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Journal ArticleDOI

Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model

TLDR
The proposed self-adaptive MARS-WCA model demonstrated a robust and significant data-intelligence mode for FCLC compressive strength prediction compared with the benchmark models and experimental formulations.
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This article is published in Construction and Building Materials.The article was published on 2020-01-10. It has received 87 citations till now. The article focuses on the topics: Multivariate adaptive regression splines & Compressive strength.

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Citations
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Journal ArticleDOI

A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

TL;DR: The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model, and it is deducted that the ELm-GWO model is capable of reaching superior performance indices in comparison with those of the other models.
Journal ArticleDOI

Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction

TL;DR: A new water cycle algorithm optimization BP neural network (BPNN) dynamic prediction model (WCA-BPNN), established to make up for the shortcoming of BPNN convergence speed, has faster convergence speed and higher prediction accuracy than the traditional BPNN model, and it is also the most accurate of the four models.
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Machine learning study of the mechanical properties of concretes containing waste foundry sand

TL;DR: M5P algorithm was used to model the compressive strength, modulus of elasticity, flexural strength, and splitting tensile strength of these concretes and indicated that the proposed models can provide reliable predictions of the target mechanical properties.
Journal ArticleDOI

Machine learning prediction of compressive strength for phase change materials integrated cementitious composites

TL;DR: In this paper, machine learning is used for the first time to predict the compressive strength of PCM-integrated cementitious composites, and a dataset of 154 cement-based mixtures incorporating PCM microcapsules was assembled.
Journal ArticleDOI

Hybridization of metaheuristic algorithms with adaptive neuro-fuzzy inference system to predict load-slip behavior of angle shear connectors at elevated temperatures

TL;DR: The results of the paper show that the SC approach is applicable in the behavior prediction of angle connectors at elevated temperatures and it is deducted that the ANFIS-PSO-GA model is capable of providing better estimations of load and slip in comparison with those of RBFN and ELM models.
References
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Journal ArticleDOI

Support-Vector Networks

TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Book

Applied multiple regression/correlation analysis for the behavioral sciences

TL;DR: In this article, the Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements is presented. But it does not address the problem of missing data.
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A logical calculus of the ideas immanent in nervous activity

TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Journal ArticleDOI

Least Squares Support Vector Machine Classifiers

TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
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.
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