Gestão & Produção
https://www.gestaoeproducao.com/article/doi/10.1590/1806-9649-2021v28e5712
Gestão & Produção
Original Article

Proposed model of analysis of the perception of the relative importance of Critical Success Factors (CSF) in the civil construction industry (CCI) using Artificial Neural Networks (ANNs): application in the academic universe

Mauro Luiz Erpen; André Luiz Aquere de Cerqueira e Souza; Clóvis Neumann; Maria Cristina Bueno Coelho

http://dx.doi.org/10.1590/1806-9649-2021v28e5712 Gest. Prod., vol.28, n4, e5712, 2021
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Abstract

Abstract:: Critical Success Factors (CSF) identify key areas for a company to succeed. This study creates a model to analyze CSF in civil construction project management, using Artificial Neural Networks (ANNs). For that, a literature review was performed to identify CSF emphasizing project management. Once the CSF were identified, a questionnaire was sent to educational institutions to evaluate the effect of each factor. Response analysis was made by the Relative Importance Index, using ANN coupled with the resilient propagation algorithm to evaluate the CSF. A total of 37,822 articles were found in 2,328 journals. Of 874 e-mails sent, 191 were answered. The respondents were distributed in 26 Brazilian states, with 70% of them being professors/researchers, 26% coordinators, 2% Rector, and 1% Director/Manager. Weights were determined using the Garson algorithm. The most critical factor in project management was ‘Unrealistic inspection and test methods in the contract’. Artificial Neural Networks produce subsidies to know the relevance of the input variables adopted and constitute an effective means for modeling nonlinear variables.

Keywords

Civil construction, Project management, Artificial Neural Networks, Critical Success Factors

References

Abourizk, S. M., & Wales, R. J. (1993). Incorporating weather effects in project simulation. In Proceedings of the 5th Computing in Civil and Building Engineering (pp. 1190-1197). Reston: ASCE.

Abourizk, S., Knowles, P., & Hermann, U. (2001). Estimating labor productivity rates for industrial construction activities. Journal of Construction Engineering and Management, 127(6), 502-511. http://dx.doi.org/10.1061/(ASCE)0733-9364(2001)127:6(502).

Apanaviciene, R., & Daugeliene, A. (2011). New classification of construction companies: overhead costs aspect. Journal of Civil Engineering and Management, 17(4), 457-466. http://dx.doi.org/10.3846/13923730.2011.625629.

Apanavičienė, R., & Juodis, A. (2003). Construction projects management effectiveness modelling with neural networks. Journal of Civil Engineering and Management, 9(1), 59-67. http://dx.doi.org/10.1080/13923730.2003.10531302.

Appolinário, F. (2007). Dicionário de metodologia científica. São Paulo: Atlas.

Asgari, H., Odeshi, A., Hosseinkhani, K., & Mohammadi, M. (2018). On dynamic mechanical behavior of additively manufactured AlSi10Mg_200C. Materials Letters, 211, 187-190. http://dx.doi.org/10.1016/j.matlet.2017.10.001.

Barros, V. P. A. (2018). Avaliação do desempenho de algoritmos de retropropagação com redes nweurais artificiais para a resolução de problemas não lineares (Dissertação de mestrado). Universidade Tecnológica Federal do Paraná, Ponta Grossa.

Biondi, L., Gomes, E., Mello, J., & Meza, L. (2004). Redes neurais artificiais para estimativa de custos de construção civil. In Anais do VII Simpósio de Pesquisa Operacional de Logistíca da Marinha. Rio de Janeiro.

Blanning, R. W. (1975). The construction and implementation of metamodels. Simulation, 24(6), 177-184. http://dx.doi.org/10.1177/003754977502400606.

Casarotto, F., Favero, N., & Castro, J. S. (2009). Gerência de projetos e engenharia simultânea(2. ed.). São Paulo: Atlas.

Cervo, A. L., & Bervian, A. (1983). Metodologia científica: para uso dos estudantes universitários (3. ed.). São Paulo: McGraw-Hill do Brasil.

