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
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Journal ArticleDOI
Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete
Anas Abdulalem Alabdullh,Rahul Das Biswas,Jitendra Gudainiyan,Kaffayatullah Khan,Abdullah Hussain Bujbarah,Qasem Ahmed Alabdulwahab,Muhammad Nasir Amin,Mudassir Iqbal +7 more
TL;DR: The newly developed HENS model has a great deal of promise to be a fresh approach to deal with the overfitting problems of CML models and thus may be utilised to forecast the IFB of FRPL.
Journal ArticleDOI
An extended multi-model regression approach for compressive strength prediction and optimization of a concrete mixture
TL;DR: In this article , a combined multi-model framework is presented where the regression methods based on artificial neural network, random forest regression and polynomial regression are jointly implemented for compressive strength prediction with a higher accuracy.
Journal ArticleDOI
Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam
TL;DR: In this paper , the authors have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging(SOM), and Bagging 5P to predict historical water levels in the study area.
Journal ArticleDOI
Prediction of Concrete Peak Load and Compressive Failure Strength Using Machine Learning
TL;DR: In this paper , the influence of the initial size of cylindrical normal-weight concrete considering three different mixtures is presented, and peak load and compressive failure strength of multiple sizes concretes are predicted using machine learning.
Journal ArticleDOI
Artificial Intelligence in Concrete Materials: A Scientometric View
TL;DR: In this article , the main research interests and knowledge structure of the existing literature on AI for concrete materials were uncovered, and a total of 389 journal articles published from 1990 to 2020 were retrieved from the Web of Science.
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.