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50 results for “asphalt”
Catalyst Regeneration of RAP-Binder in Asphalt
<p>Reclaimed Asphalt Pavement (RAP) binders are difficult to reuse because they often contain associated/aggregated molecules with very high molecular weights. This is due to the polarity gained during oxidative aging, causing the aggregation to occur. These high molecular weight components are responsible for RAP binder’s increased viscosity and certain deteriorated rheological properties. One strategy to reuse RAP binder is to mix it with virgin asphalt binder and use this partially recycled mixture in asphalt. The RAP binder is usually improved before mixing by the addition of rejuvenators, softening agents, softer binders, and antioxidants to the asphalt binder mix to rebalance their rheological properties. The purpose of this research was to study effective ways to incorporate RAP content in asphalt mixtures using a novel approach of introducing a catalyst that can modify the binder’s chemical composition; particularly to alter the oxidized molecules and reduce the number/content of aggregated structures. The use of a catalyst such as a Lewis acid to break the associated molecules in the RAP-binder is a new promising approach. The Lewis acids catalysts are known to catalyze the conversion of coal to liquid product, but the mechanism of action is not well understood. This report describes the result of our investigation into the effects of a Lewis acid catalyst such as Iron (III) chloride and Zinc chloride on the chemical composition of RAP-binder. The ultimate result is that Iron (III) chloride lowers the size of high molecular weight material when 2% w/w is used with RAP at 165 degrees Celsius for 30 minutes.</p>
An Innovative Thermo-Energy Harvesting Module for Asphalt Roadway Pavement
<p>The importance of green technologies for generating renewable energy and sustainable development is widely accepted. Road surfaces are exposed to solar radiation that generates thermal gradients and heat flow in the pavement layers. The heat stored can be harvested providing an untapped source of renewable energy. This report presents the design, construction, and assessment of an improved thermoelectric energy prototype for harvesting heat energy from roadway pavements. To accomplish this, various prototype designs were simulated using Finite Element (FE) analysis, followed by design construction and laboratory testing of the most promising prototypes to evaluate their power harvesting capabilities. The main design components of these prototypes are a heat collector/transfer plate, thermoelectric generators (TEG), and a cooling module consisting of a heat sink, phase change material, and an insulation box. The results suggest a direct relationship between thermal gradients and power generation and point out the importance of the cooling module in maintaining the efficiency of the harvester. An optimum harvester design would generate an average power output of 29 mWatt or 835 J over 8 hours per day in South Texas. Extrapolating this output for an installation that covers a length of 1 kilometer of a roadway could produce an average of 23.2 kWh/day, which appears to be a promising independent source of power for roadside signage and sensors.</p>
Maintenance and Restriping Strategies for Pavement Markings on Asphalt Pavements in Louisiana
<p>In Louisiana, most districts restripe their roadways using waterborne paints every other year; this strategy is questionable in terms of efficiency and economy. Meanwhile, previous studies showed substantial variability in the paint service life throughout the United States ranging between 0.25 and 6.2 years. Shortcomings in modeling the retroreflectivity of waterborne paints appear to significantly contribute to these variations as several studies predicted these values using degradation curves with a coefficient of determination (R2) as low as 0.1. Therefore, the objective of this study was to (i) develop new cost-effective restriping strategies using 4-inch (15-mil thickness) and 6-inch (25-mil thickness) wide waterborne paints when applied on asphalt pavements in hot and humid climates, and (ii) employ an advanced machinelearning algorithm to develop performance prediction models for waterborne paints considering the variables that are believed to affect their performance. To achieve these objectives, National Transportation Product Evaluation Program (NTPEP) data were collected and analyzed to evaluate the field performance of waterborne paints commonly used in Southern United States. Results indicated that 4-inch wide standard paints exhibited