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Akinlolu Temisola, M, Oladimeji Benedict, O and Haupt, T C (2021) A scientometric review and meta-analysis of the health and safety of women in construction: structure and research trends. Journal of Engineering, Design and Technology, 19(2), 446-66.
Aluko, O R, Godwin Iroroakpo, I and Mewomo, M C (2021) Relationship between perceived service quality and client satisfaction indicators of engineering consultancy services in building projects. Journal of Engineering, Design and Technology, 19(2), 557-77.
Barour, S and Zergua, A (2021) Numerical analysis of reinforced concrete beams strengthened in shear using carbon fiber reinforced polymer materials. Journal of Engineering, Design and Technology, 19(2), 339-57.
De-Graft, O-M, Kukah, A S, Boateng, F, Asumadu, G and Edwards, D J (2021) Exploring strategies to reduce moral hazard and adverse selection of Ghanaian public–private partnership (PPP) construction projects. Journal of Engineering, Design and Technology, 19(2), 358-72.
Evans, M, Farrell, P, Mashali, A and Zewein, W (2021) Critical success factors for adopting building information modelling (BIM) and lean construction practices on construction mega-projects: a Delphi survey. Journal of Engineering, Design and Technology, 19(2), 537-56.
Hasheminasab, S and Kashi, E (2021) Finite element analysis of ports pavement under container loading. Journal of Engineering, Design and Technology, 19(2), 497-508.
Mane, K M, Kulkarni, D K and Prakash, K B (2021) Prediction of shear strength of concrete produced by using pozzolanic materials and partly replacing NFA by MS using ANN. Journal of Engineering, Design and Technology, 19(2), 578-87.
- Type: Journal Article
- Keywords: accuracy; artificial neural network; replacement; training; experiment; neural network
- ISBN/ISSN:
- URL: http://dx.doi.org/10.1108/JEDT-12-2019-0346
- Abstract:
The use of huge quantity of natural fine aggregate (NFA) and cement in civil construction work which have given rise to various ecological problems. The industrial waste like blast furnace slag (GGBFS), fly ash, metakaolin and silica fume can be partly used as a replacement for cement and manufactured sand obtained from crusher and partly used as fine aggregate. The purpose of this paper is to predict the shear strength of concrete using artificial neural network (ANN) for concrete made by using different pozzolans and partly replacing NFA by manufactured sand (MS) which can reduce the time and experimental cost. In this work, MATLAB software model is developed using neural network toolbox to predict the shear strength of concrete made by using pozzolanic materials and partly replacing NFA by manufactured sand (MS). Shear strength was experimentally calculated, and results obtained from experiment were used to develop the ANN model. A total of 131 results values were used to modeling formation, and from that 30% data record was used for testing purpose and 70% data record was used for training purpose. In total, 25 input materials properties were used to find the 28 days shear strength of concrete obtained from partly replacing cement with pozzolans and partly replacing NFA by manufactured sand (MS). The results obtained from ANN model provide very strong accuracy to predict shear strength of concrete obtained from partly replacing cement with pozzolans and NFA by manufactured sand. This research study is on determining shear strength of concrete using ANN. The use of this study is to predict the shear strength of concrete using ANN for concrete made by using different pozzolans and partly replacing NFA by manufactured sand (MS) which can reduce the time and experimental cost.
Nabizadeh, M, Khalilzadeh, M, Ebrahimnejad, S and Ershadi, M J (2021) Developing a fuzzy goal programming model for health, safety and environment risks based on hybrid fuzzy FMEA-VIKOR method. Journal of Engineering, Design and Technology, 19(2), 317-38.
Nwaogu, J M and Chan, A P C (2021) Evaluation of multi-level intervention strategies for a psychologically healthy construction workplace in Nigeria. Journal of Engineering, Design and Technology, 19(2), 509-36.
Nyawera, J X and Theodore Conrad, H (2021) Critical drivers towards generative process health and safety culture. Journal of Engineering, Design and Technology, 19(2), 385-411.
Sharma, A and Sharma, R K (2021) Uplift behaviour of axial granular pile anchor encased with geogrid in cohesionless soil. Journal of Engineering, Design and Technology, 19(2), 588-602.
Spellacy, J, Edwards, D J, Roberts, C J, Hayhow, S and Shelbourn, M (2021) An investigation into the role of the quantity surveyor in the value management workshop process. Journal of Engineering, Design and Technology, 19(2), 423-45.
Tai, S, Zhang, Y and Li, T (2021) Factors affecting BIM application in China: a social network model. Journal of Engineering, Design and Technology, 19(2), 373-84.
Tijani, B, Jin, X and Osei-Kyei, R (2021) Critical analysis of mental health research among construction project professionals. Journal of Engineering, Design and Technology, 19(2), 467-96.
Tu, J, Liu, Y, Zhou, M and Li, R (2021) Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network. Journal of Engineering, Design and Technology, 19(2), 412-22.