Chao, L. C., & Skibniewski, M. J. (1994). Estimating construction productivity: neuralnetwork-based approach. Journal of Computing in Civil Engineering, 8(2), 234-251. http://dx.doi.org/10.1061/(ASCE)0887-3801(1994)8:2(234).

Chua, D. K. H., Kog, Y. C., & Loh, P. K. (1999). Critical success factors for different project objectives. Journal of Construction Engineering and Management, 125(3), 142-150. http://dx.doi.org/10.1061/(ASCE)0733-9364(1999)125:3(142).

Chua, D. K. H., Loh, P. K., Kog, Y. C., & Jaselskis, E. J. (1997). Neural networks for construction project success. Espert Systems with Applications. 13(4), 317-328. http://dx.doi.org/10.1016/S0957-4174(97)00046-8.

Colauto, R. D., Gonçalves, C. M., Beuren, I. M., & Santos, N. (2004). Os fatores críticos de sucesso como suporte ao sistema de inteligência competitiva: o caso de uma empresa brasileira. Revista de Administração Mackenzie., 5(2), 119-146.

Cooke-Davies, T. (2002). The “real” success factors on projects. International Journal of Project Management, 20(3), 185-190. http://dx.doi.org/10.1016/S0263-7863(01)00067-9.

Costantino, F., Di Gravio, G., & Nonino, F. (2015). Project selection in project portfolio management: an artificial neural network model based on critical success factors. International Journal of Project Management, 33(8), 1744-1754. http://dx.doi.org/10.1016/j.ijproman.2015.07.003.

Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Orlando: Harcourt Brace Jovanovich College Publishers

Dalmoro, M., & Vieira, K. M. (2013). Dilemas na construção de escalas Tipo Likert: o número de itens e a disposição influenciam nos resultados? Revista Gestão Organizacional, 6(3), 161-174.

Doloi, H. (2012). Cost overruns and failure in project management - understanding the roles of key stakeholders in construction projects. Journal of Construction Engineering and Management, 139(3):267-279. http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000621.

El-Gohary, K., & Aziz, R. (2014). Factors influencing construction labor productivity in Egypt. Journal of Management Engineering, 30(1), 1-9. http://dx.doi.org/10.1061/(ASCE)ME.1943-5479.0000168. ]

Elwakil, E., Ammar, M., Zayed, T., Mahmoud, M., Eweda, A., & Mashhour, I. (2009). Investigation and modeling of critical success factors in construction organizations. In Construction Research Congress. Seattle: ASCE. http://dx.doi.org/10.1061/41020(339)36.

Enshassi, A., Mohamed, S., & Mayer, E. (2007). Factors affecting labor productivity in building projects in the Gaza Strip. Journal of Civil Engineering and Management, 13(4), 245-254. http://dx.doi.org/10.3846/13923730.2007.9636444.

Fortune, J., & White, D. (2006). Framing of project critical success factors by a systems model. International Journal of Project Management, 24(1), 53-65. http://dx.doi.org/10.1016/j.ijproman.2005.07.004.

Foucquier, A., Robert, S., Suard, F., Stéphan, L., & Jay, A. (2013). State of the art in building modeling and energy performances prediction: a review. Renewable & Sustainable Energy Reviews, 23, 272-288. http://dx.doi.org/10.1016/j.rser.2013.03.004.

França, D. C. (2016). Modelagem de um Adaptative Neuro Fuzzy Inference Sistem para análise de risco em projetos (Masters dissertation). Universidade Fedral da Paraíba, Centro de Tecnologia, João Pessoa.

Freitag, A. E. B. (2015). Fatores críticos de sucesso para adoção da gestão “enxuta” pela indústria da Construção civil do Estado do Rio de Janeiro (Tese de doutorado). Universidade Federal Fluminense, Niterói.

Garson, D. G. (1991). Interpreting neural-network connection weights. AI Expert, 4(6), 47-51.

Gorsuch, R. L. (1983). Factor analysis (2nd ed., 425 p.). Hillsdale, NJ: Lawrence Erlbaum Associates.

Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin, 103(2), 265-275. http://dx.doi.org/10.1037/0033-2909.103.2.265. PMid:3363047.

Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989-993. http://dx.doi.org/10.1109/72.329697. PMid:18267874.

Hair, J. F., Jr., Black, W., Babin, B. J., & Anderson, R. E. (1998). Multicariate data analysis. New Jersey: Prentice Hall.

Haykin, S. (2001). Redes neurais: princípios e prática. Porto Alegre: Bookman.

Holohan, J. (1992). Use of executive information system in measuring business performance. Journal of Information Technology, 7(3), 177-186. http://dx.doi.org/10.1177/026839629200700307.

Jarkas, A., & Bitar, G. (2012). Factors affecting construction labor productivity in Kuwait. Journal of Construction Engineering and Management, 138(7), 811-820. http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000501. ]

Jiménez, M. J., Enríquez, R., Lopez, L. C., & Heras, M. R. (2013). Data analysis methods for building energy performance characterization: context and CIEMAT experiences in applications to test campaigns in warm sunny weather In Workshop on High Performance Buildings - Design and Evaluation Methodologies. Brussels: Brussels Institute for Energy and Transportation, European Commission.

Jordão, R. V. D., Pelegrini, F. G., Jordão, A. C.T., & Jeunon, E. (2015). Fatores críticos na gestão de projetos: um estudo de caso numa grande empresa latino-americana de classe mundial. Gestão e Produção, 22(2), 280-294. http://dx.doi.org/10.1590/0104-530X1091-13.2015.

Kalogirou, S. A. (2001). Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews, 5(4), 373-401.

Karshenas, S., & Feng, X. (1992). Application of neural networks in earthmoving equipment production estimating. In Proceedings of the 8th Computing in Civil Engineering and Geographic Information systems (pp. 841-847). New York: ASCE.

Kikuti, K. (2016). Fatores críticos de sucesso em projetos internacionais da construção civil (Dissertação de mestrado). Departamento de Administração, Faculdade de Economia, Administração e Contabilidade, Universidade de São Paulo, São Paulo.

Knowles, R. P. (1997). Predicting construction labour productivity using neural networks (Masters dissertation). Department of Civil and Environmental Engineering, University of Alberta, Alberta.

Kog, Y. C., & Loh, P. K. (2012). Critical success factors for different components of construction projects. Journal of Construction Engineering and Management, 138(4), 520-528. http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000464.

Laros, J. A. (2012). O uso da análise fatorial: algumas diretrizes para pesquisadores. In L. Pasquali (Ed.), Análise fatorial para pesquisadores. Brasília: LabPAM Saber e Tecnologia.

Leite, A. C. P. (2018). Fatores críticos de sucesso em projetos de construção (Dissertação de mestrado). Escola Superior de Tecnologia e Gestão Politécnico do Porto, Felgueiras.

Likert, R. (1932). A technique for the measurement of attitudes. Archives de Psychologie, 140, 1-55.

Lopes, D. (2009). Critérios de avaliação do desempenho de gerenciamento de projetos: uma abordagem de estudo de casos (Masters dissertation). Escola Politécnica da Universidade de São Paulo, São Paulo.

Lorenzi, A., Silva, B. V., Barbosa, M. P., & Silva, L. C. P., Fo. (2017). Aplicação de redes neurais artificiais na predição da aderência aço-concreto em ensaios do tipo pull-out. IBRACON Structures and Materials Journal, 10(5), 1051-1074. http://dx.doi.org/10.1590/S1983-41952017000500007.

Malhotra, N. K. (2001). Pesquisa de mercado: uma orientação aplicada (3. ed.). Porto Alegre: Bookman.

Meisel, W. S., & Collins, D. C. (1973). Repro-modeling: an approach to efficient model utilization and interpretation. IEEE Systems Man and Cybernetics Society, 3(4), 349-358. http://dx.doi.org/10.1109/TSMC.1973.4309245.

Melo, A. P. (2012). Desenvolvimento de um método para estimar o consumo de energia de edificações comerciais através da aplicação de redes neurais (Doctoral thesis). Departamento de Engenharia Civil, Universidade Federal de Santa Catarina, Florianópolis.