service life up to four years depending on the line color, traffic and initial retroreflectivity, while 4-inch wide high-build paints had a service life of at least three years. Based on a life-cycle cost analysis, it was concluded that LaDOTD could restripe their district roads every three years instead of the current two-year period using the same product (4-inch or 6-inch wide) saving about $20 or $2 million, respectively, every year when restriping a 5,000-mile network. Additionally two machine-learning models were developed with an acceptable level of accuracy, and that can predict the skip and wheel retroreflectivity of waterborne paints for up to three years using only the initial measured retroreflectivity and the anticipated project conditions over the intended prediction horizon, such as line color, traffic, air temperature, etc. These models could be used by transportation agencies throughout the United States to (1) compare between different products and select the best product for a specific project, and (2) determine the expected service life of a specific product based on a specified threshold retroreflectivity to plan for future restriping activities.</p>
Results of surface hot-in place recycling (remix) of modified and alternative asphalt mixtures in Finland. Part II: Bitumen scale
<p>The following material is included in a digital appendix.</p> <ul> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%201.%20FT-IR.pdf?versionId=6ea761b1-a830-4e01-a761-f103373497ce">Appendix 1</a>:</strong> FT-IR spectra of all bitumens before and after REM/RUT.</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%202.%20Gradations.pdf?versionId=b8b1b0b0-d421-42f8-84ef-728cb1acce33">Appendix 2</a>:</strong> Gradations of the specimens before and after REM/RUT (two specimens per material).</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%203.%20DSR%20test%20results.pdf?versionId=cd3ccf9e-a264-458c-9d54-fcfbca40ec1f">Appendix 3</a>:</strong> DSR test data. Complex shear modulus (G*), phase angle (δ), and Black diagram for all materials before and after REM/RUT.</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%204a.%20DSR%20master%20curves%20(8-mm).pdf?versionId=44f9c881-912f-4f35-b4c7-193d88d5572c">Appendix 4a</a>:</strong> DSR shifted data, and master (fitted) curves, 8-mm, summary before and after REM/RUT.</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%204b.%20DSR%20master%20curves%20(4-mm).pdf?versionId=30952a8f-029b-409f-ba87-da9d9972880b">Appendix 4b</a>:</strong> DSR shifted data, and master (fitted) curves, 4-mm, summary before and after REM/RUT.</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Appendix%205.%20WLF%20and%20sigmoidal.pdf?versionId=fb826979-868d-47f4-8acd-697550000f87">Appendix 5</a>:</strong> WLF fitting constants (Table A1), and parameters for sigmoidal curves (Table A2).</li> <li><strong><a href="https://zenodo.org/api/files/a34688f2-e7e5-43a4-bcb7-a4238429f56b/Data%20II%20-%20Read%20me.pdf?versionId=c40871da-deff-456f-b4d6-a6136e9c294a">Database</a>:</strong> Laboratory data (Excel files, brief read-me guide) of all tests at bitumen scale. </li> </ul>
Results of surface hot-in place recycling (remix) of modified and alternative asphalt mixtures in Finland. Part I: Mixture scale
<p>The following material is included as a digital appendix:</p> <ul> <li><strong><a href="https://zenodo.org/api/files/ecff4755-8652-4c3b-9dcf-28cf09002c98/Appendix.%20Cores%20and%20plots%20of%20test%20results.pdf?versionId=39468d84-e556-41c4-b86c-0ccdb2d5a36e">Appendix</a>. </strong>Images of selected cores, and test results for all specimens at the mixture scale (plots): bulk density, air voids, strength, stiffness, Prall abrasion, and creep permanent deformation.</li> <li><a href="https://zenodo.org/api/files/ecff4755-8652-4c3b-9dcf-28cf09002c98/Data%20I%20-%20Read%20me.pdf?versionId=e500484c-10df-417a-bc1c-d2017d70d64f"><strong>Database</strong></a> with laboratory test data and measurements (mixture scale). Two Excel files and brief read-me guide.</li> </ul>
Dataset for: Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning." Automation in Construction 143 (2022): 104575. https://doi.org/10.1016/j.autcon.2022.104575.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Asphalt pavement crack detection based on convolutional neural network and infrared thermography
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement crack detection based on convolutional neural network and infrared thermography." IEEE Transactions on Intelligent Transportation Systems 23, no. 11 (2022): 22145-22155. https://doi.org/10.1109/TITS.2022.3142393. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (binary images)</li> </ul>
