ericdhitchens / Predicting_Concrete_Compressive_StrengthLinks
How would you predict the compressive strength of concrete as a function of its constituent materials and curing time? In this portfolio project, I optimize a model for determining concrete compressive strength using a deep neural network in Tensorflow 2.0 and compare its performance to linear models.
☆12Updated 4 years ago
Alternatives and similar repositories for Predicting_Concrete_Compressive_Strength
Users that are interested in Predicting_Concrete_Compressive_Strength are comparing it to the libraries listed below
Sorting:
- This is a compact code for reliability analysis under uncertainty using a Polynomial Regression Machine Learning approach. The code imple…☆15Updated 6 years ago
- Different modeling techniques like multiple linear regression, decision tree, and random forest, etc. will be used for predicting the co…☆24Updated 5 years ago
- Python scripts for physics-informed neural networks for corrosion-fatigue prognosis☆41Updated 3 years ago
- This project involves using Simulink to create a comprehensive battery model that incorporates both charging and discharging resistances.…☆10Updated 2 years ago
- ☆15Updated 5 years ago
- ☆26Updated 4 months ago
- Sanitized Grey Wolf Optimizer(SGWO)-Support Vector Regressor (SVR)☆75Updated 3 years ago
- Carbon Capture and Sequestration (CCS) has been proposed as a promising and necessary technology for mitigating CO2 and the effects of an…☆29Updated 6 years ago
- Concrete Compressive Strength Prediction using Machine Learning☆25Updated 5 years ago
- Physics-Informed Neural Networks Trained with Particle Swarm Optimization☆21Updated 2 years ago
- Automate hyper-parameters tuning for NNs (learning rate, number of dense layers and nodes and activation function)☆14Updated 5 years ago
- This repository contains code for predicting multiaxial fatigue life of metals using deep learning models (CNN, LSTM, and GRU) combined w…☆19Updated last year
- Machine learning model for complex concentrated alloys/high entropy alloys using TensorFlow☆15Updated 4 years ago
- Using machine learning to predict the performance of supercapacitors.☆11Updated 5 years ago
- mahdi-usask / Wind-Speed-Forecasting-for-wind-power-generation-plant.-Neural-Network-ML-based-prediction-algo.-For largescale wind power penetration Wind speed prediction is a basic requirement of wind energy generation. There are many artificial n…☆46Updated 3 years ago
- Membrane-based dehumidification is currently being considered as a promising solution for the building application due to its low cost an…☆11Updated 4 years ago
- Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication☆17Updated 3 years ago
- ☆12Updated 8 years ago
- Clustering and Image Processing using Fuzzy Logic☆23Updated 2 years ago
- This notebook demonstrates how to implement Tensorflow & queries to forecasting for a very big dataset. ANN with 2 hidden layers has been…☆10Updated 6 years ago
- Genetic Algorithm for Neural Network Architecture and Hyperparameter Optimization and Neural Network Weight Optimization with Genetic Alg…☆12Updated 6 years ago
- Different meta-heuristic optimization techniques for feature selection☆42Updated 5 years ago
- Deep Transfer Learning and Time-Frequency Characteristics-Based Identification Method for Structural Seismic Response☆31Updated 4 years ago
- My master's dissertation on wind turbine fault prediction using machine learning☆58Updated last year
- GWO☆11Updated 5 years ago
- Training Neural Network with Particle Swarm Optimization☆14Updated 6 years ago
- Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as …☆73Updated 4 years ago
- Kriging for Analysis, Design optimization, And expLoration (KADAL)☆19Updated 3 years ago
- Deep LSTM for highly nonlinear system modeling☆56Updated 6 years ago
- Multi-task physics-informed neural networks☆25Updated 2 years ago