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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Journal ArticleDOI
A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis
TL;DR: In this paper , a surrogate machine learning model was developed with the aim of predicting seismic graphite core displacements from crack configurations for the advanced gas-cooled reactor, trained on a dataset generated by a software package which simulates the behaviour of the graphite cores during a severe earthquake.
Journal ArticleDOI
Alkali–silica reaction expansion prediction in concrete using hybrid metaheuristic optimized machine learning algorithms
Proceedings ArticleDOI
A Grade Prediction Method Based on Long Short-Term Memory Network in Smart Campus
TL;DR: In this article , a support vector machine, a multilayer perceptron, and an enhanced Long Short-Term Memory Network (LSTM) model were employed to forecast students' final grades.
Journal ArticleDOI
Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids
TL;DR: In this paper , a multilayer extreme learning machine (MELM) hybridized with the cuckoo optimization algorithm (COA) was applied to estimate rheology and filtration properties with FD, S%, and March funnel viscosity (MFV) as input variables.
Journal ArticleDOI
Predicting Surface Resistivity on Concretes Containing Potential Supplementary Cementitious Materials Cured at Nonelevated and Elevated Temperatures
TL;DR: In this paper , Gaussian process regression models were used to predict the surface resistivity (SR) values of concretes cured at two different temperatures, i.e., 23°C and 38°C.
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.