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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Machine learning for structural engineering: A state-of-the-art review
TL;DR: An overview of ML techniques for structural engineering is presented in this article with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets.
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Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete
TL;DR: Wang et al. as mentioned in this paper used three ensemble machine learning (EML) models: Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost) and Light Gradient boosting machine (LGBM) to predict concrete creep behavior.
Journal ArticleDOI
Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete
TL;DR: Wang et al. as mentioned in this paper used three ensemble machine learning (EML) models: Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost) and Light Gradient boosting machine (LGBM) to predict concrete creep behavior.
Journal ArticleDOI
ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions
TL;DR: It can be concluded that the newly constructed ELM-based ANSI models can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.
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Bonding behavior of concrete matrix and alkali-activated mortar incorporating nano-SiO2 and polyvinyl alcohol fiber: Theoretical analysis and prediction model
TL;DR: In this paper, the effects of polyvinyl alcohol (PVA) fiber and nano-SiO2 (NS) contents on the bonding behavior of two-interfaced shear samples were explored.
References
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Journal ArticleDOI
Multivariate Adaptive Regression Spline (Mars) for Prediction of Elastic Modulus of Jointed Rock Mass
TL;DR: In this article, a multivariate adaptive regression spline (MARS) for determination of elastic modulus (Ej) of jointed rock mass is presented. But the results from the developed MARS model have been compared with those of artificial neural networks (ANNs) using average absolute error.
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Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression
TL;DR: A novel model for predicting high-performance concrete (HPC) compressive strength is established, which hybridizes the firefly algorithm and the least squares support vector regression (LS-SVR), which is a promising alternative to predict HPC strength.
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A Gene Expression Programming Model for Predicting Tunnel Convergence
TL;DR: In this paper, a gene expression programming (GEP) algorithm was used to predict tunnel convergence in accordance with the New Austrian Tunneling Method (NATM) in underground spaces.
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Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model
TL;DR: Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network and this method is strongly recommended for estimating HPC strength.
Journal ArticleDOI
Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness
Danial Jahed Armaghani,Panagiotis G. Asteris,Behnam Askarian,Mahdi Hasanipanah,Reza Tarinejad,Van Van Huynh +5 more
TL;DR: The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models.