Using Fuzzy Inference Systems for Lean Management Strategies in Construction Project Delivery
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 9
Abstract
When using lean waste management in construction project delivery, computational methodologies are currently an innovative technology for the implementation of efficient and effective improvement strategies in the development of Industry 4.0 in Chile. Lean models are able to manage data obtained from construction projects along with the data obtained from the knowledge base of professional experts (expert survey). The waste management of construction projects under the lean philosophy requires cooperative efforts, where the opinion of professional experts is completely paramount to analyze multidisciplinary knowledge. Therefore, new protocols and disruptive procedures based on artificial intelligence (AI) tools can help decision makers prioritize activities, minimize uncertainty, and avoid wasteful actions that add no value to the project and thus can be minimized or completely eliminated. The vagueness of subjective human judgment in the degree of application of lean waste management in project delivery is modeled by a fuzzy logic model that includes additional considerations related to the lean implementation. Moreover, multiple linear regression analysis has been implemented in order to verify and validate the previous digital fuzzy model. In this sense, the main aim of this study is to develop new approaches regarding AI systems, using fuzzy sets and multiple linear regression for managing waste in construction project delivery in the metropolitan area of Santiago, Chile. A theorized application of the models reveals that the sample (100 construction projects) can be classified into three lean waste condition levels: high, medium, or low waste effects. The outcomes of this research will contribute to the Chilean construction industry environment and will open new ways for harnessing AI-based technology in the construction industry to the fullest potential, to achieve better time and cost predictability with a client- and end-user-centered world view.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This paper was also funded by Agencia Nacional de Investigación y Desarrollo (ANID) of Chile throughout the research projects ANID FONDECYT 11190554.
References
Akinosho, T. D., L. O. Oyedele, M. Bilal, A. O. Ajayi, M. D. Delgado, O. O. Akinade, and A. A. Ahmed. 2020. “Deep learning in the construction industry: A review of present status and future innovations.” J. Build. Eng. 32 (Nov): 101827. https://doi.org/10.1016/j.jobe.2020.101827.
Akugizibwe, A. M., and D. R. Clegg. 2014. “Lean implementation: An evaluation from the implementers’ perspective.” Int. J. Lean Enterpr. Res. 1 (2): 132. https://doi.org/10.1504/IJLER.2014.066832.
Alarcón, L. F. 1997. Lean construction. New York: Balkema.
Alves, A. C., F. J. Kahlen, S. Flumerfelt, and A. B. Siriban-Manalang. 2019. Lean engineering for global development. New York: Springer.
Bai, Y., H. Zhuang, and D. Wang. 2007. “Advanced fuzzy logic technologies in industrial applications.” Technometrics 49 (4): 494–495. https://doi.org/10.1198/tech.2007.s689.
Bajjou, M. S., and A. Chafi. 2020. “Identifying and managing critical waste factors for lean construction projects.” Eng. Manage. J. 32 (1): 2–13. https://doi.org/10.1080/10429247.2019.1656479.
Barth, K. B., and C. T. Formoso. 2021. “Requirements in performance measurement systems of construction projects from the lean production perspective.” Front. Eng. Manage. 8 (Sep): 442–455. https://doi.org/10.1007/s42524-020-0108-2.
Bertelsen, S., and L. Koskela. 2004. “Construction beyond lean: A new understanding of construction management.” In Proc., 12th Annual Conf. in the Int. Group for Lean Construction, 1–11. Edmonton, AB, Canada: International Group for Lean Construction.
Bhamu, J., and K. S. Sangwan. 2014. “Lean manufacturing: Literature review and research issues.” Int. J. Oper. Prod. Manage. 34 (7): 876–940. https://doi.org/10.1108/IJOPM-08-2012-0315.
Cabello-Briones, C., A. J. Prieto, and P. Ortiz. 2021. “Determination of the technical suitability of shelters for archaeological sites using fuzzy logic.” J. Cult. Heritage 48 (Mar): 211–226. https://doi.org/10.1016/j.culher.2020.11.006.
Castillo, C., P. Melin, and J. Kacprzyk. 2018. “Fuzzy logic augmentation of neural and optimization algorithms: Theoretical aspects and real applications.” In Vol. 749 of Studies in computational intelligence. New York: Springer.
Cha, H. S., J. Kim, and J.-Y. Han. 2009. “Identifying and assessing influence factors on improving waste management performance for building construction projects.” J. Constr. Eng. Manage. 135 (7): 647–656. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:7(647).
Chai, Y., L. Jia, and Z. Zhang. 2009. “Mamdani model based adaptive neural fuzzy inference system and its application in traffic level of service evaluation.” In Vol. 4 of Proc., 6th Int. Conf. on Fuzzy Systems and Knowledge Discovery, FSKD 2009, 555–559. New York: IEEE.
