OtterMars

kmFCV

K-fold-m-step forward cross-validation is a new approach of evaluating extrapolation performance in materials property prediction.

Predicting_Concrete_Compressive_Strength

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.

Prediction-of-cement-compressive-strength-using-stacked-ensemble-modelling

The actual concrete compressive strength (MPa) for a given mixture under aspecific age (days) was determined from laboratory. Data is in raw form (not scaled).The data has 8 quantitative input variables, and 1 quantitative output variable, and 1030 instances (observations).Context:Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. Summary of steps taken and performance achieved: Multiple models with different levels of complexity were attempted. The dependent and independent variables seem to have a nonlinear relationship as the performance of models improved with increasing complexity. MAPE was selected as the evaluation metric.Regularization, feature selection and hyper-parameter tuning was employed to improve the model performance. The models attempted are Linear Regression with no regularization Ridge and Lasso Gradient Boosting Random Forest XGboost Support Vector Machine Stacking - ensemble of the best estimators of the above tuned models with a meta regressor (i.e. Ridge) which gave the best result (MAPE of less than 10)

mt-cgcnn

NeurIPS 2018 MLMM Workshop: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

Fatigue-life-prediction-of-aluminum-alloy

Support-Vector-Machine-for-Material-Science

Support Vector Machine (SVM) Algorithms is used for predicting the mechanical properties and developing the models of Cast Aluminum alloys. The developed models are evaluated by Mean Absolute Percentage Error (MAPE). Two models with SVM are developed for Ultimate Tensile Strength(UTS) and Fatigue Strength prediction.

BurnishingFatigue

results from fatigue testing of burnished 304 and 316L steels

Fatigue-Strength-of-Steel-ML-Project

Finding Fatigue Strength of Steel using ML and Data Science Techniques

Fatigue-Machine-Learning

Jupyter Notebook application of Scikit-Learn Machine Learning Methods to steel dataset to predict the fatigue life.