Artificial neural networks for mechanical strength prediction of lightweight mortar
In this paper, the practical results of mechanical strength of different lightweight mortars made with 0, 5,10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 of scoria instead of sand and 0.55 water-cement ratio and 350 kg/m3 cement content have been used to generate arti...
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| Format: | Article |
| Language: | English |
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Scientific Research and Essays
2011
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| Online Access: | http://eprints.um.edu.my/5942/1/Artificial_neural_networks_for_mechanical_strength_prediction_of_lightweight_mortar.pdf |
| _version_ | 1796945369945341952 |
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| author | Razavi, S.V. Jumaat, Mohd Zamin Ei-Shafie, A.H. Mohammadi, P. |
| author_facet | Razavi, S.V. Jumaat, Mohd Zamin Ei-Shafie, A.H. Mohammadi, P. |
| author_sort | Razavi, S.V. |
| collection | UM |
| description | In this paper, the practical results of mechanical strength of different lightweight mortars made with 0, 5,10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 of scoria instead of sand and 0.55 water-cement ratio and 350 kg/m3 cement content have been used to generate artificial neural networks (ANNs). Totally, 52 feed-forward back-propagation neural networks (FFBNN) with different parameters have been investigated in the case of 80 data for training, 15 data for verifying, and 10 data for testing. The performance for producing networks was evaluated by root mean squared error (RMSE) and the correlation coefficient between data. The two selected networks, N1 (Net Architecture 2-10-2) and N2 (Net Architecture 2-10-5-2) had (0.020, 0.027) and (0.017, 0.018) as (Training, Testing) RMSE set and 0.997 and 0.982 as testing correlation coefficient. |
| first_indexed | 2024-03-06T05:15:37Z |
| format | Article |
| id | um.eprints-5942 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:15:37Z |
| publishDate | 2011 |
| publisher | Scientific Research and Essays |
| record_format | dspace |
| spelling | um.eprints-59422020-02-05T04:37:34Z http://eprints.um.edu.my/5942/ Artificial neural networks for mechanical strength prediction of lightweight mortar Razavi, S.V. Jumaat, Mohd Zamin Ei-Shafie, A.H. Mohammadi, P. TA Engineering (General). Civil engineering (General) In this paper, the practical results of mechanical strength of different lightweight mortars made with 0, 5,10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 of scoria instead of sand and 0.55 water-cement ratio and 350 kg/m3 cement content have been used to generate artificial neural networks (ANNs). Totally, 52 feed-forward back-propagation neural networks (FFBNN) with different parameters have been investigated in the case of 80 data for training, 15 data for verifying, and 10 data for testing. The performance for producing networks was evaluated by root mean squared error (RMSE) and the correlation coefficient between data. The two selected networks, N1 (Net Architecture 2-10-2) and N2 (Net Architecture 2-10-5-2) had (0.020, 0.027) and (0.017, 0.018) as (Training, Testing) RMSE set and 0.997 and 0.982 as testing correlation coefficient. Scientific Research and Essays 2011 Article PeerReviewed application/pdf en http://eprints.um.edu.my/5942/1/Artificial_neural_networks_for_mechanical_strength_prediction_of_lightweight_mortar.pdf Razavi, S.V. and Jumaat, Mohd Zamin and Ei-Shafie, A.H. and Mohammadi, P. (2011) Artificial neural networks for mechanical strength prediction of lightweight mortar. Scientific Research and Essays, 6 (16). pp. 3406-3417. ISSN 19922248, DOI https://doi.org/10.5897/SRE11.311 <https://doi.org/10.5897/SRE11.311>. http://www.scopus.com/inward/record.url?eid=2-s2.0-80052053386&partnerID=40&md5=70c6f5972e8f5a4f2ead4ddd175da2f1 10.5897/SRE11.311 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Razavi, S.V. Jumaat, Mohd Zamin Ei-Shafie, A.H. Mohammadi, P. Artificial neural networks for mechanical strength prediction of lightweight mortar |
| title | Artificial neural networks for mechanical strength prediction of lightweight mortar |
| title_full | Artificial neural networks for mechanical strength prediction of lightweight mortar |
| title_fullStr | Artificial neural networks for mechanical strength prediction of lightweight mortar |
| title_full_unstemmed | Artificial neural networks for mechanical strength prediction of lightweight mortar |
| title_short | Artificial neural networks for mechanical strength prediction of lightweight mortar |
| title_sort | artificial neural networks for mechanical strength prediction of lightweight mortar |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.um.edu.my/5942/1/Artificial_neural_networks_for_mechanical_strength_prediction_of_lightweight_mortar.pdf |
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