Use of artificial neural networks in the prediction of horizontal and friction pressures in a slender silo
DOI:
https://doi.org/10.34117/bjdv9n6-004Keywords:
silos, artificial intelligence, structural reliabilityAbstract
Structural reliability studies in silo pressure predictions are a subject of great interest because the number of variables in silo pressure calculations is large, which can generate numerous failures and occurrences of collapse. The objective of this study was to propose a pressure prediction model using artificial neural networks (ANNs) in comparison with experimental data. For data collection, a pilot silo model, proposed by Pieper and Schutz (1980), adopted by the German standard DIN 1055 (1987), was used. The pressure measurements were performed during loading and unloading of the silo. Smooth walls with flat bottom were used in the silo, varying the height/diameter ratio between 4, 6 and 8. The pressures observed on the silo walls were the horizontal pressures and the friction pressure of the product with the wall. The results of the pressures obtained experimentally were fed into an algorithm using multilayer Perceptron ANNs. The pressures measured on the silo walls were compared with the values predicted by ANNs, linear and polynomial regressions, and also with the values calculated by means of the European Standard EN 1991-4: 2006 and Australian Standard AS 3774 (1996). Among the models used, the best performance was obtained by ANNs, which were able to predict silo wall pressures with smaller errors.
References
AMERICAN CONCRETE INSTITUTE. ACI 313: Standard Practice for Design and Construction of Concrete Silos and Stacking Tubes for Storing Granular Materials. EUA, 1991.
AMERICAN NATIONAL STANDARDS INSTITUTE. ANSI/ASAE EP 433:1991 (reaffirmed DEC/2001). Loads exerted by free-flowing grain on shallow storage structures. EUA, 2001.
ARAÚJO, C. B. C., DANTAS NETO, S. A. e SOUZA FILHO, F. A. Streamflow forecasting for the dam Orós/CE from hydrometeorological data using perceptrons. Rev. Bras. Meteorol. 30/ Mar 2015.
AUSTRALIAN STANDARD AS 3774. (1996) Loads on bulk containers. Sydney. AS 3774 Supplement 1 (1996).” Loads on bulk containers – Commentary. Sydney.
BROWN, C. J.; LAHLOUH, E. H.; ROTTER, J. M. Experiments on a square planform steel silo. Chemical Engineering Science, v. 55, n. 20, p. 4399-4413, 2000.
CANADIAN FARM BUILDING CODE CBFC - Part 2, Structural design. Ottawa, Canada, 1983.
CARVALHO, Haridasa F. Análise de tensões em silos circulares metálicos de baixa relação altura/diâmetro. 2018. 43 f. Trabalho de Conclusão do Curso (Bacharelado em Engenharia Civil) - Universidade Tecnológica Federal do Paraná. Pato Branco, 2018.
CHEUNG, A. B. Modelo estocástico de pressões de produtos armazenados para a estimativa da confiabilidade estrutural de silos esbeltos. 2007. 333 p. Tese (Doutorado em Engenharia de Estruturas) - Universidade de São Paulo, São Paulo, 2007.
CONAB. COMPANHIA NACIONAL DE ABASTECIMENTO. Acompanhamento de safra brasileiro – grãos: 4º Levantamento - Safra 2021/22: Brasília: Companhia Nacional de Abastecimento. 2001. Disponível em: https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos. Acesso em: 02 jan. 2021.
COUTO, A.; RUIZ, A.; AGUADO, P. J. Design and instrumentation of a mid-size test station for measuring static and dynamic pressures in silos under different conditions - Part I: Description. Computers and Electronics in Agriculture, v. 85, p. 164-173, 2012.
DIN 1055. Design loads for buildings: loads in silo bins. Berlin: Deutsche Norm, 1987. 6p.
DEUTSCHE NORM - DIN 1055-6: Basis of design and actions on structures – Part 6: design loads for buildings and loads in silo bins. Berlin, Verlaz, 2005.
EUROPEAN COMMITTEE OF STANDARDIZATION. EN 1991-4: Eurocode 1: Basis of Design and Actions on Structures, Part 4 – Silos and Tanks. CEN, Brussels, 2006.
