Journal ArticleDOI
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
Reads0
Chats0
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
More filters
Journal ArticleDOI
Deep learning from physicochemical information of concrete with an artificial language for property prediction and reaction discovery
TL;DR: In this article , an approach to discover the intrinsic relationships between the physicochemical properties of the ingredients and mechanical properties of concrete is presented, where an artificial language is used to represent concrete mixtures and physicochemical information of their ingredients, and a feature extraction method based on character-level N-grams is proposed.
Posted ContentDOI
Carbon price prediction based on multiple decomposition and XGBoost algorithm
TL;DR: Wang et al. as discussed by the authors proposed a carbon price prediction model, Multi-Decomposition-XGBOOST, which is based on Sample Entropy and a new multiple decomposition algorithm.
Journal ArticleDOI
Deep Neural Networks for the Estimation of Masonry Structures Failures under Rockfalls
Olga Mavrouli,Athanasia D. Skentou,Josep Maria Carbonell,M.Z. Tsoukalas,María Amparo Núñez-Andrés,Panagiotis G. Asteris +5 more
TL;DR: In this article , the authors applied artificial intelligence methods to assess the expected damage of masonry walls which are subjected to rockfall impacts, and selected the ANN LM 4-21-1 model to optimally assess the wall damage.
Journal ArticleDOI
A Machine Learning-Based Surrogate Finite Element Model for Estimating Dynamic Response of Mechanical Systems
TL;DR: In this article , an efficient approach for improving the predictive understanding of dynamic mechanical system variability is developed, which requires low model assessment time through the fitting of surrogate models, which can accelerate finite element analysis and prevent rerunning complex simulations.
Journal ArticleDOI
Prediction of thermal conductivity ofconcrete under variable temperatures in cold regions using projection pursuit regression
TL;DR: In this article , a projection pursuit regression (PPR) model was proposed to predict the thermal conductivity of concrete (TCC) under variable temperatures in cold regions, which can directly apply numerical functions to describe the ridge functions obtained by projection.
References
More filters
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.