Dataset for: Deep learning and infrared thermography for asphalt pavement crack severity classification
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Deep learning and infrared thermography for asphalt pavement crack severity classification." Automation in Construction 140 (2022): 104383. https://doi.org/10.1016/j.autcon.2022.104383. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, Linbing Wang, and Imad L. Al-Qadi. "Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning." Automation in Construction 161 (2024): 105355. https://doi.org/10.1016/j.autcon.2024.105355.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(25IRT) images: this folder includes fusion images (25% infrared + 75% visible)</li> <li>04-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>05-Fusion(75IRT) images: this folder includes fusion images (75% infrared + 25% visible)</li> <li>06-Annotations: this folder includes annotations (xml files) based on PASCAL VOC (PASCAL Visual Object Classes Challenge) styles.</li> </ul>
Development of a Standard Test Method for Characterization of Asphalt Modifiers and Aging-Related Degradation Using an Extensional Rheometer
<p>Corresponding data set for Tran-SET Project No. 17BLSU01. Abstract of the final report is stated below for reference:</p> <p>"An extensional deformation test method using a Sentmanat Extensional Rheometer (SER) fixture inside a Dynamic Shear Rheometer (DSR) is developed in this study to investigate the degradation of the polymer due to aging and to investigate the effect of modifier type. A relationship between different percentages of modifier and ductility of the modified binder is also investigated. The sample geometrics used in this study are 1 mm 0.72 mm and 3 mm 0.72 mm. A total of one hundred and sixty-two samples were tested. Three modifiers Styrene-Butadiene-Styrene (SBS), Polyphosphoric Acid (PPA) and latex were used. One PG 76-22, one PG 64-22 and one polymer-modified asphalt emulsion (PAME) were used. First peak elongation force, (F1) is the binders’ stiffness and Second peak elongation force, (F2) is the polymer characteristics. F2 is more visible comparatively at the higher temperature. In most cases, F2 reduces after Rolling Thin Film Oven (RTFO) and Pressure Aging Vessel (PAV) aging. To normalize the stiffness effect of F1 on F2, in this study F2/F1 was used to analyze aging susceptibility of modifiers. All the testing temperatures used in this study exhibited a reduction in F2/F1 due to RTFO aging and further reduction due to PAV aging. Therefore, through this study, it is recommended that this parameter can be used to determine aging susceptibility of polymer in a polymer-modified asphalt binder. F2 is only obtained from the SBS and latex modified binders and emulsions. Addition of PPA did not show any F2, making SBS the most effective modifier among SBS, PPA and latex. F2 has a linear correlation with the percent of the polymer in the PMAE, SBS modified PG 64-22, SBS and PPA modified PG 64-22 and latex modified PG 64-22 with R2 values equal to 0.9934, 0.9323, 0.9893 and 0.9535 respectively, indicating extensional deformation test with SER very promising. Ductility analyses using final angular strain values indicate that modifiers increase ductility significantly while aging reduces ductility. Additional research is required for testing ultra-violet (UV) aged sample, and a DSR-based SER test specification will be developed subsequently."</p>
Use of Nanoclays as Alternatives of Polymers Toward Improving Performance of Asphalt Binders
<p>Corresponding data set for Tran-SET Project No. 17BASU01. Abstract of the final report is stated below for reference:</p> <p>"The main goal of this study is to assess the feasibility of the use of nanoclay as an alternative to commonly used polymers such as styrene-butadiene-styrene (SBS), which are used to modify the performance grade (PG) of asphalt binders. Three types of nanoclay and two types of neat binders were selected for laboratory investigation. Different amounts (1, 2, and 3%) of nanoclays were used. A blending protocol has been developed to mix the nanoclay with the asphalt binder. Rotational Viscosity (RV), Dynamic Shear Rheometer (DSR), Optical Contact Analyzer (OCA) and Atomic Force Microscope (AFM) were conducted to evaluate the properties of modified asphalt binders. Significant increases of viscosity and complex shear modulus were observed for nanoclay-modified binders because of nanoclay’s nanoscale phenomena such as structural features, quantum effects, spatial confinement, high surface energy, and a large fraction of surface atoms. The maximum rutting resistance is expected for binders modified with the 1% Cloisite 11B. The OCA test results suggest that the modified asphalt binders possess higher surface free energy than the neat binder. Therefore, cohesive energy which is an indicator of moisture damage was increased for modified binders. From the AFM analysis, adhesion and deformation values decreased for modified asphalt binders. The minimum adhesion and deformation values were found for asphalt binder samples modified with the 1% Cloisite 10A."</p>
