| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
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
| Organizations | Location | People |
|---|
article
Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model
Abstract
<p>The depletion of natural resources of river sand and its availability issues as a construction material compelled the researchers to use manufactured sand. This study investigates the compressive strength of concrete made of manufactured sand as a partial replacement of normal sand. The prediction model, i.e., gene expression programming (GEP), was used to estimate the compressive strength of manufactured sand concrete (MSC). A database comprising 275 experimental results based on 11 input variables and 1 target variable was used to train and validate the developed models. For this purpose, the compressive strength of cement, tensile strength of cement, curing age, D<sub>max</sub> of crushed stone, stone powder content, fineness modulus of the sand, water-to-binder ratio, water-to-cement ratio, water content, sand ratio, and slump were taken as input variables. The investigation of a varying number of genetic characteristics, such as chromosomal number, head size, and gene number, resulted in the creation of 11 alternative models (M1-M11). The M5 model outperformed other created models for the training and testing stages, with values of (4.538, 3.216, 0.919) and (4.953, 3.348, 0.906), respectively, according to the results of the accuracy evaluation parameters root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>). The R<sup>2</sup> and error indices values revealed that the experimental and projected findings are in extremely close agreement. The best model has 200 chromosomes, 8 head sizes, and 3 genes. The mathematical expression achieved from the GEP model revealed that six parameters, namely the compressive and tensile strength of cement, curing period, water–binder ratio, water–cement ratio, and stone powder content contributed effectively among the 11 input variables. The sensitivity analysis showed that water–cement ratio (46.22%), curing period (25.43%), and stone powder content (13.55%) were revealed as the most influential variables, in descending order. The sensitivity of the remaining variables was recorded as w/b (11.37%) > f<sub>ce</sub> (2.35%) > f<sub>ct</sub> (1.35%).</p>