Journal ArticleDOI
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
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TLDR
The newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.About:
This article is published in Cement and Concrete Research.The article was published on 2021-07-01. It has received 166 citations till now. The article focuses on the topics: Overfitting & Multivariate adaptive regression splines.read more
Citations
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Proceedings ArticleDOI
A Duplex Method for Prediction of Concrete Strength Using Dimensionality Reduction
TL;DR: In this paper , a dual approach for predicting the concrete strength where the first approach is based on consideration of all the features for training and in the second approach, dimensionality reduction is performed using Principal Component Analysis (PCA) technique.
Journal ArticleDOI
Machine learning in concrete technology: A review of current researches, trends, and applications
TL;DR: A review of current applications and trends of machine learning techniques and applications in concrete technology is presented in this article , where it is found that machine learning can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life.
Journal ArticleDOI
Evaluation of compressive strength of the HPC produced with admixtures by a novel hybrid SVR model
References
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Journal ArticleDOI
Multivariate Adaptive Regression Splines
TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
Book ChapterDOI
Ensemble Methods in Machine Learning
TL;DR: Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
Journal ArticleDOI
Industrially interesting approaches to “low-CO2” cements ☆
TL;DR: In this paper, the authors discuss the practicality of replacing portland cements with alternative hydraulic cements that could result in lower total CO 2 emissions per unit volume of concrete of equivalent performance.
Journal ArticleDOI
Advances in alternative cementitious binders
TL;DR: In this paper, four promising alternative binders available as alternatives to Portland cement are discussed, namely calcium aluminate cement, calcium sulfoaluminate cements, alkali-activated binders, and supersulfated cements.
Journal ArticleDOI
Modeling of strength of high-performance concrete using artificial neural networks
TL;DR: In this paper, a set of trial batches of HPC was produced in the laboratory and demonstrated satisfactory experimental results, which led to the following conclusions: 1) A strength model based on ANN is more accurate than a model based based on regression analysis; and 2) It is convenient and easy to use ANN models for numerical experiments to review the effects of the proportions of each variable on the concrete mix.