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.
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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
Machine learning prediction of mechanical properties of concrete: Critical review
TL;DR: Examination of several Machine Learning models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms are examined.
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Effect of Fly Ash and Silica Fume on Compressive and Fracture Behaviors of Concrete
TL;DR: In this paper, the effects of replacing cement by fly ash and silica fume on strength, compressive stress-strain relationship, and fracture behavior of concrete were investigated, and it was found that fly ash substantially improved the post-peak compressive behavior, with a relatively smaller gradient in the descending part of the stressstrain curve.
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Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN
TL;DR: In this article, an artificial neural network (ANN) was used to predict the 28-day compressive strength of self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash.
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Predicting the shear strength of reinforced concrete beams using artificial neural networks
TL;DR: In this paper, the authors used ANNs to predict the ultimate shear strength of reinforced concrete (RC) beams with transverse reinforcements, and the results showed that ANNs have strong potential as a feasible tool for predicting the ultimate strength of RC beams with reinforced reinforcement within the range of input parameters considered.
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Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete
TL;DR: The results showed that ANN and FL can be alternative approaches for the predicting of compressive strength of silica fume concrete.