prediction of the optimum asphalt content using artificial

Artificial Intelligence for Robotics | Udacity Free Courses

About this Course. Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics.

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Prediction of the optimum asphalt content using artificial

The performance of the asphalt mix is significantly influenced by the optimum asphalt content (OAC). The asphalt content is responsible for coating the aggregate surface and filling the voids between the aggregate particles. Thus, the aggregate gradation has a significant influence on the required asphalt content. The Marshall design process is the most common method used for estimating the

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What is Natural Language Processing? | SAS

Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.

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Marshall Mix Design - Pavement Interactive

The Marshall mix design method can use any suitable method for estimating optimum asphalt content and usually relies on local procedures or experience. Sample Asphalt Binder Contents Based on the results of the optimum asphalt binder content estimate, samples are typically prepared at 0.5 percent by weight of mix increments, with at least two

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Comparison of artificial neural network models for

Lloyd H.C. Chua, Tommy S.W. Wong, Runoff forecasting for an asphalt plane by Artificial Neural Networks and comparisons with kinematic wave and autoregressive moving average models, Journal of Hydrology, 10.1016/j.jhydrol.2010.11.030, 397, 3-4, (191-201), (2011).

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Artificial Intelligence Risk & Governance - Artificial

1.1 Document Purpose & Scope. The business uses, regulatory interest and research in artificial intelligence and machine learning (AI/ML) have seen an exponential increase over the last few years. Discussions regarding the use of Artificial Intelligence (AI) and Machine Learning (ML) have gained momentum as financial services firms evaluate the

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Evolution of control systems with artificial intelligence

Artificial intelligence use cases. DRL-based brains have been designed for over 100 use cases and have been deployed spanning a wide variety of industries and vertical markets. Several use cases and corresponding unique, challenges or applications are presented to illustrate the power of DRL-based brains.

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S. Sonny kim‬ - ‪Google Scholar

2003. Simple methods to estimate inherent and stress-induced anisotropy of aggregate base. SH Kim, DN Little, E Masad. Transportation Research Record 1913 (1), 24-31. , 2005. 39. 2005. Determination of aggregate physical properties and its effect on cross-anisotropic behavior of unbound aggregate materials.

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Prediction of Marshall test results for polypropylene modified dense

waste PP at optimum bitumen content. filled with asphalt and air voids in order to predict the Marshall stability, flow and Marshall Quotient val-.

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Machine learning predicts behavior of stainless steel at

To the naked eye, a sheet of stainless steel presents a smooth, polished, homogenous surface. The same material when viewed at 400 times magnification reveals its true jumbled structure—different crystal shapes, joined at wildly different angles. Researchers at the University of Illinois Urbana-Champaign used data from high-resolution images of stainless-steel samples to train neural

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DeepMind Wants to Use AI to Transform Soccer | WIRED

Now, defending Premier League champion Liverpool has joined forces with DeepMind to explore the use of artificial intelligence in the soccer world. A paper by researchers at the two organizations

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Heart Attack Risk Prediction Using Machine Learning | by

Heart disease is the major cause of morbidity and mortality globally: it accounts for more deaths annually than any other cause. According to the WHO, an estimated 17.9 million people died from heart disease in 2016, representing 31% of all global deaths. Over three quarters of these deaths took place in low- and middle-income countries.

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Prediction of Asphalt Mixture Resistance Using Neural Network via

Then some analytically model using visual characteristics and artificial intelligence methods are proposed. Finally the results from attained multi-pages image 

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Mahmood Akbari‬ - ‪Google Scholar

Prediction of concrete compressive strength using ultrasonic pulse velocity test and artificial neural network modeling F Khademi, M Akbari, SM Jamal Revista Romana De Materiale-Romanian Journal Of Materials 46 (3), 343-350 , 2016

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Artificial Intelligence in Salesforce - EzineArticles

Artificial Intelligence (AI) is the concept of having machines "think like humans" - in other words, perform tasks like reasoning, planning, learning, and understanding language. Salesforce is focusing on creating a platform for solving the customer problems across Sales, Service, Marketing and IT in a completely new way by using Salesforce

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PDF Nonlinear Structural Deformation Modelling of Asphalt

Asphalt Layers using Artificial Neural Networks Sunny Deol G1, Vikas Kumar Reddy T2 Further the final 2-D rut prediction modified constitutive model shows a satisfactory result with a correlation value of 0.732. density levels at optimum moisture content were also

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Accumulated strain prediction of polypropylene modified

