Compressive strength prediction for glass aggregates incorporated concrete, using neural network and reviews
Keywords:
Concrete, Prediction, Aggregates, Reviews, Artificial neural network
Abstract
Abstract: Production of concrete by use of conventional materials is unsustainable due to high demand. Henceforth, there is need to upscale the use of alternative materials, including those from waste streams, in concrete. This research aims at developing a suitable predictive model of concrete having partial or 100% glass aggregates. 50 datasets reviewed from 9 sources were adopted and artificial neural network (ANN) models were developed in GNU Octave. The trial models had 7 input variables and 1 output variable (compressive strength) and 1 hidden layer. The selected model, having 24 nodes in the hidden layer and 90.000 iterations, indicated overall root mean square error (RMSE), mean absolute errors (MAE), mean absolute percentage errors (MAPE) and absolute factor of variance (R2) of 2.679 MPa, 1.422 MPa, 6.951% and 0.996 respectively. The glass fine aggregates between >40% and 50% indicated just over 11% average strengths from the controls. Generally, RMSE, MAE, MAPE and R2 values showed that the selected model had a good accuracy level and good generalization, particularly considering that the datasets were not from the same experimental program. The study recommends research and utilization of glass fine aggregates up to 50% by weight, with consideration to other influencing factors and also research in cost-effective and environmentally friendly additive and assessment on waste glass aggregates incorporated concrete.
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References
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doi: https://doi.org/10.1155/2017/4563164
[26] V. Chandwani, V. Agrawal, R. Nagar, Modeling slump of ready-mix concrete using genetically evolved artificial neural network, Advances in artificial neural systems (2014) article ID 629137.
doi: http://dx.doi.org/10.1155/2014/629137
[27] M. Islam, M. Zain, M. Jamil, Prediction of strength and slump of rice husk ash incorporated high-performance concrete, Journal of civil engineering and management 18 (3) (2012) pp. 310-317. doi: https://doi.org/10.3846/13923730.2012.698890
[2] C. Ngandu, Prediction of Compressive for Rice Husk Ash Incorporated Concrete, Using Neural Network and Reviews, ITECKNE 18 (2) (2021), pre-print/accepted for publication.
doi: https://doi.org/10.15332/iteckne.v18i1.2532
[3] V. Chandwani, V. Agrawal et al., Modeling slump of ready-mix concrete using artificial neural network, International Journal of Technology 6 (2) (2015) pp. 207-216.
doi: http://dx.doi.org/10.14716/ijtech.v6i2.213
[4] D. Sathyan, K. Anand et al., Modeling the fresh and hardened stage properties of self-compacting concrete using random kitchen sink algorithm, International Journal of Concrete Structures and Materials 12 (2018) Article No: 24.
doi: https://doi.org/10.1186/s40069-018-0246-7
[5] R. Jayaseelan, G. Pandulu, G. Ashwini, Neural networks for the prediction of fresh properties and compressive strength of flowable concrete, Journal of urban and environmental engineering 13 (1) (2019) pp. 183-197.
doi: https://doi.org/10.4090/juee.2019.v13n1.183197
[6] A. Hasanzade-Inallu, P. Inallou, B. Eskandarinezhad, Prediction of compressive strength of concrete with manufactured sand using neural networks and bat algorithm, Soil Structure Interaction Journal 4 (2019) pp. 52-63.
[7] J. Eaton, D. Bateman et al., GNU Octave version 6.1.0 manual: A high-level interactive language for numerical computations (2020) [cited 2021-05].
URL: https://www.gnu.org/software/octave/doc/v6.1.0/
[8] A. Sharba, Possibility of using waste glass powder and ceramic tile as an aggregate on the flexural behavior and strength properties, in: R. Abd-Alhameed, R. Zubo, o. Ali (Eds.), 1st international multi-disciplinary conference – sustainable development and smart planning: IMDC-SDSP 2020, Cyberspace, 2020. doi: http://dx.doi.org/10.4108/eai.28-6-2020.2298155
[9] H. Du, K. Tan, Waste glass powder as cement replacement in concrete, Journal of advance concrete technology 12 (11) (2014), pp. 468-477.
URL: https://www.jstage.jst.go.jp/article/jact/12/11/12_468/_pdf/-char/en
[10] G. Kuruppu, R. Chandratilake, Use of recycle glass as a coarse aggregate in concrete, in: Y. Sandanayake (Ed.), World construction conference 2012- Global challenges in construction industry, Colombo, Sri Lanka, pp. 221-228.
URL: https://www.irbnet.de/daten/iconda/CIB_DC25129.pdf
[11] T. Drzymała, B. Zegardło, P. Tofilo, Properties of concrete containing recycled glass aggregates produced of exploded lighting materials, Materials 13 (1) (2020) 226.
doi: https://doi.org/10.3390/ma13010226
[12] G. Ke, J. Bai et al., The effect of waste glass on concrete performance under high temperatures, in: J. Xiao, Y. Zhang, M. Cheung, R Chu (Eds.), RILEM publications SARL., 2nd international conference on waste engineering and management: ICWEM 2010, Shanghai, China. 2010, pp. 324-331.
