concrete properties machine learning example

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Concrete properties machine learning example

It is measured by breaking cylindrical concrete specimens in a compression-testing machine. The objective of this example is to design concrete mixtures with specified properties and reduced costs. To do that, a compressive strength's predictive model is built from a set of tests performed in the laboratory for 425 specimens.

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Machine Learning: Linear Regression Example: Concrete ...

Jul 02, 2012  There is a fun archive of machine learning data sets maintained by UC Irvine. For a concrete example, let's take the Concrete Compressive Strength data set and try linear regression on it. (Get it? Concrete?Ha ha ha!) There are 1030 points in

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Machine learning prediction of mechanical properties of ...

Nov 10, 2020  Machine learning models are more accurate, flexible and can be retrained with updated databases. ... Accurate prediction of the mechanical properties of concrete has been a concern since these properties are often required by design codes. The emergence of new concrete mixtures and applications has motivated researchers to pursue reliable ...

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Machine learning in concrete strength simulations: Multi ...

Dec 30, 2014  1. Introduction. An important research problem in materials science is predicting the mechanical properties of construction materials .For many years, the use of high performance concrete (HPC) in various structural applications has markedly increased .Cement materials such as fly ash, blast furnace slag, metakaolin, and silica fume are often used to increase the compressive strength and ...

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Cement and Concrete Research

Machine learning Regression analysis ABSTRACT The use of statistical and machine learning approaches to predict the compressive strength of concrete based on mixture proportions, on account of its industrial importance, has received significant attention. However, pre-vious studies have been limited to small, laboratory-produced data sets.

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Cement and Concrete Research

employed to approximate properties of concrete, which include a variety of linear combination, statistical, machine learning, and physics-based models that are required to optimize the proportions of a mixture. We then review and discuss computational methods used to optimize concrete mixtures in the context of surveyed lit-erature.

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Machine Learning: Linear Regression Example: Concrete ...

Jul 02, 2012  There is a fun archive of machine learning data sets maintained by UC Irvine. For a concrete example, let's take the Concrete Compressive Strength data set and try linear regression on it. (Get it? Concrete?Ha ha ha!) There are 1030 points in

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How to explore different properties of concrete by using ...

Aug 09, 2019  I saw many research papers that used machine learning (ML) techniques such as artificial neural networks, gene expression programming etc to predict the compressive strength of concrete.

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Machine learning approaches for estimation of compressive ...

Aug 29, 2020  Estimation of compressive strength of rubberized concrete is important for engineering safety. In this study, measured data (the compressive strength of rubberized concrete and its impacting factors) were collected by literature review (457 samples). In order to accurately predict the compressive strength of rubberized concrete, four machine learning models [artificial neural network (ANN), k ...

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Properties of Concrete - Structural Engineers

Properties of Concrete for use in Eurocode 2 This publication is aimed at providing both civil and structural design engineers with a greater knowledge of concrete behaviour. This will enable the optimal use of the material aspects of concrete to be utilised in design. Guidance relates to the use of concrete properties for design to Eurocode 2

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6 – Interpretability – Machine Learning Blog [email protected] ...

Aug 31, 2020  Figure 1: Interpretability for machine learning models bridges the concrete objectives models optimize for and the real-world (and less easy to define) desiderata that ML applications aim to achieve. Introduction. The objectives machine learning models optimize for do not always reflect the actual desiderata of the task at hand.

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5.9 Shapley Values Interpretable Machine Learning

Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable. ... 2.5 Properties of Explanations; 2.6 Human-friendly Explanations. ... A concrete example: The machine learning model works with 4 features ...

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CHAPTER 4. Reinforced Concrete

CHAPTER 4. REINFORCED CONCRETE Slide No. 3 ENCE 454 ©Assakkaf Steel is a high-cost material compared with concrete. It follows that the two materials are best used in combination if the concrete is made to resist the compressive stresses and the steel the tensile stresses. Concrete cannot withstand very much tensile stress without cracking.

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Machine Learning and its Applications in Construction ...

Applications of Machine Learning in Construction 1. Better Designs. Machine learning can improve designs to make them more suitable for the end-user. For example, if a firm wants to customize its office space based on its specific needs, ML can help predict the frequency of use for each room and present a design that is apt for the needs of the ...

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Properties of Concrete - Workability, Setting, Bleeding ...

Mar 19, 2017  Fresh concrete is that stage of concrete in which concrete can be moulded and it is in plastic state. This is also called "Green Concrete". Another term used to describe the state of fresh concrete is consistence, which is the ease with which concrete will flow. The transition process of changing of concrete from plastic state to hardened state.

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Machine Learning in Astronomical Data Analysis

Jan 07, 2019  [2:52pm-3:03pm] Dan Patnaude (Smithsonian Astrophysical Observatory) Classifying Supernova Remnant Spectra with Machine Learning. Abstract: There is a clear connection between the evolutionary properties of a massive star and the properties of the resultant supernova and supernova remnant. Here we present new results where we have modeled 45,000 supernova remnants to ages

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STRUCTURE magazine Rheological Properties of Concrete

The rheological (flow) properties of concrete are essential for the construction industry, because concrete, for different elements of a structure, is placed into the formwork while it is in its plastic state. The flow properties affect not only proper concrete placement, consolidation, and finishing but also the hardened state properties such ...

