Machine-Learning-Algorithm to predict the High-Performance concrete compressive strength using multiple data

Abstract

Purpose The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets. Design/methodology/approach In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models. Findings The five statistical indicators like coefficient of determination (R2), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable. Originality/value The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Machine Learning Method to Explore the Correlation between Fly Ash Content and Chloride Resistance

“…On the other hand, the R 2 values for the test sets of the GBR and DT algorithms were 0.8859 and 0.8632, respectively, considerably lower than their respective training sets. The low error values of MSE and MAE proved that RF is a good model to predict total chloride ion content [ 71 , 72 ]. This indicates that GBR and DT models lacked good generalization ability and prediction stability, despite being able to predict functional relationships within a given database.…”

Optimasi Mix Design Beton Melalui Teknologi Machine Learning

“…Machine learning merupakan cabang dari kecerdasan buatan yang memungkinkan komputer untuk belajar dari data dan pengalaman, serta dapat melakukan tugas-tugas kompleks tanpa harus secara eksplisit diprogram [11]. Kamath et al (2022) menemukan bahwa Decision regression tree, Random forest regression, Lasso regression, Ridge regression, dan Multiple linear regression tampil paling baik dalam sebuah studi perbandingan lima model regresi Machine Learning (ML) [12]. Dua set data dari masa lalu dianalisis menggunakan lima model regresi ML yang berbeda, dan dua dataset dari laboratorium mutakhir digunakan.…”

Machine learning in concrete technology: A review of current researches, trends, and applications

“…A huge amount of data has been produced by scientific methods either on the laboratory scale or at the full scale (Buyya et al, 2013) and these historical data are of great asset to be used to the maximum advantage using ML to produce platforms for judging the concrete properties (Kamath et al, 2022). The main process of data analysis in machine learning involves different steps the first is data ingestion where data are collected and imported into the assigned analysis platform, while the second step is processing these data by filtering cleaning the data, or even data manipulation, the third is data analysis by choosing the best-fit model either optimization or prediction, train the model, evaluate the model and the last step is data visualization or making the prediction (Bhattacharya, 2021).…”