Chen, C. C., C. J. Chang, Z. Y. Zhuang, and D. C. Li. 2020. “An envelopment learning procedure for improving prediction accuracies of grey models.” Comput. Ind. Eng. 139 (May): 106185. https://doi.org/10.1016/j.cie.2019.106185.
Eastman, C. M. 2018. Building product models: Computer environments supporting design and construction. Boca Raton, FL: CRC Press.
EC (European Commission). 2021. European construction sector observatory. Brussels, Belgium: EC.
Egan, S. J. 2018. “Construction task force.” Accessed July 2, 2022. https://www.designingbuildings.co.uk/wiki/Construction_Task_Force.
Egwim, C. N., H. Alaka, L. O. Toriola-Coker, H. Balogun, and F. Sunmola. 2021. “Applied artificial intelligence for predicting construction projects delay.” Mach. Learn. Appl. 6 (Dec): 100166. https://doi.org/10.1016/j.mlwa.2021.100166.
Fayek, A. R. 2020. “Fuzzy logic and fuzzy hybrid techniques for construction engineering and management.” J. Constr. Eng. Manage. 146 (7): 04020064. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001854.
Furlan, A., A. Vinelli, and G. D. Pont. 2011. “Complementarity and lean manufacturing bundles: An empirical analysis.” Int. J. Oper. Prod. Manage. 31 (8): 835–850. https://doi.org/10.1108/01443571111153067.
Ghodrati, N., T. Wing Yiu, S. Wilkinson, and M. Shahbazpour. 2018. “Role of management strategies in improving labor productivity in general construction projects in New Zealand: Managerial perspective.” J. Manage. Eng. 34 (6): 04018035. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000641.
Hallam, C., and C. Contreras. 2016. “Integrating lean and green management.” Manage. Decis. 54 (9): 2157–2187. https://doi.org/10.1108/MD-04-2016-0259.
Hamzeh, F., V. A. González, L. F. Alarcon, and S. Khalife. 2021. “Lean construction 4.0: Exploring the challenges of development in the AEC industry.” In Proc., 29th Annual Conf. of the Int. Group for Lean Construction (IGLC29). Edited by L. F. Alarcon and V. A.González, 207–216. Edmonton, AB, Canada: International Group for Lean Construction. https://doi.org/10.24928/2021/0181.
Harris, F., R. MacCaffer, A. Baldwin, and F. Edum-Fotwe. 2021. Modern construction management. 8th ed. New York: Wiley.
Hendiani, S., and M. Bagherpour. 2019. “Developing an integrated index to assess social sustainability in construction industry using fuzzy logic.” J. Cleaner Prod. 230 (Sep): 647–662. https://doi.org/10.1016/j.jclepro.2019.05.055.
Jiang, L. S., and H. Liao. 2020. “Mixed fuzzy least absolute regression analysis with quantitative and probabilistic linguistic information.” Fuzzy Sets Syst. 387 (May): 35–48. https://doi.org/10.1016/j.fss.2019.03.004.
Klawonn, F., R. Kruse, and R. Winkler. 2015. “Fuzzy clustering: More than just fuzzification.” Fuzzy Sets Syst. 281 (Dec): 272–279. https://doi.org/10.1016/j.fss.2015.06.024.
Kosko, B. 1994. “Fuzzy systems as universal approximators.” IEEE Trans. Comput. 43 (11): 1329–1333. https://doi.org/10.1109/12.324566.
Kothamasu, R., and S. H. Huang. 2007. “Adaptive Mamdani fuzzy model for condition-based maintenance.” Fuzzy Sets Syst. 158 (24): 2715–2733. https://doi.org/10.1016/j.fss.2007.07.004.
Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel. 2006. “World map of the Köppen-Geiger climate classification updated.” Meteorol. Z. 15 (3): 259–263. https://doi.org/10.1127/0941-2948/2006/0130.
Lekan, A., and O. Segunfunmi. 2018. “Lean concept thinking-based quality management model for residential building construction projects.” Int. J. Mech. Eng. Technol. 9 (6): 584–597.
Mandujano, M. G., L. F. Alarcón, J. Kunz, and C. Mourgues. 2016. “Identifying waste in virtual design and construction practice from a Lean Thinking perspective: A meta-analysis of the literature.” Rev. Constr. 15 (3): 107–118. https://doi.org/10.4067/S0718-915X2016000300011.
Marsden, P. 2019. Digital quality management in construction. Abingdon, UK: Taylor and Francis.
Maulud, D., and A. M. Abdulazeez. 2020. “A review on linear regression comprehensive in machine learning.” J. Appl. Sci. Techno. Trends 1 (4): 140–147. https://doi.org/10.38094/jastt1457.
