PENALIZED REGRESSION MODELS FOR CONCRETE STRENGTH ESTIMATION

Author

Chandra Khadka, Southern Illinois University CarbondaleFollow

Date of Award

6-1-2021

Degree Name

Master of Science

Department

Civil Engineering

First Advisor

Tezcan, Jale

Abstract

Concrete compressive strength is one of the most important material properties affectingthe design of concrete structures. Strength that will be achieved once the concrete sets should be correctly predicted prior to pouring the concrete. Regression techniques can be used to calculate the 28-day concrete strength with a level of certainty. This thesis deals with the data modelling and analysis of 28-day compressive strength of high-performance concrete. Historical data on various mix compositions of high-performance concrete was obtained from University of California, Irvine repository. The data had 8 predictors and 1 response variable. In this thesis, three penalized regression approaches, namely, ridge, lasso, and elastic net were used to create a predictive model for compressive strength, and the performance of these model were compared to the traditional multiple linear regression model. Holdout sets from 2% to 40% at an increment of 2% were taken. Every regression algorithm was designed to conduct regression on 30 sets of randomly partitioned data. The performance of models was assessed using coefficient of multiple determination, RMSE, and residual plots. All regression techniques were able to predict the concrete strength with about 75% accuracy level.