A micromechanical approach for predicting the complex shear modulus and accumulated shear strain of asphalt mixtures from binder and mastics

Asphalt mixtures are particulate composite materials consisting of uniformly distributed mineral aggregates, asphalt binder and air voids. Mixtures of asphalt binder and filler, also called mastics, are often assumed to behave as simple viscoelastic materials, where the binders are stiffened by the filler. Because the workability and performance of bituminous mixes are known to be affected by the filler-asphalt mixture (or mastic) properties, this study is intended for performing rheological tests on asphalt binder and mastics and use the results in order to estimate performance parameters of asphalt mixture. The present work uses the PG64-22 asphalt binder test data initially to predict mastics\u27 performance parameters - shear modulus (G*) and accumulated permanent shear strain (γacc) and then same properties for asphalt mixtures, in order to find a correlation between the three materials. Mastics were obtained by mixing the PG64-22 asphalt with three types of filler - donna fill, limestone, and granite - in five different percentages by volume - 5, 10, 15, 20, and 30%. The asphalt mixtures contained granite aggregate, 6% air voids content and five asphalt contents - 3% through 7%. Binder and mastics were tested at three temperatures (46°, 55°, and 64°C) using a dynamic shear rheometer (DSR), while the mixtures were tested at two temperatures (55° and 64°C) using the Superpave shear tester. Newly developed Hirsch model was used for estimating the shear moduli of asphalt mastics and mixtures, while for estimating the accumulated permanent shear strain a semi-empirical equation developed by Shenoy was used. Both estimations have been performed by using the shear modulus of the binder obtained from the DSR. The binder, mastics, and mixtures rheological data were generated using the appropriate equipment for each material, under identical conditions of measurement, thus making it possible to identify a correlation between the materials. There was a good agreement between the measured and estimated values using the two methods (Hirsch and Shenoy), with Pearson correlation parameters (R2) being over 0.90 or better

Tensile strength and bonding characteristics of self-compacting concrete

Self-compacting concrete, also referred to as self-consolidating concrete, is able to flow and consolidate under its own weight and is deaerated almost completely while flowing in the formwork. It is cohesive enough to fill the spaces of almost any size and shape without segregation or bleeding. This makes SCC particularly useful wherever placing is difficult, such as in heavily-reinforced concrete members or in complicated workforms. The objectives of this research were to compare the Splitting Tensile Strength and Compressive Strength values of self-compacting and normal concrete specimens and to examine the bonding between the coarse aggregate and the cement paste using the Scanning Electron Microscope. Cylinder specimens (8 by 4 ) were tested for Splitting Tensile and Compressive Strength after 28 days of standard curing, in order find out if self-compacting concrete would show an increase in these strengths and a better bonding between aggregate and cement paste, compared to normal concrete. The mix design used for making the concrete specimens was based on previous research work from literature. The water - cement ratios varied from 0.3 to 0.6 while the rest of the components were kept the same, except the chemical admixtures, which were adjusted for obtaining the self-compactability of the concrete. All SCC mixtures exhibited greater values in both splitting tensile and compressive strength after being tested, compared to normal concrete. The splitting tensile strength increased by approximately 30%, whilst the compressive strength was around 60% greater. In addition, the SCC tensile strengths after 7 days were almost as high as those obtained after 28 days for normal concrete. This was possible due to the use of mineral and chemical admixtures, which usually improve the bonding between aggregate and cement paste, thus increasing the strength of concrete. Images taken from concrete samples having water-cement ratios of 0.3, 0.4, and 0.6, using the Scanning Electron Microscope, have shown that the widths of the physical interface microcracks were greater for normal concrete than for self-compacting concrete, which implies that the aggregate-cement bonds were better for SCC than for normal concrete

Concrete Microstructure Characterization and Performance

Microstructural characteristics such as the interfacial transition zone (ITZ) and cracking patterns from compressive strength testing are main features that characterize concrete behavior. Certain materials such as blast furnace slag or fly ash introduced in the concrete mix aid in improving its strength and durability. Others such as nanosilica particles may affect only the microstructure of the paste without making any significant improvement in the strength of the ITZ or paste-aggregate bond. Additionally, in situ investigation of the microstructures of fresh cement paste can greatly enhance knowledge of the development properties of concrete at an early age (e.g., setting and hydration), which can be helpful for improvement of the quality of concrete. Common technologies such as Scanning Electron Microscope (SEM) are currently employed in petrographic analysis of cementitious materials and concrete microstructure

Roadside Truck Placard Readers for Advanced Notice and Response at Safety-Critical Facilities: Phase 2