URL: https://www.rilem.net/publication/publication/77?id_papier=3560
[13] J. Halbiniak, M. Major, The use of waste glass for cement production, IOP conference series: materials science and engineering, 5th annual international workshop on materials science: IWMSE 2019, Hunan, Changsha, China, 012008.
doi: https://doi.org/10.1088/1757-899X/585/1/012008
URL: https://iopscience.iop.org/article/10.1088/1757-899X/585/1/012008/pdf
[14] H. Dabiri, M. Sharbatalar et al., The influence of replacing sand with waste glass particle on the physical and mechanical parameters of concrete, Civil Engineering Journal 4 (7) (2018) pp. 1646-1652. doi: http://dx.doi.org/10.28991/cej-03091101
[15] R. AL-Bawi, I. Kadhim, O. AL-Kerttani, Strengths and failure characteristics of self-compacting concrete containing recycled waste glass aggregate, Advances in materials science and engineering (2017) Article ID 6829510. doi: https://doi.org/10.1155/2017/6829510
[16] L. Pereira de Oliveira, J. Castro-Gomes, P. Santos, Mechanical and durability properties of concrete with grounded waste glass sand, in: A. Tϋerkeri, Ö. Sengϋl (Eds.), 11th international conference on durability of building materials and components: 11DBMC, Istanbul, Turkey, 2008.
URL: https://www.irb.fraunhofer.de/CIBlibrary/search-quick-result-list.jsp?ALLE=&ResultCount=10&start=1&Title=waste+glass&Contributors=&Source=&ConfData=&CIBWCTG=&CIBSpS=ALL&CIBPT=ALL&CIBPPT=ALL&PubType=conference+paper&PubDateFrom=&PubDateUntil=&PubCodes=&uebergeord=&SortSpec=jahr+desc+sort1+desc+sort2+asc&idSuche=CIB+DC25536&detailed=true&Searchtype=advanced
[17] H. Ammash, M. Muhammed, A. Nahhab, Using of waste glass as fine aggregate concrete, Al-Qadisiya Journal of engineering sciences 2 (2) (2009) pp. 206-214, in Arabic and english.
URL: https://qu.edu.iq/journaleng/index.php/JQES/article/view/49/356
[18] M. Sani, A. Osman, F. Muftah, Investigation on compressive strength of special concrete made with crushed waste glass, in: C. Guojian, Y. Muhammad (Eds.), MATEC Web of conferences, 2015 4th international conference of engineering and innovation materials: ICEIM, 2015, Penang, Malaysia, 2015, 02005. doi: https://doi.org/10.1051/matecconf/20152702005
[19] P. Rajagopalan, V. Balaji et al., Study of bond characteristics of reinforced waste glass aggregate concrete, IOP conferences series: Earth and environmental science, International conference on civil engineering and infrastructural issues and emerging economies: ICCIEE 2017, Tirumalaisamudram, Thanjavur, India, 012006.
doi: https://doi.org/10.1088/1755-1315/80/1/012006
[20] A. Otunyo, B. Okechukwu, Performance of concrete with partial replacement of fine aggregates with crushed waste glass, Nigerian journal of technology (NIJOTECH) 36 (2) (2017) pp. 403-410.
doi: http://dx.doi.org/10.4314/njt.v36i2.12
[21] A. Henigal, E. Elbeltgai et al., Artificial neural network model for forecasting concrete compressive strength and slump in Egypt, Journal of Al Azhar university engineering sector 11 (39) (2016) pp. 435-446.
doi: https://doi.org/10.21608/AUEJ.2016.19445
[22] S. Oman, A simple neural network in octave-part 2. January 3, 2016 (2016) [cited 2021-05-19]. URL https://aimatters.wordpress.com/2016/01/03/a-simple-neural-network-in-octave-part-2/
[23] S. Tiwari, A. Rai, Activation functions in neural networks, October 8th, 2020 (2020) [cited 2021-01-18]. URL https://www.geeksforgeeks.org/activation-functions-neural-networks/
[24] A. Sharma, The theory of everything: Understanding activation functions in neural networks, (March 30, 2017) [cited 2021].
URL https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0
[25] J. Zhang, Y. Zhao, H. Li, Experimental investigation and prediction of compressive strength of ultra high performance concrete containing supplementary cementitious materials, Advances in material science and engineering (2017) article ID 4563164.
doi: https://doi.org/10.1155/2017/4563164
[26] V. Chandwani, V. Agrawal, R. Nagar, Modeling slump of ready-mix concrete using genetically evolved artificial neural network, Advances in artificial neural systems (2014) article ID 629137.
doi: http://dx.doi.org/10.1155/2014/629137
[27] M. Islam, M. Zain, M. Jamil, Prediction of strength and slump of rice husk ash incorporated high-performance concrete, Journal of civil engineering and management 18 (3) (2012) pp. 310-317. doi: https://doi.org/10.3846/13923730.2012.698890
Published
2022-06-13
How to Cite
Ngandu, C. (2022). Compressive strength prediction for glass aggregates incorporated concrete, using neural network and reviews. ITECKNE, 19(2), 97-103. https://doi.org/https://doi.org/10.15332/iteckne.v19i2.2769
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Section
Research and Innovation Articles
