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Crumb rubber use in asphalt mixtures by means of wet process technology has been in place for several years in the United States with good performance record; however, it has some shortcomings such as maintaining high mixing and compaction temperatures in the field production. Organosilane (OS), a nanotechnology chemical substantially

Crumb rubber use in asphalt mixtures by means of wet process technology has been in place for several years in the United States with good performance record; however, it has some shortcomings such as maintaining high mixing and compaction temperatures in the field production. Organosilane (OS), a nanotechnology chemical substantially improves the bonding between aggregate and asphalt by modifying the aggregate structure from hydrophilic to hydrophobic contributing to increased moisture resistance of conventional asphalt mixtures. Use of Organosilane also reduces the mixing and compaction temperatures and facilitates similar compaction effort at lower temperatures. The objective of this research study was first to perform a Superpave mix design for Crumb Rubber Modified Binder (CRMB) gap-graded mixture with and without Organosilane; and secondly, analyse the performance of CRMB mixtures with and without Organosilane by conducting various laboratory tests. Performance Grade (PG) 64-22 binder was used to create the gap-graded Hot Mix Asphalt (HMA) mixtures for this study. Laboratory tests included rotational viscometer binder test and mixtures tests: dynamic modulus, flow number, tensile strength ratio, and C* fracture test. Results from the tests indicated that the addition of Organosilane facilitated easier compaction efforts despite reduced mixing and compaction temperatures. Organosilane also modestly increased the moisture susceptibility and resistance to crack propagation yet retaining equal rutting resistance of the CRMB mixtures.

ContributorsSrinivasan, Aswin Kumar Kumar (Author) / Kaloush, Kamil (Thesis advisor) / Medina, Jose R. (Jose Roberto) (Committee member) / Mamlouk, Michael S. (Committee member) / Arizona State University (Publisher)
Created2018
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Future autonomous vehicle systems will be diverse in design and functionality since they will be produced by different brands. In the automotive industry, trustworthiness of a vehicle is closely tied to its perceived safety. Trust involves dependence on another agent in an uncertain situation. Perceptions of system safety, trustworthiness, and

Future autonomous vehicle systems will be diverse in design and functionality since they will be produced by different brands. In the automotive industry, trustworthiness of a vehicle is closely tied to its perceived safety. Trust involves dependence on another agent in an uncertain situation. Perceptions of system safety, trustworthiness, and performance are important because they guide people’s behavior towards automation. Specifically, these perceptions impact how reliant people believe they can be on the system to do a certain task. Over or under reliance can be a concern for safety because they involve the person allocating tasks between themselves and the system in inappropriate ways. If a person trusts a brand they may also believe the brand’s technology will keep them safe. The present study measured brand trust associations and performance expectations for safety between twelve different automobile brands using an online survey.

The literature and results of the present study suggest perceived trustworthiness for safety of the automation and the brand of the automation, could together impact trust. Results revelated that brands closely related to the trust-based attributes, Confidence, Secure, Integrity, and Trustworthiness were expected to produce autonomous vehicle technology that performs in a safer way. While, brands more related to the trust-based attributes Harmful, Deceptive, Underhanded, Suspicious, Beware, and Familiar were expected to produce autonomous vehicle technology that performs in a less safe way.

These findings contribute to both the fields of Human-Automation Interaction and Consumer Psychology. Typically, brands and automation are discussed separately however, this work suggests an important relationship may exist. A deeper understanding of brand trust as it relates to autonomous vehicles can help producers understand potential for over or under reliance and create safer systems that help users calibrate trust appropriately. Considering the impact on safety, more research should be conducted to explore brand trust and expectations for performance between various brands.
ContributorsCelmer, Natalie (Author) / Branaghan, Russell (Thesis advisor) / Chiou, Erin (Thesis advisor) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Highway safety is a major priority for the public and for transportation agencies. Pavement distresses directly affect ride quality, and indirectly contribute to driver distraction, vehicle operation, and accidents. In this study, analysis was performed on highways in the states of Arizona, North Carolina and Maryland for years

Highway safety is a major priority for the public and for transportation agencies. Pavement distresses directly affect ride quality, and indirectly contribute to driver distraction, vehicle operation, and accidents. In this study, analysis was performed on highways in the states of Arizona, North Carolina and Maryland for years between 2013 and 2015 in order to investigate the relationship between accident rate and pavement roughness and rutting. Two main types of data were collected: crash data from the accident records and roughness and rut depth data from the pavement management system database in each state. Crash rates were calculated using the U.S. Department of Transportation method, which is the number of accidents per vehicle per mile per year multiplied by 100,000,000. The variations of crash rate with both International Roughness Index (IRI) and rut depth were investigated. Linear regression analysis was performed to study the correlation between parameters. The analysis showed positive correlations between road roughness and rut depth in all cases irrespective of crash severity level. The crash rate data points were high for IRI values above 250-300 inches/mile in several cases. Crash road segments represent 37-48 percent of the total length of the network using 1-mile segments. Roughness and rut depth values for crash and non-crash segments were close to each other, suggesting that roughness and rutting are not the only factors affecting number of crashes but possibly in combination with other factors such as traffic volume, human factors, etc. In summary, it can be concluded that both roughness and rut depth affect crash rate and highway maintenance authorities should maintain good pavement condition in order to reduce crash occurrences.
ContributorsVinayakamurthy, Mounica (Author) / Mamlouk, Michael S. (Thesis advisor) / Underwood, Benjamin (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2017
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Description
Vehicular automation and autonomy are emerging fields that are growing at an

exponential rate, expected to alter the very foundations of our transportation system within the next 10-25 years. A crucial interaction has been born out this new technology: Human and automated drivers operating within the same environment. Despite the well-