FERREIRA, A., FERREIRA, R., SILVA, A., FERREIRA, A. Um estudo sobre previsão da demanda de encomendas utilizando uma rede neural artificial. XVIII simpósio de pesquisa operacional & logística da marinha. Aug, 2016.
GANDIA, Rômulo Marçal et al. Influence of specific weight and wall friction coefficient on normal pressures in silos using the Finite Element Method. Revista Engenharia na Agricultura-REVENG, v. 29, p. 192-203, 2021.
GOLAFSHANI, E. M.; BEHNOOD, A. Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete. Journal of Cleaner Production, v. 176, p. 1163–1176, 2018.
ISAAC JÚNIOR, M. A. Modelo misto e rede neural artificial para estimativas dendométricas de árvores individuais. 2018. 82 f. Tese (Doutorado em Engenharia Florestal) - Departamento de ciências florestais, Universidade Federal de Lavras, Lavras – MG.
INTERNATIONAL ORGANIZATION FOR STANDART ISO 11697: Bases for design of structures: Loads due to bulk materials, London, 2012.
JANSSEN, H. A. Versuchüber Getreidedruck in Sillozellen. Zeitschrift des Vereins Deutscher Ingenieure, v. 39, p. 1045–1049, 1895.
JENIKE, A. W.; JOHANSON, J. R.; CARSON, J. W. Bin loads—Part 2: concepts. Journal of Engineering for Industry, v. 95, n. 1, p. 1-5, 1973.
LIMA, G. R. T. e SCOFIELD, G. B. Feasibility study on operational use of neural networks in a flash flood early warning system. Scientific Technical Article. 2021.
MEIRA A.S., NASCIMENTO J.W.B., SILVA V.R., NASCIMENTO R.T. & SOARES H.R. (2020) Análise comparativa de pressões horizontais durante o carregamento em silos verticais metálicos. Pesquisa e Ensino em Ciências Exatas e da Natureza, 4: e1539. http://dx.doi.org/10.29215/pecen.v4i0.1539 Editor acadêmico: Angel Ramon Sanchez Delgado. Recebido: 04 setembro 2020. Aceito: 30 outubro 2020. Publicado: 15 dezembro 2020.
MOREIRA I. I. P.; MORAIS E. C.; MORAES R. I. R.; ZISSOU A. J.; CAMPOS P. S. S.; MARQUES G. T.; CHASE O. A. & ALMEIDA J. F. S. Redes neurais artificiais para previsão de capacidade de carga em estacas do tipo hélice contínua. Brazilian Journal of Development, 7(12), 112577-112597. https://doi.org/10.34117/bjdv7n12-171
PIEPER, K.; SCHÜTZ, M. Bericht Über das Forschungsvorhaben - Norm-Mess-Silo für Schüttguteigenschaften. Technische Universität Braunschweig - Lehrstuhl für Hochbaustatik, Deutschland, 1980, 109p.
RAMÍREZ, A.; NIELSEN, J; AYUGA, F. Pressure measurements in steel silos with eccentric hoppers. Powder Technology, v. 201, n. 1, p. 7-20, 2010.
REIS, S. G. dos. Determinação das ações atuantes nos silos através de redes neurais artificiais. 2011. 76 p. Dissertação (Mestrado em Engenharia de Sistemas) - Universidade Federal de Lavras, Lavras, 2011.
SIMÕES, M. G.; SHAW, I. S. Controle e Modelagem fuzzy. São Paulo: Blucher: FAPESP, 2007
TAVARES, D. S.; RIBEIRO, D. A.; YANAGI JUNIOR, T.; LACERDA, W. S.; TIRADENTES, E. T.; TEIXEIRA, R. G. & GARCIA, H. V. S. 2020. Use of artificial neural networks to predict concrete compression strength. Brazilian Journal of Development, 6(7), 42815–42826. https://doi.org/10.34117/bjdv6n7-050
VAZIRIZADE, S. M.; NOZHATI, S.; ZADEH, M. A. Seismic reliability assessment of structures using artificial neural network. Journal of Building Engineering, v. 11, n. November 2016, p. 230–235, 2017.
YUKSEL, S. B.; ARSLAN, M. H. Design force estimation using artificial neural network for groups of four cylindrical silos. Advances in Structural Engineering, v. 13, n. 4, p. 681-693, 2010.
