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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Book ChapterDOI
Liquefaction hazard mitigation using computational model considering sustainable development
TL;DR: In this article , an artificial neural network (ANN) model has been developed for predicting the liquefaction susceptibility of soil deposits, which is a function of the plasticity index, liquid limit, water content, and other geotechnical parameters.
Journal ArticleDOI
Predicting the Fracture Characteristics of Concrete Using Ensemble and Meta-heuristic Algorithms
Quan Zhang,Xiao Bei Zhou +1 more
Journal ArticleDOI
Forecasting the self-healing capacity of engineered cementitious composites using bagging regressor and stacking regressor
TL;DR: In this article , two different ensemble machine learning (ML) algorithms i.e. bagging regressor (BR) and stacking regressor(SR) were employed to estimate ECC's self-healing capacity.
Posted ContentDOI
Using Explainable Machine Learning to Predict Compressive Strength of Blended Concrete: A Data-Driven Metaheuristic Approach
TL;DR: In this article , the compressive strength of blended concrete is estimated using machine learning techniques, including XGBoost, decision trees (DT), deep neural networks (DNN), and linear regression (LR).
Journal ArticleDOI
Processing Optimization of Shear Thickening Fluid Assisted Micro-Ultrasonic Machining Method for Hemispherical Mold Based on Integrated CatBoost-GA Model
TL;DR: In this paper , an STF-MUM polishing method that combines STF with MUM is proposed to improve the surface roughness of the silicon micro-hemisphere concave molds inside the MEMS hemispherical resonant gyroscope.
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.