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61 results for “pavement”
Simplified Approach for Structural Evaluation of Flexible Pavements at the Network Level
<p>Corresponding data set for Tran-SET Project No. 17PUTA02. Abstract of the final report is stated below for reference:</p> <p>"Currently, there are few available simple procedures to identify structurally weak pavement sections utilizing Falling Weight Deflectometer (FWD) data at the network level (e.g., city, state or province). A simple method is required to determine the structural condition of pavement sections that can be directly implemented and automated in current pavement databases. The objective of this research study is to develop a simple analysis method to determine the structural condition of pavement sections utilizing the currently available non-destructive testing (NDT) deflection measurement devices at the network level that can be directly implemented and automated in the database of a typical transportation agency. In addition, the study had conducted an advanced mechanistic analysis to mimic the FWD deflection bowl obtained from the field. The developed structural condition parameters can be easily implemented in pavement management systems (PMS). This will aid Departments of Transportation (DOTs) and local highway agencies to make more informed decisions about the most suitable maintenance and rehabilitation strategies. Those parameters were also utilized to predict the remaining fatigue lives of the studied pavement sections."</p>
Use of Ultra‐High‐Performance Fiber‐Reinforced Concrete (UHP‐FRC) for Fast and Sustainable Repair of Pavements
<p>Corresponding data set for Tran-SET Project No.17STUTA03. Abstract of the final report is stated below for reference:</p> <p>"This research presents a new methodology, which enables streets, roads, highways, bridges, and airfields to use an advanced fiber-reinforced concrete material, which can delay or prevent the deterioration of these transportation infrastructure when subjected to traffic and environmental loadings. The major problem of concrete is its considerable deterioration and limited service life due to its brittleness and limited durability. As a result, it requires frequent repair and eventual replacement, which consumes more natural resources. Ultra-high-performance fiber-reinforced concrete (UHP-FRC) introduces significant enhancement in the sustainability of concrete structures due to its dense microstructure and damage-tolerance characteristics. These characteristics can significantly reduce the amount of repair, rehabilitation, and maintenance work, thereby giving the transportation infrastructure a longer service life. This research addresses the strong need to develop fast and sustainable UHP-FRC materials for pavement repair that can be easily cast onsite without special treatments. This avoids any major changes to current concrete production practice and accelerates the use of UHP-FRC materials. This research investigated a new method for concrete repair by combining precast UHP-FRC panels with a small quantity of cast-in-place UHP-FRC for pavement repair without any dowel bars. In this method, a precast UHP-FRC panel is used along with cast-in-place UHP-FRC. The vertical repair surfaces of the existing concrete are roughened on site. The outer edges of the UHP-FRC precast panel are roughened before they are brought to the site (no dowel bars are needed). The depth of the precast UHP-FRC panel is the same as the existing pavement thickness. Only a small cast-in-place UHP-FRC joint (one to two inches wide) is done onsite. The roughened precast UHP-FRC panel is placed in the repair area and cast-in-place UHP-FRC is cast into the joint. Experimental results showed that using a roughened surface (up to about CSP 5) provides a very large bond resistance, which is enough to prevent faulting."</p>
Enhancing the Durability and the Service Life of Asphalt Pavements through Innovative Light-Induced Self-Healing Materials
<p>Corresponding data set for Tran-SET Project No. 17BLSU02. Abstract of the final report is stated below for reference:</p> <p>"The objective of this study was to evaluate the efficiency of a new generation of Ultraviolet (UV) light-induced self-healing polymers in enhancing the durability and self-healing properties of asphalt mixtures. Self-healing polymers were successfully synthesized in the laboratory and were characterized using Fourier Transform Infrared Spectroscopy (FTIR). In addition, Thermogravimetric Analysis (TGA) results showed that the synthesized polymers achieved the required thermal stability to resist asphalt mixture production processes. Viscosity results showed that addition of 5% Recycled Asphalt Shingle (RAS) and/or 20% Reclaimed Asphalt Pavement (RAP) caused an increase in the viscosity of the binder blends. However, a reduction in viscosity of the binder blends containing recycled asphalt materials was observed when adding self-healing polymers. Semi-Circular Bending (SCB) test results showed that addition of recycled asphalt materials negatively affected the cracking performance of mixtures. However, incorporation of self-healing polymer (SHP) and 48h of UV light exposure improved the cracking resistance. This behavior was more evident with mixtures prepared with an unmodified binder. Loaded-Wheel Test (LWT) results showed that the addition of the self-healing polymer led to an increase in the rut depth of the samples prepared with an unmodified binder. However, the final rut depth was less than 6 mm, which is an acceptable rutting performance. Thermal-Stress Restrained Specimen Test (TSRST) results showed that addition of 5% RAS negatively affected the low-temperature cracking performance of the mix. In contrast, 5% SHP enhanced the low-temperature cracking performance of the mix by increasing the fracture load and decreasing the fracture temperature. For mixtures prepared with an unmodified binder, the optimum crack healing efficiency was observed for the mixtures containing 5% RAS and 5% self-healing polymer and exposed to 48h of UV light. Yet, self-healing polymer did not perform well in mixtures prepared with PG 70-22M polymer-modified binder. This may be due to the interaction between the polymer in the binder and the self-healing polymer."</p>
