scispace - formally typeset
Search or ask a question

How to determine the rutting of bitumen? 


Best insight from top research papers

To determine the rutting of bitumen, various methods and models have been developed. One approach involves evaluating the correlation between rutting susceptibility of bitumen and asphalt mixtures prepared using the same bitumen, utilizing tests like multiple stress creep and recovery, Superpave rutting parameter G*/sinδ, and flow number tests . Additionally, the use of polymer-modified binders, such as EVA-18 and EVA-28, in high modulus asphalt mixes has shown promising results in improving rut resistance, as indicated by rheological tests and mix rut depth measurements . Furthermore, the development of AI-based prediction models using computer vision techniques has enabled accurate identification of rutting on asphalt pavement roads, aiding in periodic pavement surveys . These methods collectively contribute to assessing and addressing the issue of rutting in bitumen and asphalt pavements.

Answers from top 5 papers

More filters
Papers (5)Insight
The rutting of bitumen can be determined by developing a mathematical model using IDT test results and Marshall Parameters, validated with ANN for predicting specimen rut depth accurately.
To determine rutting of bitumen, consider realistic tire-pavement contact area using Rotary Compactor and Wheel Tracker equipment, revealing significant underestimation of contact stress and reduced pavement operational life.
Rutting of bitumen can be determined through temperature and frequency oscillation, MSCR tests, and measuring mix rut depth using a wheel tracker, as discussed in the research paper.
The rutting of bitumen can be determined using a novel AI-based model combining image processing, LSSVC, feature selection, and FBI metaheuristic optimization for accurate recognition.
Rutting of bitumen can be determined by evaluating parameters like percentage of recovery (%R), non-recoverable compliance (Jnr) through MSCR test, and G*/sinδ through frequency sweep tests.

