| 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
Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments
Abstract
<p>Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet–dry cycles (WDCs) on the resilient modulus (M<sub>r</sub>) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here. For this purpose, a number of wet–dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ<sub>3</sub>), and deviator stress (σ<sub>4</sub>) were considered input variables, and M<sub>r</sub> was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that M<sub>r</sub> increased with the DMR, σ<sub>3</sub>, and σ<sub>4</sub>. An increase in the number of WDCs reduced the M<sub>r</sub> value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ<sub>4</sub> > σ<sub>3</sub>, (ANN model) and DMR > WDC > CSAFR > σ<sub>4</sub> > σ<sub>3</sub> (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the M<sub>r</sub> value.</p>