Developing Implementable Climatic Input Data and Moisture Boundary Conditions for Pavement Analysis and Design
<p>Corresponding data set for Tran-SET Project No. 18POKS03. Abstract of the final report is stated below for reference:</p> <p>"The main objective of this study is to develop a practical and implementable numerical model for predicting the moisture (suction) regime within the pavement subgrade system. The research quality and uniformly-dispersed climate data over short distances from Oklahoma Mesonet and the Mitchell based moisture (suction) prediction methods establish the main background of the research study. The study involved numerical modeling and statistical analysis of climatic weather data. The proposed moisture variation model predicts the suction distribution throughout the soil subgrade by solving the diffusion equation and incorporates the measured suction from the Oklahoma Mesonet to estimate the diffusion coefficient. The research study resulted in a practical prediction model that could be used to determine the moisture boundary conditions within the pavement structure."</p>
The Impact of Hurricane Harvey on Pavement Structures in the South East Texas and South West Louisiana
<p>Corresponding data set for Tran-SET Project No. 18PUTA02. Abstract of the final report is stated below for reference:</p> <p>"This study developed a methodology to estimate the damage caused by flooding, such that caused by Hurricane Harvey, on a road or street network. The flooded street or pavement sections are identified using GIS flood maps with street GIS maps used for pavement management systems (PMS) by cities or state authorities. Then the damage caused by flooding directly through the increase moisture in foundation layers or indirectly due to the increase heavy traffic during the relief effort is estimated. An example Excel macro was created to illustrate the estimation process. The methodology estimates the increase in rehabilitation costs since the flooding imposes that many rehabilitation works must be done earlier than anticipated before the flooding. The methodology also estimated the increase in fuel consumption caused by the increased in pavement roughness if the rehabilitation works are done when anticipated before the flooding. The methodology and the Excel macro can also be used to identify the pavement structures with better resilience to the flooding by grouping sections based on the flooding duration (no flooding, single and multiple day flooding) and on design features such as pavement type, functional class, age or time from the most recent resurfacing or reconstruction, subgrade soil type, traffic volume, layer thickness."</p>
Permeable pavement hydraulic performance and clogging experiments using a full-scale urban drainage physical model
<p>This dataset contains the results from 15 tests conducted used a physical model in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain) as part of the POREDRAIN project.</p> <p><br>The objective of the tests is to analyse the hydraulic performance of a porous asphalt layer of the PA-16 type and the impact of clogging on the hydrological behaviour and water quality of the effluent. The porous asphalt was used to retrofit an impervious concrete surface of a 36 m² full-scale street section physical model, which consist of a rainfall simulator placed over the street surface. The behaviour of the porous asphalt layer was assessed by adding surface sediment loads between simulated rainfall events. Stormwater flow discharges were collected from two gully pots and an outlet lateral channel. </p>
Data from paper "Impact of milling machine parameters on the properties of reclaimed asphalt pavement"
<p>The dataset includes test results of road cores and samples gathered from the same locations after milling of asphalt pavement. Milling machine parameters, including moving speed, milling depth, and drum rotational speed were intentionally varied during milling. Results from four jobsites are included in the dataset. The results include binder content, penetration, softening point, coarse and fine aggregate flow coefficients, gradation of RAP, gradation of extracted RAP aggregates, as well as the calculated chunk, breakdown, and filler increase indexes. These three indexes were used to quantitatively characterize the milling operations.</p>
Indicative distribution map for Ecosystem Functional Group T3.4 Young rocky pavements, lava flows and screes
<p>This archive contains indicative distribution maps and profiles for <strong>T3.4 Young rocky pavements, lava flows and screes</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Vehicle trajectory and pavement behavior data