Related Questions

What are the ways used to estimate axle loads of vehicles?4 answersVarious methods are employed to estimate axle loads of vehicles. One approach involves utilizing visual measurement techniques to determine axle loads based on vehicle unsprung masses, mass ratios, and sprung mass centroid position. Another method includes analyzing load estimates obtained through different estimation techniques and selecting the most accurate output estimate based on specific characteristics, such as surface gradients and acceleration comparisons. Additionally, a method involves measuring the distance between the vehicle body and chassis, determining loading or unloading processes, and projecting axle load values using hysteresis curves for driving dynamics control systems. Furthermore, an axle-load measuring apparatus calculates axle loads by detecting displacements in captured images and utilizing correction information. These diverse techniques showcase the innovation and variety in estimating axle loads of vehicles.
What is objective of rutting analysis of asphalt mixture?5 answersThe objective of rutting analysis of asphalt mixture is to understand and predict the permanent deformation behavior of the pavement caused by the load frequency and other factors. This analysis helps in evaluating the rutting resistance of asphalt mixtures and identifying the factors that affect it. The analysis involves conducting rutting tests using different test systems and procedures, such as the Modified Wheel Tracker (MWT) and material test system (MTS). Statistical analysis and regression models are used to determine the significant parameters and establish prediction models for rutting behavior. The analysis also helps in identifying the factors that can improve the rutting resistance of asphalt mixtures, such as aggregate gradation, asphalt content, compaction degree, layer thickness, and avoiding overloaded vehicles. The goal is to develop accurate and efficient methods for assessing the rutting potential of asphalt mixtures, which can be used for quality control and assurance purposes.
What are the factors that contribute to rutting in asphalt with Kenpave?5 answersRutting in asphalt pavement is influenced by several factors. The primary influencing factors include the internal composition characteristics of the asphalt mixture, environmental conditions, and human factors. Other factors that affect the anti-rutting performance of asphalt pavement include temperature, wheel load, asphalt dosage, air-void, and fine aggregate. The mixtures' structural type also plays a significant role in rutting resistance, with temperature and asphalt dosage having a similar and greater influence. Additionally, the binder grade, binder content, aggregate gradation, air voids, traffic loading, and temperature are factors that contribute to rutting in asphalt mixes. The presence of a flexible or semi-rigid base can also impact asphalt pavement rut. Overall, these factors need to be considered in the design and construction of asphalt pavements to mitigate rutting issues.
What are the best methods for measuring the characteristics and components of asphalt?5 answersThe best methods for measuring the characteristics and components of asphalt include: - Using mathematical statistical methods to quantify and analyze the conventional indexes of different batches and sources of petroleum asphalt, allowing for quick and effective comparison and analysis of the comprehensive performance of different asphalts. - Utilizing a system that includes a load frame and a test fixture, where the load frame applies a load to the asphalt sample and a load cell measures the applied load, generating corresponding load electrical signals. - Employing an asphalt identification examination method that measures the position characteristics of infrared absorption intensity of specific chemical components in asphalt, compares the infrared spectrum of the asphalt to be measured with the infrared spectrum of standard asphalt in a standard spectrum database, and compares the position and peak value height similarity of the specific components in the two infrared spectrums. - Directing light from a light source to the surface of the asphalt, detecting the reflected or refracted light using a light detector, and assigning a number indicating the quality of the asphalt based on the detected light. - Using molecular simulation methods to explore the micromechanical and thermodynamic properties of asphalt, collecting molecules to simulate fresh asphalt and generating molecules to simulate aged asphalt binders, establishing asphalt models and aging models, and comparing the simulated results to test results.
What is the use of rotational viscometer test results in the bitumen research?5 answersThe rotational viscometer test results are used in bitumen research to understand the behavior and characteristics of bitumen. The test helps in measuring the viscosity of bitumen, which is an important property in determining fluid dynamics for various applications. The viscosity of bitumen can be affected by factors such as temperature, binder grade, and the presence of fillers. By using a rotational viscometer, researchers can quantify the changes in viscosity over time and assess the effect of different materials on the viscosity of bitumen. This information is crucial for asphalt technologists to optimize mix designs, ensure even distribution of filler particles, and improve the mechanical properties of bituminous binders. Additionally, the rotational viscometer can be used to develop new metrics and methodologies for characterizing foamed asphalt binders, which are used in warm-mix asphalt applications.
How can the optimal proportion of rejuvenator for aged bitumen be determined in a more efficient and accurate way?2 answersThe optimal proportion of rejuvenator for aged bitumen can be determined in a more efficient and accurate way by considering various factors. Molecular dynamics (MD) simulation can be used to predict the compatibility between rejuvenators and aged bitumen, providing insights into the intermolecular interaction, dispersion degree, and molecular mobility. Wetting parameters and permeation experiments can help evaluate the wetting behavior and permeability of bio-oil in aged bitumen, aiding in the selection and modification of suitable rejuvenators. Physicochemical rejuvenation processes using reactive substances like polyurethane (PU) prepolymer and aromatic oil (AO) can effectively rebuild the structure and adjust the compatibility of aged bitumen, improving its high-temperature performance. Free volume theory can be utilized to predict the diffusion behavior of rejuvenators in aged asphalt, considering factors like temperature, rejuvenator dosage, and rejuvenator type. Additionally, studying the rejuvenation of Reclaimed Asphalt Pavement (RAP) binders can provide insights into the impact of rejuvenators on different types of bitumen and their ageing behavior.

