal Poly campus over the years have altered the natural hydrology of the area. Stormwater management practices could help reduce the impacts of these developments. Computer models can help to design effective and economical stormwater management solutions at a watershed scale. As such, the objective of this study was to develop a stormwater management model for Cal Poly campus. The model was developed based on the utility data obtained from the university and other watershed data available from open sources. Field surveys were conducted to address some anomalies in the utility data, and streamflow monitoring was performed. The model was calibrated using the streamflow data measured during this study. The calibration effort significantly improved the prediction accuracy of the model. The calibrated model was then used to analyze the hydrologic performance of implementing LID systems for two projects that Cal Poly plans to build. Permeable Pavements (PPs) and Bioretention Cells (BRCs) were the LID types examined. The LIDs were evaluated based on peak flow and runoff volume reductions they would achieve. The potential reductions were compared for current conditions and the proposed project if LIDs were implemented, and for inflows to the LIDs and outflows from the LIDs. The results indicate that implementing a PP system for the proposed student apartment at the current H-1 and R-1 parking lots and a BRC system for the proposed engineering project facilities at the current H-2 parking lots will significantly reduce peak flow and runoff volume. Overall, the developed model will help the university with the traditional stormwater management practices such as flood control and to identify effective LID practices for future developments. Limitations of the current model and recommendations on how to improve the model are also discussed.

The Utah Department of Transportation (UDOT) has fully implemented the Mechanistic-Empirical Pavement Design Guide for pavement design but has been using primarily level-three design inputs obtained from correlations to aggregate base materials developed at the national level. UDOT was interested in investigating correlations between laboratory measurements of resilient modulus, California bearing ratio (CBR), and other material properties specific to base materials commonly used in Utah; therefore, a statewide testing program was needed. The objectives of this research were to 1) determine the resilient modulus of several representative aggregate base materials in Utah and 2) investigate correlations between laboratory measurements of resilient modulus, CBR, and other properties of the tested materials. Two aggregate base materials were obtained from each of the four UDOT regions. Important material properties, including particle-size distribution, soil classification, and the moisture-density relationship, were investigated for each of the sampled aggregate base materials. The CBR and resilient modulus of each aggregate base material were determined in general accordance with American Society for Testing and Materials D1883 and American Association of State Highway and Transportation Officials T 307, respectively. After all of the data were collected, several existing models were evaluated to determine if one or more of them could be used to predict the resilient modulus values measured in this research. Statistical analyses were also performed to investigate correlations between measurements of resilient modulus, CBR, and other properties of the tested aggregate base materials, mainly including aspects of the particle-size distributions and moisture-density relationships. A set of independent predictor variables was analyzed using both stepwise regression and best subset analysis to develop a model for predicting resilient modulus. After a suitable model was developed, it was analyzed to determine the sensitivity of the model coefficients to the individual data points. For the aggregate base materials tested in this research, the average resilient modulus varied from 16.0 to 25.6 ksi. Regarding the correlation between resilient modulus and CBR, the test results show that resilient modulus and CBR are not correlated for the materials tested in this research. Therefore, a new model was developed to predict the resilient modulus based on the percent passing the No. 200 sieve, particle diameter corresponding to 30 percent finer, optimum moisture content, maximum dry density (MDD), and ratio of dry density to MDD. Although the equation may not be applicable for values outside the ranges of the predictor variables used to develop it, it is expected to provide UDOT with reasonable estimates of resilient modulus values for aggregate base materials similar to those tested in this research.

While cement treatment is a proven method for improving the strength and durability of soils and aggregates, cement hydration causes shrinkage strains in the cement-treated base (CTB) that can lead to reflection cracking in asphalt surfaces. Cracking may then cause increased pavement roughness and lead to poor ride quality. The overall purpose of this research was to utilize data collected through the Long-Term Pavement Performance (LTPP) program to investigate the use and classification of CTB layers and evaluate the relative impact of cement content on the development of roughness and cracking in asphalt concrete (AC) pavements constructed using CTB layers. The data included 52 LTPP test sites, which represented 13 different states and one Canadian province, with cement contents ranging from 3.0 to 9.5 percent by weight of dry aggregate. Statistical procedures were utilized to identify the factors that were most correlated to the observed pavement performance and to develop prediction equations that transportation agencies can use to estimate the amount of roughness for a given pavement at a given age and the amount of distress associated with a particular crack severity level for a given pavement. The data collected for this study suggest that wide ranges of cement contents are used to stabilize soils within individual American Association of State Highway and Transportation Officials soil classifications. The data also suggest that CTBs comprising flexible pavement structures are constructed mainly on rural facilities. A backward-selection model development technique was used to develop sets of prediction equations for roughness and cracking. Age, AC thickness, CTB thickness, and cement content were determined to be significant predictors of International Roughness Index, while age, air freezing index, AC thickness, CTB thickness, cement content, and traffic loads in thousands of equivalent single-axle loads were determined to be significant predictors of low-severity, medium-severity, and high-severity block, fatigue, longitudinal (wheel-path and non-wheel-path), and transverse cracking in AC pavements constructed using CTB layers. Investigation of the relationships between CTB modulus and the development of roughness and cracking is recommended for further study.