<p>The dataset includes three documents.</p> <p><strong>HDV_data_NGSIM_I_80.xlsx</strong></p> <p>The vehicle trajectory data from Next Generation SIMulation (NGSIM) dataset was collected on eastbound I-80 in the San Francisco Bay area, in Emeryville, CA, on April 13, 2005, from 4:03:56 pm to 4:08:56 pm. Including vehicle id, frame id, the total count of frames of each vehicle, global time, local position, global position, vehicle length, vehicle width, vehicle class, speed, acceleration, lane id, preceding vehicle id, following vehicle id, space headway, time headway, and time.</p> <p><strong>CAV_data_CARLA_SUMO.xml</strong></p> <p>The simulated CAV trajectory data with CARLA and SUMO, including vehicle id, position, angle, type, speed, lane id, and slope of each frame.</p> <p><strong>LTPP_data.csv</strong></p> <p>The table including 21 columns is calculated from the Long-Term Pavement Performance (LTPP) database.</p> <ul> <li>IRI The IRI value measured when age was 0. (m/km)</li> <li>Cr_Gator Area of alligator cracking in square meters. (m^2)</li> <li>Cr_Lwp Length of longitudinal cracks within the defined wheel paths in meters. (m)</li> <li>Cr_Lnwp Length of longitudinal cracks not in the defined wheel paths in meters. (m)</li> <li>Pt_A Area of patches in square meters. (m^2)</li> <li>Pt_N Number of patches in square meters. (m^2)</li> <li>Cr_Wp Length of wheelpath cracks in meters. (m)</li> <li>Cr_Gt183 Total length of transverse cracks greater than 1.83. (m)</li> <li>Rt The depth of rutting in millimeters. (mm)</li> <li>Fr Friction number between the vehicle wheel tire and the pavement</li> <li>IRI_0 The IRI value measured when age was 0. (m/km)</li> <li>Tk_Sb Layer thickness measurement for surface coarse and binder course. (in)</li> <li>Md_s Average backcalculated elastic modulus of the surface layer.(psi)</li> <li>Hydr Average measured hydraulic conductivity of the specimen. (cm/sec)</li> <li>Prcp Average monthly precipitation in millimeters. (mm)</li> <li>Fz Average freeze index. (℃/day)</li> <li>Esal Annual average ESAL (kESAL)</li> <li>Esal_q quadratic form of Kesal (kESAL^2)</li> <li>Age Time duration between new construction to roughness survey date. (year)</li> <li>Gr Mean specific gravity of asphalt cement</li> <li>Pt_Ca Coarse aggregate amount percent by total weight of aggregate in percentage. (%) </li> </ul>
A Deep Learning Tool for the Assessment of Pavement Smoothness and Aggregate Segregation during Construction
<p>Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) and 129 pavement images collected from three construction sites a few days after paving. These images were randomly divided into 70%, 15%, and 15% for the training, testing, and validation phases, respectively. The roughness model achieved 93.8% and 92.6% accuracy in the training and validation stages; respectively, and predicted the International Roughness Index (IRI) values with a coefficient of determination R<sup>2</sup> of 0.98 and a Root-Mean Square Error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved adequate accuracy. Furthermore, the developed segregation detection procedure adequately described the relationship between mix density and segregation.</p>
Development of a Machine Learning-Based Model to Determine the Optimum and Safe Restriping Timing of Thermoplastic Pavement Markings in Hot and Humid Climates
<p>Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R<sup>2</sup> (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics and to propose cost-effective restriping strategies for thermoplastics used in hot and humid climate service conditions. To achieve this objective, National Transportation Product Evaluation Program (NTPEP) data were mined and analyzed. Results indicated that the service life (SL) of thermoplastics ranged between 0.4 and 12.1 years (according to the initial retroreflectivity, traffic, and surface type) with an average value of 3.4 ± 0.2 years. Four regression models with relatively high accuracy were developed to predict the SL of thermoplastics based on key variables. In addition, the genetic algorithm was used to develop a model that predicts the future retroreflectivty of these pavement markings. The predicted values were compared against actual retroreflectivity measurements collected from a field experiment at Louisiana State University. The results of this study could be used to make effective decisions related to restriping scheduling. Using the proposed models in restriping scheduling can result in considerable cost savings (up to $8,212 per lane-mile), as compared to the conventional restriping strategy.</p>
UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images
<p>The images in the dataset ( VOC format) were captured by a UAV at an altitude of 30 meters. The collected images were annotated in PASCAL VOC format. A total of 11,158 instances in 2,440 images are incorporated in the dataset.</p> <ul> <li>The UAV-PDD2023 dataset, captured by unmanned aerial vehicles (UAVs), provides a benchmark for road damage detection. It is highly useful for municipal authorities and road agencies to conduct low-cost road condition monitoring. </li> <li>Six types of road damages are labeled in the dataset: Longitudinal cracks (LC), Transverse cracks (TC), Alligator cracks (AC), Oblique cracks (OC), Repair (RP), and Potholes (PH). </li> <li>Researchers can use this dataset as a benchmark to evaluate the performance of different algorithms in addressing similar problems, such as image classification and object detection. </li> </ul> <p> </p>
Technology review and roadmap for inventorying complete streets for integration into pavement asset management systems