See what other people are reading

What are the common diagnostic methods for detecting iron deficiency anemia?
5 answers
Common diagnostic methods for detecting iron deficiency anemia include the use of serological parameters like serum ferritin and transferrin saturation (TSAT). Additionally, reticulocyte biomarkers such as reticulocyte hemoglobin content (RET-He) and Delta-hemoglobin equivalent (Delta-He) determined by fluorescence flow cytometry have shown to be more specific and sensitive in diagnosing iron-deficient anemia, especially in critically ill patients in the intensive care unit. Furthermore, non-invasive techniques utilizing machine learning algorithms have been increasingly employed for the detection of anemia, with methods like Naïve Bayes, CNN, SVM, k-NN, and Decision Tree algorithms showing high accuracy rates in detecting iron-deficiency anemia through the analysis of conjunctiva, palpable palm, and fingernail images. These methods offer efficient, cost-effective, and less time-consuming alternatives for diagnosing anemia.
Are there any review papers specifically on remote sensing of river water quality?
5 answers
Yes, there are review papers focusing on the remote sensing of river water quality. One such review paper discusses the techniques, strengths, and limitations of remote sensing applications for monitoring water quality parameters using various algorithms and sensors, including spaceborne and airborne sensors like those on Sentinel-2A/B and Landsat. Another paper presents a systematic review of water quality prediction through remote sensing approaches, emphasizing the importance of predicting water quality changes and the use of multispectral and hyperspectral data from satellite and airborne imagery for parameter retrieval. Additionally, a study proposes a feature selection method based on machine learning for water quality retrieval in urban rivers using Sentinel-2 remote sensing images, highlighting the effectiveness of the ReliefF-GSA method and specific models like Random Forest regression.
What are the current parallel machine learning methods used in black oil modeling?
5 answers
Current parallel machine learning methods used in black oil modeling include the utilization of functional networks (FN) coupled with particle swarm optimization (PSO) for predicting PVT properties like bubble point pressure, oil formation volume factor, and oil viscosity. Additionally, the fully implicit method is commonly employed in reservoir simulation for black oil problems, with strategies to reduce computational time through a sequential formulation and efficient Gauss–Seidel solver for transport problems. Furthermore, the parallel performance of MPI-based BIILU2 linear solver with optimal parameter selection has been analyzed for three-phase black oil models in highly heterogeneous fields, demonstrating trends in parameter choice for optimal solver performance. These methods showcase advancements in machine learning techniques for enhancing black oil modeling efficiency and accuracy.
What is bank performance?
4 answers
Bank performance refers to how effectively a bank utilizes its assets to achieve its objectives, which is crucial for economic development. Various methods are employed to assess bank performance, such as statistical approaches and Machine Learning (ML) classifiers like Random Forest, Support Vector Machine, and Logistic Regression. Evaluating bank performance is essential for promoting balanced regional development, supporting public administration functions, and contributing to economic growth. Factors like capital, business activities, and income significantly influence bank performance, with profitability playing a consistent role in enhancing performance. Enhancing bank performance is vital for mobilizing savings, financing investments, and ultimately fostering economic growth and societal development.
What does Binned Contigs mean in genomics?
5 answers
Binned contigs in genomics refer to the process of clustering together contigs that are inferred to originate from the same organism within metagenomic data. This clustering is essential for reconstructing complete genomes from fragmented metagenomic assemblies. Various binning tools and algorithms have been developed to aid in this process, such as BinaRena, binny, Binnacle, and BinChecker, each offering unique approaches to improve the quality and completeness of metagenome-assembled genomes (MAGs). These tools utilize different strategies, including k-mer composition, coverage by metagenomic reads, assembly graphs, and protein domain searches, to accurately group contigs and identify high-quality genomes that may have been missed by other methods.
What are oil spill properties?
4 answers
Oil spill properties include viscosity, density, evaporation, and water uptake behaviors. Different types of oils and fuels, such as gasoline, diesel fuel, light and heavy crude oils, and bunker fuels, exhibit varying viscosities and densities. Evaporation of spilled oil is a crucial factor, with about 80% of evaporation occurring within the first 2 days after a spill. Water uptake by spilled oil can happen through soluble water, entrained water, meso-stable emulsions, stable emulsions, or in forms that do not fit these categories. Additionally, the physicochemical properties of soil affected by oil spills can be altered, impacting soil fertility and plant growth, with changes in pH, organic matter, nitrogen, phosphorus, and heavy metal concentrations observed in affected areas.
Impact of input data quantity (size) on AI outcomes?
4 answers
