Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming.
By Muhammad Farjad Iqbal, Qing-feng Liu, Iftikhar Azim, Xingyi Zhu, Jian Yang, Muhammad Faisal Javed, Momina Rauf. Journal of Hazardous Materials, 2020
364 citations recorded by the source corpus.
Waste foundry sand (WFS) is a major pollutant generated from metal casting foundries and is classified as a hazardous material due to the presence of organic and inorganic pollutants which can cause adverse environmental impact. In order to promote the re-utilization of WFS, gene expression programming (GEP) has been employed in this study to develop empirical models for prediction of mechanical properties of concrete made with WFS (CMWFS). An extensive and reliable database of mechanical properties of CMWFS is established through a comprehensive literature review. The database comprises of 234 compressive strength, 163 split tensile strength and 85 elastic modulus results. The four most influential parameters i.e. water-to-cement ratio, WFS percentage, WFS-to-cement content ratio and fineness modulus of WFS are considered as the input parameters for modelling. The mechanical properties can be estimated by the application of proposed simplified mathematical expressions. The performance of the models is assessed by conducting parametric analysis, applying statistical checks and comparing with regression models. The results reflected that the proposed models are accurate and possess a high generalization and prediction capability. The findings of this study can enhance the re-usage of WFS for development of green concrete leading to environmental protection and monetary benefits.
Subfields: Materials Chemistry, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/4307be25210c8656fc67da0dc08b655feaf19fb8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
In Mechanical Engineering
- Rayed Alyousef
- Hisham Alabduljabbar
- Shazim Ali memon
- Alireza Ashrafian
- Álvaro de Gracia
- L. F. Cabeza
Citations in the corpus
- On the Recent Trends in Expansive Soil Stabilization Using Calcium-Based Stabilizer Materials (CSMs): A Comprehensive Review (2020, background): “somehow restrained since global warming and rapidly changing climate are challenging global issues of today’s world [22, 123].”
- Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, methodology): “In comparison with nonlinear and linear regression models, the GEP model gives outburst performance and provides an empirical expression, which is suitable for the preliminary design of FA-dependent GPC. (3) +e sensitivity analysis reveals that curing temperature is the most sensitive and dominant p”
- Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete (2020, background): “The data points with up to 20% deviation from the global trend were not considered in the development and model performance evaluation [34].”
- Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement (2020, methodology): “focused on comprehensive data to present simple and robust model to formulate mechanical characteristics of green concrete using GEP approach [21].”