Prediction of mechanical properties of concrete blended with marble stone powder by artificial neural network
The current research work is mainly concentrated on the mechanical properties concrete blended with marble stone power resulted from waste sludge marble processing it has a high specific area. M25 grade concrete mix design was considered for this research work. The mechanical properties of concrete...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01003.pdf |
| _version_ | 1797248877838991360 |
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| author | Ramesh Babu T.S. Thangamani K. Kishore Mendu Jugal Guru Jawahar J. Pavan Kumar D. Garigipati Satish Maksudovna Khristina |
| author_facet | Ramesh Babu T.S. Thangamani K. Kishore Mendu Jugal Guru Jawahar J. Pavan Kumar D. Garigipati Satish Maksudovna Khristina |
| author_sort | Ramesh Babu T.S. |
| collection | DOAJ |
| description | The current research work is mainly concentrated on the mechanical properties concrete blended with marble stone power resulted from waste sludge marble processing it has a high specific area. M25 grade concrete mix design was considered for this research work. The mechanical properties of concrete i.e. compressive strength, unit weight, splitting tensile strength, modulus of elasticity and flexural strength were considered for the study. The compressive strength of these mixes was measured on 150mm ×150mm × 150mm cubes and tension test split tensile test 150 mm dia × 300 mm height cylinders. The concrete unit weight was considered for calculating the elastic modulus of concrete. The investigational values were matched with ACI, CEB-FIP, BIS and AASHTO LRFD empirical equation and regression analysis was done. The empirical equation result was compared with regression analysis of Artificial Neural Network, and conclusion was brough down that regression analysis of artificial neural network had better prediction than that of above-mentioned empirical equations. The study concluded that 15% replacement of marble power attained highest strength and optimum replacement, 25% replacement was concluded as economical replacement to attain designed strength. |
| first_indexed | 2024-04-24T20:21:34Z |
| format | Article |
| id | doaj.art-4dda4d117e7f4c049f523233ce9845a4 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:21:34Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-4dda4d117e7f4c049f523233ce9845a42024-03-22T08:05:25ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920100310.1051/matecconf/202439201003matecconf_icmed2024_01003Prediction of mechanical properties of concrete blended with marble stone powder by artificial neural networkRamesh Babu T.S.0Thangamani K.1Kishore Mendu Jugal2Guru Jawahar J.3Pavan Kumar D.4Garigipati Satish5Maksudovna Khristina6Department of Civil Engineering, K. G. Reddy College of Engineering and TechnologyDepartment of Civil Engineering, K. G. Reddy College of Engineering and TechnologyDepartment of Civil Engineering, Kallam Haranadha Reddy Institute of TechnologyDepartment of Civil Engineering, Annamacharya Institute of Technology and SciencesDepartment of Civil Engineering, Jawahar Lal Nehru Technological University College of Engineering AnantapurDepartment of CSE, GRIETLovely Professional UniversityThe current research work is mainly concentrated on the mechanical properties concrete blended with marble stone power resulted from waste sludge marble processing it has a high specific area. M25 grade concrete mix design was considered for this research work. The mechanical properties of concrete i.e. compressive strength, unit weight, splitting tensile strength, modulus of elasticity and flexural strength were considered for the study. The compressive strength of these mixes was measured on 150mm ×150mm × 150mm cubes and tension test split tensile test 150 mm dia × 300 mm height cylinders. The concrete unit weight was considered for calculating the elastic modulus of concrete. The investigational values were matched with ACI, CEB-FIP, BIS and AASHTO LRFD empirical equation and regression analysis was done. The empirical equation result was compared with regression analysis of Artificial Neural Network, and conclusion was brough down that regression analysis of artificial neural network had better prediction than that of above-mentioned empirical equations. The study concluded that 15% replacement of marble power attained highest strength and optimum replacement, 25% replacement was concluded as economical replacement to attain designed strength.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01003.pdf |
| spellingShingle | Ramesh Babu T.S. Thangamani K. Kishore Mendu Jugal Guru Jawahar J. Pavan Kumar D. Garigipati Satish Maksudovna Khristina Prediction of mechanical properties of concrete blended with marble stone powder by artificial neural network MATEC Web of Conferences |
| title | Prediction of mechanical properties of concrete blended with marble stone powder by artificial neural network |
| title_full | Prediction of mechanical properties of concrete blended with marble stone powder by artificial neural network |
| title_fullStr | Prediction of mechanical properties of concrete blended with marble stone powder by artificial neural network |
| title_full_unstemmed | Prediction of mechanical properties of concrete blended with marble stone powder by artificial neural network |
| title_short | Prediction of mechanical properties of concrete blended with marble stone powder by artificial neural network |
| title_sort | prediction of mechanical properties of concrete blended with marble stone powder by artificial neural network |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01003.pdf |
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