| 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
Assessment of acid resistance of natural pozzolan-based alkali-activated concrete
Abstract
<p>Although the synthesis and properties of natural pozzolan (NP)-based alkali-activated binder (AAB) have been investigated, to the best of our knowledge, no study has focused on and assessed the performance of such concrete when exposed to acid attack. In addition, there is a lack of information regarding the optimisation of reaction parameters. Therefore, in the present study, NPs blended with nano-silica (nSiO<sub>2</sub>) from 0 to 7.5% were taken into account to develop alkali-activated concrete (ACC), cured at room temperature, and subsequently exposed to 5% sulfuric acid (H<sub>2</sub>SO<sub>4aq</sub>). The performance of the NP/nSiO<sub>2</sub>-based ACC was evaluated by visual examination, microstructure, weight loss, and compressive strength loss up to one year of exposure to an acidic environment. In addition, artificial neural network (ANN) and response surface methodology (RSM) models were developed to predict and optimize nSiO<sub>2</sub> to ascertain the minimum weight and strength loss. Based on both the predicted and actual results, a significant improvement in the microstructure was achieved with an increase in nSiO<sub>2</sub>. The micro-analytical examination revealed the leaching of vital elements from the binder structure, such as Al, Ca, and Na, which enabled the creation of highly expansive substances such as gypsum, which caused cracking and eventually disintegration in the OPC and NP-based AAB incorporating lower quantities of nSiO<sub>2</sub>. Both the loss in weight and strength were in the range of 23%–39% in the 1% to 7.5% nSiO<sub>2</sub> modified AAC. In contrast, in the control AAC and OPC-based concrete, a weight loss of more than 50% was recorded, along with a substantial reduction in strength.</p>