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
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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.
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
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
Prediction of the Compressive Strength of High Performance Concrete Mix using Tree Based Modeling
TL;DR: The result from this study suggests that tree based models perform remarkably well in predicting the compressive strength of the concrete mix, using classification algorithms like Multilayer Perceptron, M5P Tree models and Linear Regression.
Journal ArticleDOI
Mapping and holistic design of natural hydraulic lime mortars
Maria Apostolopoulou,Panagiotis G. Asteris,Danial Jahed Armaghani,Maria G. Douvika,Paulo B. Lourenço,Liborio Cavaleri,Asterios Bakolas,Antonia Moropoulou +7 more
TL;DR: In this paper, artificial neural networks (ANNs) are used to simulate and map the development of natural hydraulic lime (NHL5) mortars' characteristics, such as compressive strength, ratio of compressive to flexural strength (CS/FL) and consistency (CO), for selected mortar mix parameters.
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Neuro-fuzzy technique to predict air-overpressure induced by blasting
Danial Jahed Armaghani,Mohsen Hajihassani,Houman Sohaei,Edy Tonnizam Mohamad,Aminaton Marto,Hossein Motaghedi,Mohammad Reza R. Moghaddam +6 more
TL;DR: An adaptive neuro-fuzzy inference system (ANFIS) model is presented for prediction of blast-induced AOp in quarry blasting sites and results demonstrated the superiority of the ANFIS model to predict AOp compared to other methods.
Journal ArticleDOI
Soft computing techniques in structural and earthquake engineering: a literature review
TL;DR: A state-of-the-art review of the main applications of soft computing techniques to relevant structural and earthquake engineering problems is proposed, including the applications of fuzzy computing, evolutionary computing, swarm intelligence, and neural networks.
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Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming
Iris González-Taboada,Belén González-Fonteboa,Fernando Martínez-Abella,Juan Luis Pérez-Ordóñez +3 more
TL;DR: In this paper, the most important properties of structural recycled concrete (compressive strength, modulus of elasticity and splitting tensile strength) were predicted using multivariable regression and genetic programming.