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
Hybrid intelligence models for compressive strength prediction of MPC composites and parametric analysis with SHAP algorithm
TL;DR: In this article , the compressive strength of magnesium phosphate cement (MPC) composites using the deep learning and machine learning based hybrid models, which is rarely seen in the literature, was predicted.
Journal ArticleDOI
Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming
Muhammad Nasir Amin,Muhammad Raheel,Mudassir Iqbal,Kaffayatullah Khan,Muhammad Ghulam Qadir,Fazal E. Jalal,Anas Abdulalim Alabdullah,Ali Ajwad,Majdi Adel Al-Faiad,A. M. Abu-Arab +9 more
TL;DR: In this paper , the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach was investigated.
Journal ArticleDOI
Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete
TL;DR: In this article , the authors proposed a pioneering study on the utilization of ML for predicting post-fire self-healing of concrete, where four ML methods were optimized and compared based on their performance error: Support Vector Machines (SVM), Regression Trees (RT), Artificial Neural Networks (ANN), and Ensemble of Regression Tree (ET).
Journal ArticleDOI
PCA-Based Hybrid Intelligence Models for Estimating the Ultimate Bearing Capacity of Axially Loaded Concrete-Filled Steel Tubes
Kaffayatullah Khan,Rahul Das Biswas,Jitendra Gudainiyan,Muhammad Nasir Amin,Hisham Jahangir Qureshi,Abdullah Mohammad Abu Arab,Mudassir Iqbal +6 more
TL;DR: The generated ANN-GWO (hybrid model of ANN and grey wolf optimizer) produced the most accurate predictions in the training and testing phases, respectively, and may be utilised as a substitute tool to estimate the load-carrying capacity of CFST columns in civil engineering projects according to the experimental findings.
Journal ArticleDOI
A novel integrated approach of RUNge Kutta optimizer and ANN for estimating compressive strength of self-compacting concrete
Rahul Das Biswas,Manish Kumar,Raushan Kumar Singh,Majed Alzara,S.B.A. El Sayed,Mohamed Abdelmongy,Ahmed Yosri,S. Yousef +7 more
TL;DR: In this article , a hybrid artificial neural network (ANN) along with metaheuristic optimization techniques and two traditional models were employed which comprised of 300 datasets to predict the compressive strength of self-compacting concrete (SCC) depending on percentage replacement of cement by supplementary cementitious material.
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.