The transport of hazardous and dangerous materials (HAZMAT) through safety-critical facilities poses significant risk to overall system reliability should those assets be incapacitated by the occurrence of a related incident. This problem is particularly acute for facilities in remote locations such as Virginia\u2019s mountain tunnels on Interstate 77 (I-77) due to limitations on alternate routes and the availability and proximity of emergency responders and specialized equipment/supplies. Automated placard reader systems (APRSs) are commercially available camera-based computer vision systems that \u201cread\u201d hazardous material placards on passing trucks from roadside installations. This information, along with other pertinent vehicle identification data may then be forwarded to critical facility operators to inform any preparations or responses that may be required. The Virginia Tech Transportation Institute conducted an initial phase of work to assess the readiness of APRSs for their reliable and effective roadside deployment and to determine how the data from such a system could be used by facility operators to improve safety and mitigate disruption during an event involving HAZMAT. The findings of the first phase of work indicated that available APRS technology was sufficiently advanced to warrant a second phase of work that included field testing and further refinement of the preliminary deployment plan. In Phase I, an APRS from Intelligent Imaging Systems (IIS) was identified for further evaluation. In this (second) phase of work, a mobile APRS system provided by IIS was evaluated under experimental and naturalistic scenarios at the Virginia Smart Roads and at several locations on Virginia public roads. A photographic survey of public HAZMAT placard usage conducted previously was used to inform this testing. Additional naturalistic data were acquired from a permanent APRS installation in Delaware when difficulties with the mobile APRS were encountered. The mobile and permanent APRSs were able to classify HAZMAT placard accurately at rates of 96% and 99%, respectively. The mobile and permanent systems were able to read United States Department of Transportation (USDOT) numbers from the sides of tractors correctly at rates of 46% and 67%, respectively, and tractor license plates correctly at rates of 43% and 39%, respectively. Moderate levels of rain and snow, as observed through roadside cameras and reported at nearby weather stations, had minimal impact on reporting accuracy. Performance of the system at night compared favorably with daytime performance. To address potential false negative concerns, a visual survey of 187 commercial vehicles was conducted that revealed that the APRS was 85% successful at locating and identifying the presence of placards on commercial vehicles passing in the near lane. With respect to implementation, the nature of how the subject APRS data is provided to users is not currently conducive to automated integration with existing or future VDOT tunnel or traffic management systems as data must be read from an online interface and no \u201cpush\u201d options are currently available. Also, providing advance warning of the approach of HAZMAT to tunnel operators on I-77 is not feasible given constraints related to the geographic siting of potential APRS installations and respective traffic characteristics. However, facility operator access to APRS data after an incident has occurred may provide benefits of improved responder and traveler safety as well as faster clearance times

Evaluation of a Buried Cable Roadside Animal Detection System Standard Title Page -Report on Federally Funded Project 1. Report No.: 2. Government Accession No.: 3. Recipient's Catalog No.: FHWA/VCTIR 15-R25 4. Title and Subtitle: 5. Report Date: Evaluati