Vehicular automation and autonomy are emerging fields that are growing at an

exponential rate, expected to alter the very foundations of our transportation system within the next 10-25 years. A crucial interaction has been born out this new technology: Human and automated drivers operating within the same environment. Despite the well- known dangers of automobiles and driving, autonomous vehicles and their consequences on driving environments are not well understood by the population who will soon be interacting with them every day. Will an improvement in the understanding of autonomous vehicles have an effect on how humans behave when driving around them? And furthermore, will this improvement in the understanding of autonomous vehicles lead to higher levels of trust in them? This study addressed these questions by conducting a survey to measure participant’s driving behavior and trust when in the presence of autonomous vehicles. Participants were given several pre-tests to measure existing knowledge and trust of autonomous vehicles, as well as to see their driving behavior when in close proximity to autonomous vehicles. Then participants were presented with an educational intervention, detailing how autonomous vehicles work, including their decision processes. After examining the intervention, participants were asked to repeat post-tests identical to the ones administered before the intervention. Though a significant difference in self-reported driving behavior was measure between the pre-test and post- test, there was no significant relation found between improvement in scores on the education intervention knowledge check and driving behavior. There was also no significant relation found between improvement in scores on the education intervention knowledge check and the change in trust scores. These findings can be used to inform autonomous vehicle and infrastructure design as well as future studies of the effects of autonomous vehicles on human drivers in experimental settings.
ContributorsReagan, Taylor (Author) / Cooke, Nancy J. (Thesis advisor) / Chiou, Erin (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2019
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Description
Communications between air traffic controllers and pilots are critical to national airspace traffic management. Measuring communications in real time made by pilots and air traffic controllers has the potential to predict human error. In this thesis a measure for Deviations from Closed Loop Communications is defined and tested to predict

Communications between air traffic controllers and pilots are critical to national airspace traffic management. Measuring communications in real time made by pilots and air traffic controllers has the potential to predict human error. In this thesis a measure for Deviations from Closed Loop Communications is defined and tested to predict a human error event, Loss of Separation (LOS). Six retired air traffic controllers were recruited and tested in three conditions of varying workload in an Terminal Radar Approach Control Facility (TRACON) arrival radar simulation. Communication transcripts from simulated trials were transcribed and coding schemes for Closed Loop Communication Deviations (CLCD) were applied. Results of the study demonstrated a positive correlation between CLCD and LOS, indicating that CLCD could be a variable used to predict LOS. However, more research is required to determine if CLCD can be used to predict LOS independent of other predictor variables, and if CLCD can be used in a model that considers many different predictor variables to predict LOS.
ContributorsLieber, Christopher Shane (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S (Committee member) / Niemczyk, Mary (Committee member) / Arizona State University (Publisher)
Created2020
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Description

United States Air Force airfield PAVER pavement management system enterprise data was reviewed for 67 networks. The distress survey extents and severity fields were joined with treatment costs estimated using RSMeans to determine the costliest distress. In asphalt surfaced pavements Longitudinal/transverse cracking, weathering, and block cracking resulted in the most

United States Air Force airfield PAVER pavement management system enterprise data was reviewed for 67 networks. The distress survey extents and severity fields were joined with treatment costs estimated using RSMeans to determine the costliest distress. In asphalt surfaced pavements Longitudinal/transverse cracking, weathering, and block cracking resulted in the most pavement condition index (PCI) deducts while the costliest distresses are weathering, block cracking and longitudinal cracking. In portland cement concrete surfaced pavements linear cracking, joint seal damage, and joint spalling resulted in the most PCI deducts while the costliest distresses are joint seal damage, linear cracking, and corner spalling. The results of this data were then compared to airfield attributes: Pavement Temperature Group, Dominant American Association of State Highway and Transportation Officials (AASHTO) Soil Classification, Pavement- Transportation Computer Assisted Structural Engineering (PCASE) Climate Zone, and years since last maintenance. Maps showing the Pavement Temperature Group, Dominant AASHTO Soil Classification, and PCASE Climate Zone are included in Appendix A. Alligator cracking is most prevalent at the airfields with PTG 64-34 (Ellsworth, Fairchild, Hill, and Offutt) and 58-22 (Niagara and Vandenberg). Rutting is most prevalent at PTG 64-34 (Ellsworth, Fairchild, Hill, and Offutt). An increasing trend of joint spalling, corner spalling, and corner break with decreasing soil quality (AASHOTO A-1 to A-8 soils). The PCASE Climate Zone Cost Indices the cost index for weathering is approximately double in the moist region over the dry region. The cost index for block cracking is approximately double in the cold region over the hot region. It is recommended that the agency review its pavement performance modeling in the pavement management system to increase the recommendation of pavement preservation treatments and review the use of higher quality materials for pavement maintenance treatments.

ContributorsThevenot, Ronald (Author) / Kaloush, Kamil (Thesis advisor) / Mamlouk, Michael S. (Thesis advisor) / Ozer, Hasan (Committee member) / Arizona State University (Publisher)
Created2020