In order to determine the optimum bitumen content, it is required to perform Marshall stability and flow tests. To determine the optimum bitumen content, the bitumen contents corresponding to the mixtures with maximal stability and unit weight, 4% air voids and 70% voids filled with asphalt, were found and averaged according to the limits given by the General Directorate of Highways of Turkey

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PDF) An artificial neural network base prediction model

10] Prediction of fatigue life of ru bberized asphalt concrete mix tures containing reclaimed asphalt pavement using artificial neural networks, Xiao, F., Amirkhanian, A., and Juang, C.H

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Development of Fatigue Predictive Models of Rubberized

the use of crumb rubber is effective in improving the aging resistance of rubberized asphalt concrete, 2) the addition of RAP decreased the asphalt content and increased the ITS values, 3) the developed specific regression models predicted a reasonable fatigue response of mixture, and the measured and predicted fatigue values

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Google Maps 101: How AI helps predict traffic and

To predict what traffic will look like in the near future, Google Maps analyzes historical traffic patterns for roads over time. For example, one pattern may show that the 280 freeway in Northern California typically has vehicles traveling at a speed of 65mph between 6-7am, but only at 15-20mph in the late afternoon.

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Superpave Mix Design - Pavement Interactive

Selection of Optimum Asphalt Binder Content. The optimum asphalt binder content is selected as that asphalt binder content that results in 4 percent air voids at N design. This asphalt content then must meet several other requirements: Air voids at N initial > 11 percent (for design ESALs ≥ 3 million). See Table 5 for specifics.

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Investigation of Strength and Fatigue Life of Rubber Asphalt Mixture

26 Jul 2020 life and optimum asphalt content also can be predicted with the BPNN model. Xiao et al. used regression analysis and neural network methods 

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Using AI to Understand What Causes Diseases

They use "what if" reasoning to predict how a continuous-time trajectory (e.g., disease outcomes) will progress under different sequences of actions (e.g., health care interventions).

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Predicting pavement performance utilizing artificial

According to the Bureau of Transportation Statistics, in 2012 around 263.36 billion tons of goods valued at $195.99 billion were transported on Iowa highways (BTS, 2012). PMSs that use robust pavement prediction models are needed to ensure continued optimum performance of Iowa highways.

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Explore the Benefits of Leveraging Artificial Intelligence

Artificial intelligence is a computer science branch that creates smart systems. It simulates thoughts of human intelligence in machines that can think and act like humans. AI helps in learning and problem-solving with its advanced algorithms. This technology finds the optimum way to reach the desired result with rational analysis and actions.

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AI bias may worsen COVID-19 health disparities for people

The COVID-19 pandemic has had a hugely outsized impact on people of color, worsened by existing disparities in healthcare and systemic racism. At the same time, researchers noted, COVID-19 prediction models can present serious shortcomings, especially regarding potential bias. In a recent systematic review of COVID-19 prediction models, they

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The Top 21 Security Predictions for 2021 - GovTech

11) Forrester — Similar to Gartner, there is more free Forrester prediction content this year than I have ever seen. I am impressed with the number of predictions and scope of coverage that can

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Artificial Intelligence: Terms marketers need to know

This article and expertise was originally published in Business2Community. Artificial Intelligence (AI) continues to make its way into the world, influencing popular culture (think Steven Spielberg's "A.I.", or Disney's "Big Hero 6") and becoming a disruptor is a variety of industries.

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Prediction of the Compressive Strength of Recycled

To guarantee the safe use of RAC, a compressive strength prediction model based on artificial neural network (ANN) was built in this paper, which can be applied to predict the RAC compressive strength for 28 days. A data set containing 88 data points was obtained by relative tests with different mix proportion designs.

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Response surface optimization and artificial neural

The present study describes the renewable, environment-friendly approach for the production of biodiesel from low cost, high acid value mahua oil under supercritical ethanol conditions using carbon dioxide (CO 2) as a co-solvent.CO 2 was employed to decrease the supercritical temperature and pressure of ethanol. A response surface method (RSM) is the most preferred method for optimization of

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Application of Neural Networks and Adaptive-Network- Based

Based Fuzzy System in the Prediction of Optimum Bitumen Content for Asphaltic Concrete Mixtures Moussa. S. Elbisya*, M. H. Alawib, M. A. Saif c a,b,cDepartment of Civil Engineering, Umm Al-Qura University, Makkah, Saudi Arabia aEmail: [email protected] bEmail: [email protected] cEmail: [email protected] Abstract

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