Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique
The use of enormous amounts of material is required for production. Due to the current emphasis on the environment and sustainability of materials, waste products and by-products, including silica fume and fly ash (FA), are incorporated into concrete as a substitute partially for cement. Additionall...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Axioms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1680/12/1/81 |
| _version_ | 1797446134165143552 |
|---|---|
| author | Musa Adamu Andaç Batur Çolak Yasser E. Ibrahim Sadi I. Haruna Mukhtar Fatihu Hamza |
| author_facet | Musa Adamu Andaç Batur Çolak Yasser E. Ibrahim Sadi I. Haruna Mukhtar Fatihu Hamza |
| author_sort | Musa Adamu |
| collection | DOAJ |
| description | The use of enormous amounts of material is required for production. Due to the current emphasis on the environment and sustainability of materials, waste products and by-products, including silica fume and fly ash (FA), are incorporated into concrete as a substitute partially for cement. Additionally, concrete fine aggregate has indeed been largely replaced by waste materials like crumb rubber (CR), thus it reduces the mechanical properties but improved some other properties of the concrete. To decrease the detrimental effects of the CR, concrete is therefore enhanced with nanomaterials such nano silica (NS). The concrete mechanical properties are essential for the designing and constRuction of concrete structures. Concrete with several variables can have its mechanical characteristics predicted by an artificial neural network (ANN) technique. Using ANN approaches, this paper predict the mechanical characteristics of concrete constructed with FA as a partial substitute for cement, CR as a partial replacement for fine aggregate, and NS as an addition. Using an artificial neural network (ANN) technique, the mechanical characteristics investigated comprise splitting tensile strength (Fs), compressive strength (Fc), modulus of elasticity (Ec) and flexural strength (Ff). The ANN model was used to train and test the dataset obtained from the experimental program. Fc, Fs, F<sub>f</sub> and Ec were predicted from added admixtures such as CR, NS, FA and curing age (P). The modelling result indicated that ANN predicted the strength with high accuracy. The proportional deviation mean (MoD) values calculated for F<sub>c</sub>, F<sub>s</sub>, F<sub>f</sub> and E<sub>c</sub> values were −0.28%, 0.14%, 0.87% and 1.17%, respectively, which are closed to zero line. The resulting ANN model’s mean square error (MSE) values and coefficient of determination (R<sup>2</sup>) are 6.45 × 10<sup>−2</sup> and 0.99496, respectively. |
| first_indexed | 2024-03-09T13:35:50Z |
| format | Article |
| id | doaj.art-30f780619a064aec99ac01500307d369 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-09T13:35:50Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-30f780619a064aec99ac01500307d3692023-11-30T21:12:03ZengMDPI AGAxioms2075-16802023-01-011218110.3390/axioms12010081Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network TechniqueMusa Adamu0Andaç Batur Çolak1Yasser E. Ibrahim2Sadi I. Haruna3Mukhtar Fatihu Hamza4Engineering Management Department, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi ArabiaInformation Technologies Application and Research Center, Istanbul Commerce University, Istanbul 34445, TurkeyEngineering Management Department, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi ArabiaDepartment of Civil Engineering, Bayero University Kano, Kano 700223, NigeriaDepartment of Mechanical Engineering, College of Engineering in Alkharj, Prince Sattam bin Abdulaziz University, Alkharj 11942, Saudi ArabiaThe use of enormous amounts of material is required for production. Due to the current emphasis on the environment and sustainability of materials, waste products and by-products, including silica fume and fly ash (FA), are incorporated into concrete as a substitute partially for cement. Additionally, concrete fine aggregate has indeed been largely replaced by waste materials like crumb rubber (CR), thus it reduces the mechanical properties but improved some other properties of the concrete. To decrease the detrimental effects of the CR, concrete is therefore enhanced with nanomaterials such nano silica (NS). The concrete mechanical properties are essential for the designing and constRuction of concrete structures. Concrete with several variables can have its mechanical characteristics predicted by an artificial neural network (ANN) technique. Using ANN approaches, this paper predict the mechanical characteristics of concrete constructed with FA as a partial substitute for cement, CR as a partial replacement for fine aggregate, and NS as an addition. Using an artificial neural network (ANN) technique, the mechanical characteristics investigated comprise splitting tensile strength (Fs), compressive strength (Fc), modulus of elasticity (Ec) and flexural strength (Ff). The ANN model was used to train and test the dataset obtained from the experimental program. Fc, Fs, F<sub>f</sub> and Ec were predicted from added admixtures such as CR, NS, FA and curing age (P). The modelling result indicated that ANN predicted the strength with high accuracy. The proportional deviation mean (MoD) values calculated for F<sub>c</sub>, F<sub>s</sub>, F<sub>f</sub> and E<sub>c</sub> values were −0.28%, 0.14%, 0.87% and 1.17%, respectively, which are closed to zero line. The resulting ANN model’s mean square error (MSE) values and coefficient of determination (R<sup>2</sup>) are 6.45 × 10<sup>−2</sup> and 0.99496, respectively.https://www.mdpi.com/2075-1680/12/1/81crumb rubberfly ashnano silicamechanical characteristicsartificial neural network |
| spellingShingle | Musa Adamu Andaç Batur Çolak Yasser E. Ibrahim Sadi I. Haruna Mukhtar Fatihu Hamza Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique Axioms crumb rubber fly ash nano silica mechanical characteristics artificial neural network |
| title | Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique |
| title_full | Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique |
| title_fullStr | Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique |
| title_full_unstemmed | Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique |
| title_short | Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique |
| title_sort | prediction of mechanical properties of rubberized concrete incorporating fly ash and nano silica by artificial neural network technique |
| topic | crumb rubber fly ash nano silica mechanical characteristics artificial neural network |
| url | https://www.mdpi.com/2075-1680/12/1/81 |
| work_keys_str_mv | AT musaadamu predictionofmechanicalpropertiesofrubberizedconcreteincorporatingflyashandnanosilicabyartificialneuralnetworktechnique AT andacbaturcolak predictionofmechanicalpropertiesofrubberizedconcreteincorporatingflyashandnanosilicabyartificialneuralnetworktechnique AT yassereibrahim predictionofmechanicalpropertiesofrubberizedconcreteincorporatingflyashandnanosilicabyartificialneuralnetworktechnique AT sadiiharuna predictionofmechanicalpropertiesofrubberizedconcreteincorporatingflyashandnanosilicabyartificialneuralnetworktechnique AT mukhtarfatihuhamza predictionofmechanicalpropertiesofrubberizedconcreteincorporatingflyashandnanosilicabyartificialneuralnetworktechnique |