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1 What is Machine Learning? - Princeton University

2 Examples of Machine Learning Problems There are many examples of machine learning problems. Much of this course will focus on ... • What are the intrinsic properties of a given learning problem that make it hard or ... if not concrete algo-rithms, that will be helpful in designing practical algorithms. Through theory, we hope ...

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Machine learning enables prompt prediction of hydration ...

Feb 16, 2021  Concrete—a mixture of ordinary portland cement, water, and aggregates—is the foundational material used in construction of various forms of surface and sub-surface infrastructure.

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Example – modeling the strength of concrete with ANNs ...

Example – modeling the strength of concrete with ANNs In the field of engineering, it is crucial to have accurate estimates of the performance of building materials. These estimates are required in order to develop safety guidelines governing the materials used in the construction of

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On the Use of Machine Learning Models for Prediction of ...

Compared with traditional prediction methods, an advantage of machine learning is that the prediction could be made without knowing the exact relationship between features and compressive strength. Machine learning models have been used for predicting compres-sive strength of concrete for a long time [19,20]. Different ML models, from simple linear

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Machine learning prediction of mechanical properties of ...

DOI: 10.1016/j.conbuildmat.2020.119889 Corpus ID: 224855556. Machine learning prediction of mechanical properties of concrete: Critical review @article{Chaabene2020MachineLP, title={Machine learning prediction of mechanical properties of concrete: Critical review}, author={W. Chaabene and Majdi Flah and M. Nehdi}, journal={Construction and Building Materials}, year={2020}, volume={260},

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6 – Interpretability – Machine Learning Blog [email protected] ...

Aug 31, 2020  Figure 1: Interpretability for machine learning models bridges the concrete objectives models optimize for and the real-world (and less easy to define) desiderata that ML applications aim to achieve. Introduction. The objectives machine learning models optimize for do not always reflect the actual desiderata of the task at hand.

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Mechanical Properties of Concrete and Steel

Mechanical Properties of Concrete and Steel Reinforced Concrete (RC, also called RCC for Reinforced Cement Concrete) is a widely used construction material in many parts the world. Due to the ready availability of its constituent materials, the strength and economy it provides

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Example – modeling the strength of concrete with ANNs ...

Example – modeling the strength of concrete with ANNs In the field of engineering, it is crucial to have accurate estimates of the performance of building materials. These estimates are required in order to develop safety guidelines governing the materials used in the construction of

Get Price

5.9 Shapley Values Interpretable Machine Learning

Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable. ... 2.5 Properties of Explanations; 2.6 Human-friendly Explanations. ... A concrete example: The machine learning model works with 4 features ...

Get Price

Machine Learning in Astronomical Data Analysis

Jan 07, 2019  [2:52pm-3:03pm] Dan Patnaude (Smithsonian Astrophysical Observatory) Classifying Supernova Remnant Spectra with Machine Learning. Abstract: There is a clear connection between the evolutionary properties of a massive star and the properties of the resultant supernova and supernova remnant. Here we present new results where we have modeled 45,000 supernova remnants to ages

Get Price

Frontiers Prediction of Rubber Fiber Concrete Strength ...

Jan 28, 2021  The conventional design method of concrete mix ratio relies on a large number of tests for trial mixing and optimization, and the workload is massive. It is challenging to cope with today's diverse raw materials and the concrete's specific performance to fit modern concrete development. To innovate the design method of concrete mix ratio and effectively use the various complex novel raw ...

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Properties of Concrete every civil engineer must know

Apr 08, 2018  Properties of concrete during its plastic stage:-The chemical interaction between cement and water binds the aggregate into a solid mass. Fresh concrete will be plastic so that it can be moulded to any desired shape. The Fresh concrete should possess following properties. Workability of Concrete: Workability is a complex property of concrete.

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Machine Learning Visualizations with Yellowbrick by ...

Apr 24, 2020  Introduction. Yellowbrick is an open-source python project that wraps the scikit-learn and matplotlib APIs to create publication-ready figures and interactive data explorations. It is basically a ...

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UCI Machine Learning Repository: Concrete Compressive ...

Center for Machine Learning and Intelligent Systems: ... Concrete Compressive Strength Data Set Download: Data Folder, Data Set Description. Abstract: Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients.

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Basic Concepts in Machine Learning

Aug 15, 2020  What are the basic concepts in machine learning? I found that the best way to discover and get a handle on the basic concepts in machine learning is to review the introduction chapters to machine learning textbooks and to watch the videos from the first model in online courses. Pedro Domingos is a lecturer and professor on machine learning at the University of Washing and

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CS 391L: Machine Learning

CS 391L Machine Learning Project Report Format. Below are guidlines on how to write-up your report for the final project. Of course, for a short class project, all of the comments may not be relevant. However, please use it as a general guide in structuring your final report.

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Exploratory Data Analysis for Feature Selection in Machine ...

Machine learning (ML) projects typically start with a comprehensive exploration of the provided datasets. It is critical that ML practitioners gain a deep understanding of: The properties of the data : schema, statistical properties, and so on The quality of the data : missing values, inconsistent data

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