The impact of input data quantity on AI outcomes varies across different contexts. In the realm of image processing systems within IoT, the size of input images significantly affects node offloading configurations, with larger images increasing communication costs. Time-dependency in data can lead to a decline in AI algorithm performance over time, where even an infinite amount of older data may not enhance predictions, emphasizing the importance of current data. For machine learning-based prediction schemes, an optimal number of input images exists to avoid overfitting, with an experiment finding 16 images as the most accurate prediction point. In freeway incident detection systems, the quantity and balance of real-world data samples impact the performance of AI models, highlighting the importance of data quantity in training ANN models.
Impact of input data quantity (size) on AI predictionoutcomes?
5 answers
The quantity of input data significantly impacts AI prediction outcomes. Research indicates that time-dependent data loses relevance over time, affecting algorithm performance and business value creation. In the context of predicting PM2.5 concentrations, the division of data into training and testing sets influences model performance, with specific ratios proving more suitable for accurate predictions. Additionally, in a study on mmWave signal strength prediction, the optimal number of input images for machine learning models was found to be crucial, as an excessive amount can lead to overfitting and reduced prediction accuracy. Moreover, in IoT image processing systems, the size of input images plays a significant role in determining the efficiency of node offloading configurations, with communication costs outweighing processing costs as image size increases.
What are the main personalized online learning models?
5 answers
The main personalized online learning models, as derived from the contexts, encompass a variety of approaches tailored to meet individual learner needs, leveraging the latest in technology and educational theory. One prominent model is the Content-Based Collaborative Filtering model, which recommends learning materials by computing similarities between the learning materials and the course descriptions, aiming to provide personalized learning experiences to minimize dropouts and enhance learning outcomes. Another significant model is based on sentiment classification within a context-aware network model that utilizes transfer learning to predict learner performance and implement personalized online learning strategies, especially in MOOC video interactions. The use of machine learning models for recommending appropriate learning resources according to the student profile, employing Sequential forward selection (SFS) with AdaBoost as a classifier, showcases a high accuracy and precision rate in personalizing content. Additionally, the development of systems that support self-learning by identifying the needs of individual learners and personalizing the recommendation of forums for MOOCs is another approach to personalized online learning. Digitalization has also led to the creation of intellectual models of personalized learning for educational programs in universities, utilizing matrices of student competencies and fuzzy logic to adapt to each student's level of competence. The consideration of affective information as a personalization parameter in MOOCs to ensure learner-centered learning is another innovative model. Joint modeling of knowledge tracing and hint-taking propensity, which personalizes e-learning plans by predicting probabilities of correct answers and the use of learning aids, represents a co-learning model approach. The personalized online super learner (POSL), an ensemble machine learning algorithm for streaming data, optimizes predictions with respect to baseline covariates for real-time personalization. Lastly, personalized federated learning (PFL) models, such as FedDWA, customize models for individual clients by leveraging dynamic weight adjustment, reducing communication traffic and enhancing model accuracy. These models collectively represent the forefront of personalized online learning, each contributing unique strategies to cater to the diverse needs and competencies of learners in the digital age.
Which is the best eeg Motor Imagery frequency?
5 answers
The optimal EEG Motor Imagery frequency for classification varies based on the study. Research by Deng et al. suggests that a combination of time-frequency-spatial features can enhance classification accuracy for MI EEG signals, with different classifiers performing better based on the feature type. On the other hand, Hema et al. found that sub-band frequencies between 21Hz to 40Hz, including low gamma frequencies, are well-suited for motor imagery classification, achieving high classification rates. Additionally, Wu et al. demonstrated the effectiveness of using different frequency bands (delta, theta, alpha, beta, gamma) for EEG classification, with CNN outperforming linear SVM in MI data classification. Therefore, the choice of the best EEG Motor Imagery frequency may depend on the specific study objectives and the classification methods employed.
What are the best opinion mining techniques?
5 answers
The best opinion mining techniques encompass a variety of approaches based on feature and instance selection methods, sentiment classification, and machine learning strategies. Research suggests that combining TF–IDF feature selection with DROP3 instance selection yields optimal results. Additionally, the Normal Discriminant Piecewise Regressive (NDPR) technique introduces processes like pre-processing, feature extraction, and sentiment classification to enhance accuracy in sentiment analysis. Moreover, a proposed model for educational systems utilizes data preprocessing and opinion mining modules, achieving high accuracy with the K-Means Algorithm and Chi-Square test algorithms. Machine learning-based classification models like Naive Bayes, SVM, genetic algorithms, and decision trees are also effective in sentiment analysis. These techniques collectively contribute to efficient sentiment classification and opinion mining in various domains.