Mori, L. M. (2008). Sistema de Informação Gerencial para previsão de Produtividade do Trabalho na alvanaria de elevação (Doctoral thesis). Universidade Federal de Santa Catarina, Florianópolis.

Morioka, S., & Carvalho, M. M. (2014). Análise de fatores críticos de sucesso de projetos: um estudo de caso no setor varejista. Produção, 24(1), 132-143. http://dx.doi.org/10.1590/S0103-65132013005000015.

Moselhi, O., Hegazy, T., & Fazio, P. (1991). Neural network as tool in construction. Journal of Construction Engineering and Management, 117(4), 606-623. http://dx.doi.org/10.1061/(ASCE)0733-9364(1991)117:4(606).

Paschoal, M. S. (2014). Fatores críticos de sucesso: um estudo sobre sua influência no desempenho de projetos de Construção civil (Dissertação de mestrado). Programa de Pós-gradfuação em Engenharia de Produção, Universidade Metodista de Piracicaba, Piracicaba.

Pinto, J. K., & Slevin, D. P. (1988). Critical success factors across the project life cycle. Project Management Journal, 19(3), 67-75.

Portas, P., & Abourizk, S. (1997). Neural network model for estimating construction productivity. Journal of Construction Engineering and Management, 123(4), 399-410. http://dx.doi.org/10.1061/(ASCE)0733-9364(1997)123:4(399).

Ribeiro, A. J. A., Silva, C. A. U., & Barroso, S. H. A. (2018). Metodologia de baixo custo para mapeamento geotécnico aplicado à pavimentação. Revista Transportes, 26(2), 84-100.

Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The rprop algorithm. In Proceeding of the IEEE International Conference on Neural Networks (pp. 586-591). San Francisco: IEEE. http://dx.doi.org/10.1109/ICNN.1993.298623.

Saqib, M., Farooqui, R. U., & Lodi, S. H. (2008). Assessment of critical success factors for construction projects in Pakistan. In Proceedings of the First International Conference on Construction In Developing Countries (ICCIDC–I) (pp. 392-404). Karachi, Pakistan.

Silva, A. N. R., Ramos, R. A. R., Souza, L. C. L., Rodrigues, D. S., & Mendes, J. F. G. (2004). SIG: uma plataforma para introdução de técnicas emergentes no planejamento urbano, regional e de transportes - Uma ferramenta 3D para análise ambiental urbana, avaliação multicritério e redes neurais artificiais (227 p.). São Paulo: Ed. dos Autores.

Sonmez, R., & Rowings, J. E. (1998). Construction labour productivity modeling with neural networks. Journal of Construction Engineering and Management, 124(6), 498-504. http://dx.doi.org/10.1061/(ASCE)0733-9364(1998)124:6(498).

Tam, C. M., Tong, T. K. L., & Tse, S. L. (2002). Artificial neural networks model for predicting excavator productivity. Engineering, Construction and Architectural Management, 5(6), 446-452. http://dx.doi.org/10.1108/eb021238.

Tsiga, Z., Emes, M. & Smith, A. (2016). Critical success factors for projects in the space sector. Journal of Modern Project Mangement, 3(3), 56-63.

Valença, M. J. S. & Ludemir, T. B. (2007). Explicando a relação entre as variáveis de uma rede neural: Iluminando a “Caixa Preta”. In Anais do XVII Simpósio Brasileiro de Recursos Hídricos. São Paulo: ABRhidro.

Waziri, B. S., Bala, K., & Bustani, S. A. (2017). Artificial neural networks in construction engineering and management. International Journal of Architecture, Engineering and Construction, 6(1), 50-60.

Yi, W., & Chan, A. P. (2013). Optimizing worke-rest schedule for construction rebar workers in hot and humid environment. Building and Environment, 61, 103-113. http://dx.doi.org/10.1016/j.buildenv.2012.12.012.

Zayed, T., Elwakil, E., & Ammar, M. (2012). A framework for performance assessment of organizations in the construction industry. International Journal of Architeture, Engineering and Construction, 1(4), 199-212.
 

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