Impacts of Moisture on Asphalt Properties
<p>Corresponding data set for Tran-SET Project No. 17BASU03. Abstract of the final report is stated below for reference:</p> <p>"Stripping related moisture damage has been recognized as one of the major pavement distresses since the early 1990s. The main objective of this study is to establish an effective test protocol to quantify moisture susceptibility of asphalt pavements. To this end, selective test methods (Texas Boiling test, Tensile Strength Ratio, Retained Stability, and Hamburg Wheel Test), and procedures based on surface chemistries and molecular-level mechanistic properties have been investigated in this study. Firstly, a comprehensive list of literature related to moisture damage in asphalts was reviewed. Based on the literature review, a detailed project plan and test matrix were developed. Binder samples originated from two different crude sources were collected. The moisture resistance related tests such as static contact angle measurements and Texas Boiling tests were conducted. Besides, asphalt binders’ nanomechanical properties using an Atomic Force Microscopy (AFM) and surface chemistries using a static contact were evaluated in the laboratory. Based on limited test data and analysis, it is concluded that there does not exist any single test method that all agencies are comfortable and equipped to follow in their daily work as each technique has some merits and demerits. However, the Texas Boiling test is found to be the simplest method that requires minimal time and resources. On the other hand, surface chemistry and atomic force microscope-based techniques are becoming popular among researchers and pavement professionals. Findings of this study are expected to help ARDOT in selecting an appropriate moisture resistance test method that is simple, reliable, and easy to implement in their routine work."</p>
Development of a Self-Healing and Rejuvenating Mechanisms for Asphalt Mixtures Containing Recycled Asphalt Shingle
<p>Corresponding data set for Tran-SET Project No. 17BLSU06. Abstract of the final report is stated below for reference:</p> <p>"The objective of this study was to test the hypothesis that hollow-fibers encapsulating a rejuvenator product could improve both self-healing, rejuvenation, and mechanical properties of asphalt mixtures. Hollow-fibers containing a rejuvenating product were synthesized via a wet spinning procedure with sodium-alginate polymer as the encapsulating material. An optimization of the production parameters for the synthesis of fibers was performed to develop fibers suitable for high-temperature and shear stress environment typical of asphalt mixture production. A self-healing experiment was conducted to evaluate the healing/rejuvenation capabilities of sodium-alginate fibers in asphalt mixtures with varying types of binders and recycled materials. Based on the self-healing experiment, a 5% fiber content was determined to be the optimum fiber content to enhance the self-healing ability of asphalt mixtures. In addition, the effect of different fiber contents on binder blends and asphalt mixtures was evaluated by performing the Multiple Stress Creep Recovery (MSCR) and Semi-Circular Bending (SCB) tests. Results of the self-healing experiment showed that the enhancement in the healing recovery depends on the breakage of the fibers. When the fibers break, the rejuvenator is released resulting in softening of the binder. In contrast, when the fibers do not break, they act as a reinforcement for the mix. Loaded Wheel Tester (LWT) test results showed a performance improvement against permanent deformation for asphalt mixtures containing recycled materials with sodium-alginate fibers compared to conventional asphalt mixtures. Furthermore, SCB test results showed that the addition of sodium-alginate fibers enhanced the fracture properties of asphalt mixtures with Recycled Asphalt Shingle (RAS) at intermediate temperatures. Moreover, the addition of fibers in mixtures with recycled materials resulted in an improved performance against low-temperature cracking as the mixtures resisted higher stresses before failure."</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>
