Automatic Modeling for Concrete Compressive Strength Prediction Using Auto-Sklearn

Abstract

Highlights: What are the main findings? It’s feasible to use AutoML for concrete compressive strength prediction. Concrete compressive strength model based on AutoML has stronger robustness than ML. What is the implication of the main finding? AutoML can reduce modeling time and reliance upon engineer modeling experience. Machine learning is widely used for predicting the compressive strength of concrete. However, the machine learning modeling process relies on expert experience. Automated machine learning (AutoML) aims to automatically select optimal data preprocessing methods, feature preprocessing methods, machine learning algorithms, and hyperparameters according to the datasets used, to obtain high-precision prediction models. However, the effectiveness of modeling concrete compressive strength using AutoML has not been verified. This study attempts to fill the above research gap. We construct a database comprising four different types of concrete datasets and compare one AutoML algorithm (Auto-Sklearn) against five ML algorithms. The results show that Auto-Sklearn can automatically build an accurate concrete compressive strength prediction model without relying on expert experience. In addition, Auto-Sklearn achieves the highest accuracy for all four datasets, with an average (Formula presented.) of 0.953; the average (Formula presented.) values of the ML models with tuned hyperparameters range from 0.909 to 0.943. This study verifies for the first time the feasibility of AutoML for concrete compressive strength prediction, to allow concrete engineers to easily build accurate concrete compressive strength prediction models without relying on a large amount of ML modeling experience.