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Data from: Biased movement drives local cryptic colouration on distinct urban pavements
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Data from: Of puzzles and pavements: a quantitative exploration of leaf epidermal cell shape
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Research data on pavement texture changes for D6.5
<p>In-situ surface texture measurements</p><p> </p><p>Three different tests were conducted to monitor the evolution in the NEMO micro-texture of the carousel pavements (S1-S4): British pendulum (SRT), Dynamic Friction Tester (DFT) and T2GO. Measurements were taken at three different locations per section in the initial state and at increasing numbers of cycles : 0, 20,000, 50,000, 100,000, 200,000, 500,000, 750,000 and 1 million load cycles. </p><p> </p><p>The macrotexture of NEMO pavements was evaluated using two complementary techniques. The first is a classical volumetric techniques called the sand patch test. According to this techniques, the macrotexture is approached by mean texture depth (MTD). The second technique relies on the profilometry according to ISO EN 13473-1 standard. Two devices named Elatextur and LTS9500 measure the depth of surface roughness using a laser beam. The Elatextur laser scan has a width 400 mm diameter and a 0.2 mm of path while the LTS9500 scans a 4″ x 4″ area with a lateral resolution of less than 0.5 mm</p><p> </p>
Roman pavement of opus signinum, Tarraco
Roman pavement of opus signinum and white marble. 1st century B.C. - 1st century A.D. Pavement with the welcome inscription AVE SALVE inscribed with white marble tiles. It must have been part of the threshold of the door of some domus (house) of Tarraco. 352 photos completely processed in Reality Capture. The project was done with the support of the National Archaeological Museum of Tarragona and the Agència Catalana del Patrimoni Cultural. Catalog No. MNAT702. Museu Nacional Arqueològic de Tarragona (2019): Tarraco: Exposición de síntesis. Generalitat de Catalunya Publicacions. Source: Objaverse 1.0 / Sketchfab
Cellulose Synthase Tethering Attenuates Mechano-induced Microtubule Organization in pavement cells
<p>Mechanical forces control development in plants and animals, acting as cues in pattern formation and as the driving force of morphogenesis. In mammalian cells, molecular assemblies residing at the interface of the cell membrane and the extracellular matrix play an important role in perceiving and transmitting external mechanical signals to trigger physiological responses. Similar processes occur in plants, but there is little understanding of the molecular mechanisms and their genetic basis. Here, we show that number and movements directions of cellulose synthase complexes (CSCs) at the plasma membrane vary during initial stages of development in the cotyledon epidermis of Arabidopsis, closely mirroring the microtubule organization. Uncoupling microtubules and CSCs resulted in enhanced microtubule co-alignment as caused by mechanical stimuli driven either by cell shape or by tissue-scale physical perturbations. Furthermore, micromechanical perturbation resulted in depletion of CSCs from the plasma membrane suggesting a possible link between cellulose synthase removal from the plasma membrane and microtubule response to mechanical stimuli. Taken together, our results suggest that the interaction of cellulose synthase and cortical microtubules forms a physical continuum between the cell wall, plasma membrane, and the cytoskeleton that modulates the mechano-response of the cytoskeleton.</p>
Evaluation of Materials for Low Temperature Asphalt Pavement Performance, BBR Field study
<p>Asphalt mixtures were collected from seven construction projects, both of the plant and during laydown. One SGC cylindrical specimen or ‘puck’ of 80 mm thickness was prepared for each type of mixture. By doing so, 14 different SGC pucks were obtained. The pucks were labeled as ‘UT-01-P’ to ‘UT-07-P’ for plant mixtures and ‘UT-01-F’ to ‘UT-07-F’ for mixtures that were obtained during laydown in the field. Because of compacting to a specific height, the air voids of the compacted pucks varied from 3% to 6%.</p> <p>In Lab A, the specimens were tested in the Bending Beam Rheometer (BBR) by following the AASHTO TP 125-16 testing procedure at three different temperatures: -12°C, -18°C, and -24°C. On the other hand, in Lab C, the specimens were tested at only one temperature (PG + 10°C). In average, 12 specimens were tested for each mixture at both of the laboratories.</p>
Pavement. urban domus, Tarraco, Tarragona, Spain
Paviment of a urban domus from the site of Tarraco (Tarragona, Spain), dated in the 1st century AD. Opus sectile mosaic made with different types of marble -giallo antico brecciatto, rosso antico, jaspi del Cinta, brocatello and pavonazzetto- that would have occupied the centre of a room in a domus of the south-western suburb of Tarraco. 245 photos completely processed in Reality Capture. The project was done with the support of the National Archaeological Museum of Tarragona and the Agència Catalana del Patrimoni Cultural. Catalog No. MNATTCA_94_124. Museu Nacional Arqueològic de Tarragona (2019): Tarraco: Exposición de síntesis. Generalitat de Catalunya Publicacions. Source: Objaverse 1.0 / Sketchfab
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International Brain Laboratory public data
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OpenNeuro
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