Frost heave damage to roadways costs millions of dollars every year. The need for an improved understanding of the fundamental mechanisms associated with frost heave and methods for efficiently improving frost-susceptible materials prompted the Department of Civil and Environmental Engineering at Brigham Young University (BYU) to undertake a project to design, construct, and verify the functionality of a new frost heave testing apparatus. Frost heave research has been carried out for more than 75 years. The equipment used to conduct this testing has advanced in accuracy and utility over the years. To establish a background in past and current frost heave research, a survey of 12 frost heave devices, including their construction and capabilities, was performed in this research. Several design objectives were then delineated, and a nine-specimen frost heave device was designed and constructed to meet the specifications. The apparatus uses one collective heat source and one collective heat sink for all nine specimens. Heave data and temperature data are collected electronically, while the weights of the specimens before and after frost heave testing are measured manually. Preparatory tests were conducted to confirm the functionality of the data acquisition systems, the uniformity of conditions experienced by all specimens, and the replication of natural roadway freezing conditions. Once preparatory testing was complete, a full-scale frost heave test was performed using the apparatus to investigate the efficacy of cement stabilization in reducing the frost susceptibility of a Montana silt and to validate the functionality of the finished device. Results from the testing indicate that adding 2.0 percent cement actually induces frost heave in excess of that exhibited by the untreated soil. However, additions of 3.5 percent and 5.0 percent were found to be effective in preventing frost heave. Although minor, recommendations for further improvements to the frost heave apparatus include provisions to further decrease the thermal gradient across the specimen and installation of an automatic temperature control device for the water source.

At the turn of the century, the City of Orlando initiated the "Neighborhood Horizon Program." This program involved local citizens to help improve their community resources by engaging in a process of planning where the problems associated with the communities were identified. Many residents favored to bring back the brick roads that were overlaid with asphalt concrete to provided better transportation in the mid 1900s. With majority of the neighborhood streets already bricked, removing asphalt ensured safety, served as a technique for slowing traffic, and added to the historical integrity. Since there were no official documentations available that stated the definite existence of bricks beneath the asphalt surface course, it would have been rather impossible to core hundreds of locations to ensure the whereabouts of these anomalies. Thus, without time delays and excessive coring costs, a nondestructive instrumentation of Ground Penetrating Radar (GPR) was employed in the detection of bricks. This geophysical survey system distinguishes materials based on their different electrical properties that depend upon temperature, density, moisture content and impurities by providing a continuous profile of the subsurface conditions. The Ground Penetrating Radar operates on the principle of the electromagnetic wave (EMW) theory. The main objectives of this study was to investigate the existing pavement by using Ground Penetrating Radar (GPR) in detecting the brick base and to analyze the performance of pavement system for fatigue and rutting. The results of this study will assist the City of Orlando in removing asphalt layer, rebuilding of brick roads, and facilitate in better zoning and planning of the city. The construction of controlled test area provided with a good sense of brick detection, which helped in precise locations bricks for sections of Summerlin Avenue, Church Street and Cherokee Drive. The project demonstrated a good sense of detecting the subsurface anomalies, such as bricks. The validation of the profile readings was near to a 100%.

