Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques.

Yue Xu,Marta Dudek,Krzysztof Adam Ostrowski,Ayaz Ahmad,Waqas Ahmad,Fahid Aslam,Panuwat Joyklad

doi:10.3390/ma14227034

Abstract

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.

Highlights

Concrete is the most commonly used material in construction [1,2,3,4,5]
supplementary cementitious materials (SCMs) can be used to make a variety of concretes, including low-carbon concrete (LCC), self-compacting concrete (SCC), high strength concrete (HSC), and highperformance concrete (HPC) [13,14,15]
The utilization of SCMs in HPC results in reduced costs, reduced heat production, decreased porousness, and enhanced chemical resistance, all of which contribute to the lower maintenance costs associated with structures created using HPC [20,21]

Summary

Introduction

Concrete is the most commonly used material in construction [1,2,3,4,5]. One of the necessary components of concrete is its binder, i.e., cement. HPC has been employed in the construction of several concrete structures because of its superior features, which include high strength, durability, and efficiency [18,19]. These qualities are often obtained by incorporating SCMs (especially silica fume) into HPC. The utilization of SCMs in HPC results in reduced costs, reduced heat production, decreased porousness, and enhanced chemical resistance (due to high tightness caused by the usage of a new generation of admixtures in concrete mixtures), all of which contribute to the lower maintenance costs associated with structures created using HPC [20,21]

Objectives

Results

Discussion

Conclusion