Mesa, H. A., K. R. Molenaar, and L. F. Alarcón. 2019. “Comparative analysis between integrated project delivery and lean project delivery.” Int. J. Project Manage. 37 (3): 395–409. https://doi.org/10.1016/j.ijproman.2019.01.012.
MINVU (Ministerio de Vivienda y Urbanismo). 2023. “Ministries of state of the country.” Accessed June 2, 2023. http://www.minvu.cl/.
Montgomery, D. C., E. A. Peck, and G. G. Vining. 2021. Introduction to linear regression analysis. New York: Wiley.
Oberlender, G. D. 2014. Project management for engineering and construction. New York: McGraw-Hill.
Ohno, T. 1988. Toyota production system: Beyond large-scale production. Boca Raton, FL: CRC Press.
Pekuri, A., M. Herrala, A. Aapaoja, and H. Haapasalo. 2012. “Applying Lean in construction–cornerstones for implementation.” In Proc., 20th Annual Conf. of the Int. Group for Lean Construction, 18–20. Edmonton, AB, Canada: International Group for Lean Construction.
Prieto, A. J. 2021. “Fuzzy systems in the digital management of heritage timber buildings in South Chile.” Build. Res. Inf. 49 (8): 878–892. https://doi.org/10.1080/09613218.2021.1889350.
Prieto, A. J., and L. F. Alarcón. 2022. “Digital lean management system in residential construction projects.” In Proc., CIB–World Building Congress (WBC). Kanata, ON, Canada: CIB.
Prieto, A. J., and A. Silva. 2020. “Service life prediction and environmental exposure conditions of timber claddings in South Chile.” Build. Res. Inf. 48 (2): 191–206. https://doi.org/10.1080/09613218.2019.1631143.
Robinson, G. 2015. “Global construction market to grow $8 trillion by 2030: Driven by China, US and India.” Global Constr. 44 (May): 8–10.
Roosen, T. J., and D. J. Pons. 2013. “Environmentally lean production: The development and incorporation of an environmental impact index into value stream mapping.” J. Ind. Eng. 2013 (Dec): 1–17. https://doi.org/10.1155/2013/298103.
Sanders, A., C. Elangeswaran, and J. Wulfsberg. 2016. “Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing.” J. Ind. Eng. Manage. 9 (3): 811–833. https://doi.org/10.3926/jiem.1940.
Satoglu, S., A. Ustundag, E. Cevikcan, and M. B. Durmusoglu. 2018. “Lean production systems for Industry 4.0.” In Industry 4.0: Managing the digital transformation, 43–59. New York: Springer.
Sawhney, A., M. Riley, and J. Irizarry. 2020. Construction 4.0: An innovation platform for the built environment. Milton Park, UK: Routledge.
Silva, A., J. de Brito, and P. L. Gaspar. 2016. Methodologies for service life prediction of buildings. New York: Springer.
Tezel, A., L. Koskela, and Z. Aziz. 2018. “Lean thinking in the highways construction sector: Motivation, implementation and barriers.” Prod. Plann. Control 29 (3): 247–269. https://doi.org/10.1080/09537287.2017.1412522.
Vargas, M. 2016. “Tacit collusion in housing markets: The case of Santiago, Chile.” Appl. Econ. 48 (54): 5257–5275. https://doi.org/10.1080/00036846.2016.1176111.
Wang, T. 2019. Value added of US construction as a percentage of GDP 2018. New York: Statista.
Womack, J. P., and D. T. Jones. 1997. “Lean thinking—Banish waste and create wealth in your corporation.” J. Oper. Res. Soc. 48 (11): 1148. https://doi.org/10.1057/palgrave.jors.2600967.
Womack, J. P., D. T. Jones, and D. Roos. 1990. The machine that changed the world. New York: Free Press.
Yazdani-Chamzini, A., and S. H. Yakhchali. 2012. “Tunnel boring machine (TBM) selection using fuzzy multicriteria decision making methods.” Tunnelling Underground Space Technol. 30 (Jul): 194–204. https://doi.org/10.1016/j.tust.2012.02.021.
Zadeh, L. A. 1965. “Fuzzy sets.” Inf. Control 8 (3): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.
Zhan, W., W. Pan, and G. Hao. 2022. “Productivity measurement and improvement for public construction projects.” J. Constr. Eng. Manage. 148 (6): 1–12. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002282.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 12, 2022
Accepted: Apr 20, 2023
Published online: Jul 3, 2023
Published in print: Sep 1, 2023
Discussion open until: Dec 3, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction industry
- Construction management
- Construction wastes
- Environmental engineering
- Fuzzy logic
- Industrial wastes
- Lean construction
- Pollutants
- Project delivery
- Project management
- Solid wastes
- Waste management
- Wastes
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.