Use of pervious concrete pavement as a method of stormwater management has shown great promise in previous studies. Reduction in runoff, water quality improvements, and long-term economic benefits are but a few of its many advantages. Regulatory agencies such as the St. Johns River Water Management District require further research into the performance of pervious concrete pavement before granting credits for its use as a best management practice in controlling stormwater. As a part of a larger series of studies by UCF's Stormwater Management Academy, this thesis studies the hydrologic mass balance of pervious concrete pavement in sandy soil common in Florida. In order to conduct this study, a field experiment was constructed at the UCF Stormwater Field Lab. The experiment consisted of three 4-foot tall cylindrical polyethylene tanks with 30-inch diameters. All three tanks were placed into the side of a small embankment and fitted with outlet piping and piezometers. The test tanks were assembled by laying a 6-inch layer of gravel into the bottom of each tank, followed by a layer of Mirafi geofabric, followed by several feet of fine sand into which soil moisture probes were laid at varying depths. Two of the tanks were surfaced with 6-inch layers of portland cement pervious concrete, while the third tank was left with a bare sand surface. Mass balance was calculated by measuring moisture influx and storage in the soil mass. Data collection was divided into three phases. The first phase ran from August to November 2005. Moisture input consisted of normal outdoor rainfall that was measured by a nearby rain gauge, and storage was calculated by dividing the soil mass into zones governed by soil moisture probes. The second phase ran for two weeks in March 2006. Moisture input consisted of water manually poured onto the top of each tank in controlled volumes, and storage was calculated by using probe readings to create regression trendlines for soil moisture profiles. The third phase followed the procedure identical to the second phase and was conducted in the middle of April 2006. Data tabulation in this study faced several challenges, such as nonfunctional periods of time or complete malfunction of essential measuring equipment, flaws in the method of calculating storage in phase one of the experiment, and want of more data points to construct regression trendlines for soil moisture calculation in phases two and three of the experiment. However, the data in all phases of the experiment show that evaporation volume of the tanks with pervious concrete surfacing was nearly twice that of the tank with no concrete. Subsequent infiltration experiments showed that pervious concrete pavement is capable of retaining a portion of precipitation volume, reducing infiltration into the underlying soil and increasing total evaporation in the system.

Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number of user-defined parameters. A technical challenge with crack extraction algorithms, including the Bottom-hat transform, is that extracted crack pixels are usually fragmented along crack paths. For de-fragmenting those crack pixels, a novel crack-grouping algorithm is proposed as an image segmentation method, so called MorphLink-C. Statistical validation of this method using flexible pavement images indicated that MorphLink-C not only improves crack-detection accuracy but also reduces crack detection time. Crack characterization was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand the statistical characteristics of cracks in different deterioration stages. Statistical significance of crack features was ranked based on their relevancy and redundancy. The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance. Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application.

This research explores the red light running phenomena and offer a better understanding of the factors associated with it. The red light running is a type of traffic violation that can lead to angle crash and the most common counter measure is installing a red light running cameras. Red light running cameras some time can reduce the rates of red light running but because of the increased worry of the public towards crossing the intersection it can cause an increase in rear end crashes. Also the public opinion of the red light running cameras is that they are a revenue generator for the local counties and not a concern of public safety. Further more, they consider this type of enforcement as violation of privacy. There was two ways to collect the data needed for the research. One way is through a tripod cameras setup temporarily placed at the intersection. This setup can collect individual vehicles caught in the change phase with specific information about their reactions and conditions. This required extensive manual analysis for the recorded videos plus data could not be collected during adverse weather conditions. The second way was using traffic monitoring cameras permanently located at the site to collect red light running information and the simultaneous traffic conditions. This system offered more extensive information since the cameras monitor the traffic 24/7 collecting data directly. On the other hand this system lacked the ability to identify the circumstances associated with individual red light running incidents. The research team finally decided to use the two methods to study the red light running phenomena aiming to combine the benefits of the two systems. During the research the team conducted an experiment to test a red light running countermeasure in the field and evaluate the public reaction and usage of this countermeasure. The marking was previously tested in a driving simulator and proved to be successful in helping the drivers make better stop/go decisions thus reducing red light running rates without increasing the rear-end crashes. The experiment was divided into three phases; before marking installation called "before", after marking installation called "after", and following a media campaign designed to inform the public about the use of the marking the third phase called "after media" The behavior study that aimed at analyzing the motorist reactions toward the signal change interval identified factors which contributed to red light running. There important factors were: distance from the stop bar, speed of traffic, leading or following in the traffic, vehicle type. It was found that a driver is more likely to run red light following another vehicle in the intersection. Also the speeding vehicles can clear the intersection faster thus got less involved in red light running violations. The proposed "Signal Ahead" marking was found to have a very good potential as a red light running counter measure. The red light running rates in the test intersection dropped from 53 RLR/hr/1000veh for the "before" phase, to 24 RLR/hr/1000veh for the "after media" phase. The marking after media analysis period found that the marking can help the driver make stop/go decision as the dilemma zone decreased by 50 ft between the "before" and the "after media" periods. Analysis of the traffic condition associated with the red light running it revealed that relation between the traffic conditions and the red light running is non-linear, with some interactions between factors. The most important factors included in the model were: traffic volume, average speed of traffic, the percentage of green time, the percentage of heavy vehicles, the interaction between traffic volume and percentage of heavy vehicles. The most interesting finding was the interaction between the volume and the percent of heavy vehicles. As the volume increased the effect of the heavy vehicles reversed from reducing the red light running to increasing the red light. This finding may be attributed to the sight blocking that happens when a driver of a passenger car follows a larger heavy vehicle, and can be also explained by the potential frustration experienced by the motorist resulting from driving behind a bigger vehicle.