| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Salami, Babatunde Abiodun
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (25/25 displayed)
- 2024Evaluating long-term durability of nanosilica-enhanced alkali-activated concrete in sulfate environments towards sustainable concrete developmentcitations
- 2023Graphene-based concretecitations
- 2023Microencapsulated phase change materials for enhanced thermal energy storage performance in construction materialscitations
- 2023Using explainable machine learning to predict compressive strength of blended concretecitations
- 2023Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concretecitations
- 2023An overview of factors influencing the properties of concrete incorporating construction and demolition wastescitations
- 2023High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithmcitations
- 2023Evaluating mechanical, microstructural and durability performance of seawater sea sand concrete modified with silica fumecitations
- 2022Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Modelscitations
- 2022Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Modelcitations
- 2022Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Modelcitations
- 2022Acid Resistance of Alkali-Activated Natural Pozzolan and Limestone Powder Mortarcitations
- 2022Engineered and green natural pozzolan-nano silica-based alkali-activated concretecitations
- 2022Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environmentscitations
- 2022Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysiscitations
- 2021Predicting the compressive strength of a quaternary blend concrete using Bayesian regularized neural networkcitations
- 2021Strength and acid resistance of ceramic-based self-compacting alkali-activated concretecitations
- 2021Effect of alkaline activator ratio on the compressive strength response of POFA-EACC mortar subjected to elevated temperaturecitations
- 2021Assessment of acid resistance of natural pozzolan-based alkali-activated concretecitations
- 2020Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concretecitations
- 2019Influence of composition and concentration of alkaline activator on the properties of natural-pozzolan based green concretecitations
- 2017POFA-engineered alkali-activated cementitious composite performance in acid environmentcitations
- 2016Impact of added water and superplasticizer on early compressive strength of selected mixtures of palm oil fuel ash-based engineered geopolymer compositescitations
- 2016Durability performance of Palm Oil Fuel Ash-based Engineered Alkaline-activated Cementitious Composite (POFA-EACC) mortar in sulfate environmentcitations
- 2014Mechanical properties and durability characteristics of SCC incorporating crushed limestone powdercitations
Places of action
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article
Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete
Abstract
<p>The construction sector would greatly benefit from a strategy for optimizing high-performance concrete mixtures. However, traditional proportioning techniques are insufficient because of their high prices, usage restrictions, and inability to account for nonlinear interactions between components and concrete qualities. High-performance concrete (HPC) is a complicated composite material with highly nonlinear mechanical behaviour. When strength can be accurately predicted, design costs, design time, and material waste caused by several mixing trials can all be reduced. In this research, feed-forward neural network (FFNN), Elman neural network (ENN), support vector machine (SVM) and multilinear regression (MLR) were employed for predicting the compressive strength of HPC. The input variables include cement (C), cement strength (CeS), superplasticizer (S), fly ash (F), air entraining agent (A), coarse aggregate (CA), Sand (Sd) and water/binder (W/B) and 28 days’ compressive strength as the output variables. Finally, the results indicate that the proposed model has predictive robustness for predicting the compressive strength of HPC. The results showed that FFNN-M4, ENN-M4, SVM-M4, and MLR-M4 combination have the highest performance evaluation criteria of R<sup>2</sup>=0.9950, R<sup>2</sup>=0.9853, R<sup>2</sup>=0.9736, R<sup>2</sup>= 0.9678 in the testing phase respectively. The outcomes also show that the proposed model has high accuracy and effectiveness in predicting the compressive strength of HPC.</p>