Compression test results of asphalt samples
<p><span>The record contains 7 files, each file contains the results of a strained controlled compression test performed on asphalt samples. The tests were carried out at different temperatures, with varying strain rates, and some of them have unloading-reloading cycles too. </span></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>
CAPRI ASPHALT ONLINE WEBINAR | 20 October 2022
<p>CAPRI organised the 1st online webinar, dedicated to Asphalt Use Case, on October 20, 2022.</p> <p>This is a recording of the webinar.</p> <p>For more information visit the website: https://www.capri-project.com/.</p> <p>The project receives funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement Number 870062.</p>
Dataset of construction and demolition waste images: aerated autoclaved concrete (AAC), asphalt, ceramics, and concrete
<p>Image subsets: the dataset of images (RGB) of CDW materials (aerated autoclaved concrete (AAC), asphalt, ceramics, and concrete) cropped to 200x200 px. The images are annotated and split into testing and training datasets for the purposes of machine-learning models' training.</p> <p>Whole CDW fragments: images used for validation of algorithms - whole fragments placed on contrast background.</p>
Asphalt profile temperatures and weather data of CyPaTs test track
<p>This dataset includes asphalt temperature measurements and weather data of CyPaTs test track located at Campus Groenenborger, University of Antwerp, Antwerp, Belgium. The recorded asphalt temperature measurements and weather data is between March 17<sup>th</sup>, 2021, and March 14<sup>th</sup>, 2022. There are five sheets in each excel file, containing asphalt temperatures at various depths (near-surface, 4cm, 7cm and 10 below asphalt surface), and corresponding weather data. The total number of data points is 371707, with each of these data points including information on weather parameters and 45 sensors embedded in different layers of the asphalt pavement.</p> <ol> <li>Date (time): time of the recorded data</li> <li>TC_x-x: label of the temperature sensor embedded in asphalt pavement, in °C</li> <li>Ta: ambient air temperature, in °C</li> <li>RH: relative humidity, in %</li> <li>FF: wind speed, in m/s</li> <li>SR: solar radiation, in W/m<sup>2</sup></li> </ol>
A New Generation of Dense-Graded Asphalt Mixtures with Superior Performance against Stripping and Moisture Damage
<p>The presence of moisture beneath the pavement surface is a matter of great concerns as it is responsible for significant distresses such as asphalt concrete (AC) stripping, fatigue cracking, rutting, and poor durability of asphalt mixes. The objective of this study was to evaluate and recommend an asphalt mixture design that would provide superior performance against AC stripping and cracking. To achieve this objective, a laboratory test factorial was developed to evaluate the use of nanomaterials, emerging anti-stripping agents, warm-mix asphalt technologies, and adhesion promotors. In the experimental program, the modified Lottman test (AASHTO T 283) and the Indirect Tensile Asphalt Cracking Test (IDEAL-CT) test were used as performance indicators of moisture damage resistance and cracking susceptibility. Results were analyzed statistically to identify and quantify the effects of the design variables and selected additives on the performance, moisture damage resistance, and durability of asphalt mixes. Based on the cracking test results, a superior cracking resistance performance was observed with Zycotherm<sup>®</sup>, irrespective of the mix type. AD-here<sup>®</sup> had the lowest average cracking indices for both mix types, which suggests that it would not function as good as the other additives in terms of cracking resistance. Overall, Stone Matrix Asphalt (SMA) mixes showed greater cracking resistance than the dense-graded mixture, which may have been a result of the RAP material used in the dense-graded mix and its lower asphalt binder content. In terms of moisture resistance, both nanomaterials (Graphene Nanoplatelet [GNP] and Nanoclay) did not perform well as they did not meet the minimum required tensile strength ratio (TSR) criterion. Overall, nanomaterials showed the lowest TSR values in both mix types suggesting that their effectiveness against moisture-induced damage may not be as good as warm-mix additives. In addition, warm-mix additives were expected to show enhanced performance in terms of moisture resistance as compared to the other additives evaluated in this study.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.