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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Prediction of Compressive Strength and Modulus of Elasticity of Concrete Using Machine Learning Models
Author(s) Taihao Han, Aditya Kumar
On-Site Speaker (Planned) Taihao Han
Abstract Scope Machine learning (ML) techniques are increasingly used to simulate and predict quantitative composition-property relationships of concrete. This poster presents a comprehensive study using multilayer perceptron artificial neural network (MLP-ANN), support vector machine (SVM), and random forest (RF) algorithms to predict the different properties of cement system. The RF constructs multiple decision trees to vote for the final predicted results. The SVM utilizes the hyperplanes of data sets in a high or infinite dimensional space to determine the relationship between inputs and outputs. The MLP-ANN consists of several neuron layers- an input layer, an output layer, and one more hidden layers- to compute the final prediction. The accuracy of predicted capabilities of these techniques is investigated by using concrete data from different literatures and our lab experimental results. The metrics used for evaluation of prediction accuracy of these algorithms included five different statistical parameters and a composite performance index (CPI).


Atomistic Modeling of Fundamental Deformation Mechanisms in MAX Phases
Development of Boron Oxide Potentials for Computer Simulations of Multi-component Oxide Glasses
Embedding Machine Learning in the Physics of Disordered Solids
Exploring Molecular Dynamics Descriptors to Improve Machine Learning Predictions of Glass Forming Ability
Force-Enhanced Refinement of the Atomic Structure of Silicate Glasses
Genesis of “Free” Carbon in Silicon Oxycarbide Ceramics
Impact of Carbon Morphology on Mechanical Properties of SiCO Ceramics
Machine Learning-aided Development of Empirical Force-fields for Glassy Materials
Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons
Machine Learning Applied to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
Machine Learning to Predict the Elastic Properties of Glasses
Peridynamics Modeling of Impact-induced Crack Patterns in Glass
Physics-Based Machine Learning Models for High Throughput Screening of Novel Scintillator Chemistries
Predicting Nuclear Magnetic Resonance Parameters in Ceramics Using Density Functional Theory
Prediction of Compressive Strength and Modulus of Elasticity of Concrete Using Machine Learning Models
Reactive MD Simulations of Polysiloxanes: Modeling the Polymer-to-Ceramic Route towards Silicon Oxycarbide Ceramics
Role of Multi-state Hydrogen during Mayenite Electride Formation by First-principles Calculation
The Stability, Structure and Properties of the Zeta Phase in the Transition Metal Carbides
The Thermophysical Properties of TcO2
Thermal Conductivity of a Glass Material by First-principles Molecular Dynamics: The Case of GeTe4
Tuning Electronic Properties in II-IV-V2 Semiconductors via Sub-lattice Configurational Disorder

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