Sequential model4/9/2023 the Mori-Tanaka (MT) model (Mori andTanaka 1973, Benveniste 1987), the Self-consistent (SC) model (Budiansky 1965, Hill 1965, the Generalised self-consistent (GSC) model (Christensen and Lo 1979), the differential model (Norris 1985), etc. Along the line of the homogenisation technique, micromechanical models, i.e. In general, it was concluded that the homogenization technique could be a reliable and effective way to analyze the raveling distress of a PA mix pavement. The commonly available pavement analysis tool 3D-MOVE was used to compute the response of the analyzed pavement. In the real field-like example, the Mori–Tanaka model was used as a homogenization technique. To demonstrate the application of the proposed approach, a real field-like example was presented. This study aims to propose a new approach to analyze the raveling distress of a PA mix pavement by using the homogenization technique. As an alternative, the homogenization technique provides a way to calculate the stress and strain fields at the component level without the need for much computation power. However, they require the development of large FEM meshes and large-scale computational facilities. Computational models based on finite element methods (FEM), discrete element methods (DEM), or both, can be used to compute local stress and strain fields. To analyze the raveling distress of a PA mix pavement, the stress and strain fields at the component level are required. Raveling is one of the most prominent distresses that occur on PA mix pavements. However, pavements with PA mixes are known to have a shorter lifetime and higher maintenance costs as compared with traditional dense asphalt mixes. The benefits of using PA mixes include, among others, the reduction of noise and the improvement of skid resistance. The prerequisite knowledge required in order to be successful in this course is proficiency in the python programming language, (this course uses python 3), knowledge of general machine learning concepts (such as overfitting/underfitting, supervised learning tasks, validation, regularisation and model selection), and a working knowledge of the field of deep learning, including typical model architectures (MLP/feedforward and convolutional neural networks), activation functions, output layers, and optimisation.In the Netherlands, more than 80% of the highways are surfaced by porous asphalt (PA) mixes. This course is intended for both users who are completely new to Tensorflow, as well as users with experience in Tensorflow 1.x. The release of Tensorflow 2 marks a step change in the product development, with a central focus on ease of use for all users, from beginner to advanced level. Tensorflow is an open source machine library, and is one of the most widely used frameworks for deep learning. In addition there is a series of automatically graded programming assignments for you to consolidate your skills.Īt the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop an image classifier deep learning model from scratch. You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models. Welcome to this course on Getting started with TensorFlow 2!
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