Animal-vehicle collisions (AVC) are a concern for departments of transportation as they translate into hundreds of human fatalities and billions of dollars in property damage each year. A recently published report states that the Virginia Department of Transportation (VDOT) currently spends over 4 million yearly to remove about 55,000 deer carcasses from its roadways. Currently, one of the most effective existing methods to reduce AVCs is the use of animal detection systems, which can detect animals near the roadway and alert approaching drivers accordingly. In order to reduce AVCs in Virginia, VDOT, in collaboration with the Virginia Tech Transportation Institute, proposed the evaluation of an innovative roadside animal detection system in naturalistic and controlled conditions. This type of system offers numerous apparent advantages over aboveground animal detection technologies when environmental interferences, such as precipitation and vegetation, and site-specific characteristics, such as topology, subsidence, and road curvature, are considered. The subject animal detection system (ADS), a 300-m-long buried dual-cable sensor, detects the crossing of large and medium-sized animals and provides data on their location along the length of the cable. The system has a central processor unit for control and communication and generates an invisible electromagnetic detection field around buried cables. When the detection field is perturbed, an alarm is declared and the location of the intrusion is determined. Target animals are detected based on their conductivity, size, and movement, with multiple simultaneous intrusions being detected during a crossing event. The system was installed and tested at a highly suitable site on the Virginia Smart Road where large wild animals, including deer and bear, are often observed in a roadside environment. This report describes the installation of the ADS, data collection and analysis methodology, evaluation of the system's reliability and effectiveness, cost analysis, and implementation prospects. The system used continuous, all-weather and nighttime video surveillance to monitor animal movement and to gauge system detections, and potential non-detections of the ADS. Also, a communication link between the buried ADS and the Virginia Smart Road fiber optic network was established to allow operation and monitoring of the system from a dedicated server in the Virginia Smart Road Control Room. A performance verification of the network communication was successfully conducted through continuous data collection and transfer to a storage unit. Data were collected continuously for a period of 10 months that included winter, and then analyzed to determine overall detection performance of the system. Data analyses indicate that the ADS, if properly installed and calibrated, is capable of detecting animals such as deer and bear, and possibly smaller animals, such as fox and coyotes, with over 95% reliability. The ADS also performed well even when covered by 3 ft of snow. Moreover, the system was tested under various traffic conditions and no vehicle interferences were noted during the same monitoring period. The acquired data can be used to improve highway safety through driver warning systems installed along roadway sections where high wildlife activity has been observed. Additionally, this system may be integrated with the connected vehicle framework to provide advance, in-vehicle warnings to motorists approaching locations where animals have been detected in or near the roadway. DISCLAIMER The project that is the subject of this report was done under contract for the Virginia Department of Transportation, Virginia Center for Transportation Innovation and Research. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement. Each contract report is peer reviewed and accepted for publication by staff of Virginia Center for Transportation Innovation and Research with expertise in related technical areas. Final editing and proofreading of the report are performed by the contractor. Copyright 2015 by the Commonwealth of Virginia. All rights reserved. iii ABSTRACT Animal-vehicle collisions (AVC) are a concern for departments of transportation as they translate into hundreds of human fatalities and billions of dollars in property damage each year. A recently published report states that the Virginia Department of Transportation (VDOT) currently spends over 4 million yearly to remove about 55,000 deer carcasses from its roadways. Currently, one of the most effective existing methods to reduce AVCs is the use of animal detection systems, which can detect animals near the roadway and alert approaching drivers accordingly. In order to reduce AVCs in Virginia, VDOT, in collaboration with the Virginia Tech Transportation Institute, proposed the evaluation of an innovative roadside animal detection system in naturalistic and controlled conditions. This type of system offers numerous apparent advantages over aboveground animal detection technologies when environmental interferences, such as precipitation and vegetation, and site-specific characteristics, such as topology, subsidence, and road curvature, are considered. The subject animal detection system (ADS), a 300-m-long buried dual-cable sensor, detects the crossing of large and medium-sized animals and provides data on their location along the length of the cable. The system has a central processor unit for control and communication and generates an invisible electromagnetic detection field around buried cables. When the detection field is perturbed, an alarm is declared and the location of the intrusion is determined. Target animals are detected based on their conductivity, size, and movement, with multiple simultaneous intrusions being detected during a crossing event. The system was installed and tested at a highly suitable site on the Virginia Smart Road where large wild animals, including deer and bear, are often observed in a roadside environment. This report describes the installation of the ADS, data collection and analysis methodology, evaluation of the system's reliability and effectiveness, cost analysis, and implementation prospects. The system used continuous, all-weather and nighttime video surveillance to monitor animal movement and to gauge system detections, and potential nondetections of the ADS. Also, a communication link between the buried ADS and the Virginia Smart Road fiber optic network was established to allow operation and monitoring of the system from a dedicated server in the Virginia Smart Road Control Room. A performance verification of the network communication was successfully conducted through continuous data collection and transfer to a storage unit. Data were collected continuously for a period of 10 months that included winter, and then analyzed to determine overall detection performance of the system. Data analyses indicate that the ADS, if properly installed and calibrated, is capable of detecting animals such as deer and bear, and possibly smaller animals, such as fox and coyotes, with over 95% reliability. The ADS also performed well even when covered by 3 ft of snow. Moreover, the system was tested under various traffic conditions and no vehicle interferences were noted during the same monitoring period. The acquired data can be used to improve highway safety through driver warning systems installed along roadway sections where high wildlife activity has been observed. Additionally, this system may be integrated with the connected vehicle framework to provide advance, in-vehicle warnings to motorists approaching locations where animals have been detected in or near the roadway

Fast Automatic Analysis of Graceful Degradation in Power Combining Structures

International audienceGraceful degradation analysis in power combining structures is essential, but time consuming, when the number of combined ways is very large. The tool presented in this paper aims to automate the graceful degradation analysis when amplifiers fail or are switched off for output power flexibility or in phased array antenna applications. It is an alternative to commercial software as the tool highly reduces the designer operations by automating, for each number and combination of outages, the calculation of the 2-port scattering parameters of the whole structure. Therefore, the comparison between different isolation solutions of the combiner is made easier and faster. Simulation and comparison with measurements on a 16-way radial power amplifier in the Ka band are presented

Automated Last Mile Connectivity for Vulnerable Road Users \u2013 Real-world Low Speed Autonomous Vehicle Deployment

69A3551747125An EasyMile EZ10 low speed autonomous vehicle (LSAV) was deployed on a route between the Virginia Tech Transportation Institute campus and a nearby bus transit stop to study prospective user attitudes and acceptance regarding trust in technology, system safety, and personal security. The LSAV operated on this route within normal travel lanes and interacted with mixed public traffic that included the full range of transportation users from pedestrians to heavy vehicles. The findings of this deployment work are shared in a lesson-learned format in the hope that the knowledge gained through this research and technology deployment will inform future LSAV implementations and provide insights into how automated technologies should be applied and regulated considering real-life usage aspects This report is one of two produced for the larger study. While this report focuses on the actual deployment of the LSAV, the other focuses on vulnerable road users and their prospective use of this technology