IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15338-d1268282.html
   My bibliography  Save this article

Machine-Learning-Based Comprehensive Properties Prediction and Mixture Design Optimization of Ultra-High-Performance Concrete

Author

Listed:
  • Chang Sun

    (School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Kai Wang

    (School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Qiong Liu

    (School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Pujin Wang

    (School of Civil Engineering, Tongji University, Shanghai 200092, China)

  • Feng Pan

    (Shanghai Construction Industry Fifth Construction Group Co., Ltd., Shanghai 200063, China)

Abstract

Ultra-high-performance concrete (UHPC) is widely used in the field of large-span and ultra-high-rise buildings due to its advantages such as ultra-high strength and durability. However, the large amount of cementitious materials used results in the cost and carbon emission of UHPC being much higher than that of ordinary concrete, limiting the wide application of UHPC. Therefore, optimizing the design of the UHPC mix proportion to meet the basic properties of UHPC with low carbon and low cost at the same time will help to realize the wide application of UHPC in various application scenarios. In this study, the basic properties of UHPC, including the compressive strength, flexural strength, fluidity, and shrinkage properties, were predicted by machine-learning algorithms. It is found that the XGBoost algorithm outperforms others in predicting basic properties, with MAPE lower than 5% and R 2 higher than 0.9 in four output properties. To evaluate the comprehensive performance of UHPC, a further analysis was conducted to calculate the cost- and carbon-emissions-per-unit volume for 50,000 UHPC random mixes. Combined with the analytical hierarchy process (AHP) model, the comprehensive performance of UHPC, including basic properties, cost-per-unit volume, and carbon-emissions-per-unit volume, was evaluated. This study proposes an optimized UHPC mix proportion, based on low-cost or low-carbon emission, oriented to comply with the excellent overall performance and obtain its corresponding various properties.

Suggested Citation

  • Chang Sun & Kai Wang & Qiong Liu & Pujin Wang & Feng Pan, 2023. "Machine-Learning-Based Comprehensive Properties Prediction and Mixture Design Optimization of Ultra-High-Performance Concrete," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15338-:d:1268282
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15338/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/21/15338/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Badri, Masood A., 2001. "A combined AHP-GP model for quality control systems," International Journal of Production Economics, Elsevier, vol. 72(1), pages 27-40, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fathi Abid & Slah Bahloul, 2010. "Selected MENA Countries’ Attractiveness to G7 Investors," Working Papers 531, Economic Research Forum, revised 07 Jan 2010.
    2. Ho, William, 2008. "Integrated analytic hierarchy process and its applications - A literature review," European Journal of Operational Research, Elsevier, vol. 186(1), pages 211-228, April.
    3. Aznar Bellver, Jerónimo & Guijarro Martínez, Francisco & Moreno Jiménez, José María, 2007. "Valoración Agraria multicriterio en un entorno con escasa información/Multicriteria Agricultural Valuation in an Environment with scarce information," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 25, pages 549-572, Abril.
    4. Emre Çalişkan & Erdem Aksakal & Saliha Çetinyokuş & Tahsin Çetinyokuş, 2019. "Hybrid Use of Likert Scale-Based AHP and PROMETHEE Methods for Hazard Analysis and Consequence Modeling (HACM) Software Selection," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1689-1715, September.
    5. Ho, Hui-Ping & Chang, Ching-Ter & Ku, Cheng-Yuan, 2015. "House selection via the internet by considering homebuyers’ risk attitudes with S-shaped utility functions," European Journal of Operational Research, Elsevier, vol. 241(1), pages 188-201.
    6. Jerónimo Aznar & Francisco Guijarro & José Moreno-Jiménez, 2011. "Mixed valuation methods: a combined AHP-GP procedure for individual and group multicriteria agricultural valuation," Annals of Operations Research, Springer, vol. 190(1), pages 221-238, October.
    7. Kahraman, Cengiz & Cebeci, Ufuk & Ruan, Da, 2004. "Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey," International Journal of Production Economics, Elsevier, vol. 87(2), pages 171-184, January.
    8. Topaloglu, Seyda, 2009. "A shift scheduling model for employees with different seniority levels and an application in healthcare," European Journal of Operational Research, Elsevier, vol. 198(3), pages 943-957, November.
    9. Vaidya, Omkarprasad S. & Kumar, Sushil, 2006. "Analytic hierarchy process: An overview of applications," European Journal of Operational Research, Elsevier, vol. 169(1), pages 1-29, February.
    10. Pasura Aungkulanon & Walailak Atthirawong & Pongchanun Luangpaiboon, 2023. "Fuzzy Analytical Hierarchy Process for Strategic Decision Making in Electric Vehicle Adoption," Sustainability, MDPI, vol. 15(8), pages 1-20, April.
    11. Harshitha Urs Ajjipura Shankar & Udaya Kumara Kodipalya Nanjappa & M. D. Alsulami & Ballajja C. Prasannakumara, 2022. "A Fuzzy AHP-Fuzzy TOPSIS Urged Baseline Aid for Execution Amendment of an Online Food Delivery Affability," Mathematics, MDPI, vol. 10(16), pages 1-24, August.
    12. Cinzia Colapinto & Raja Jayaraman & Simone Marsiglio, 2017. "Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review," Annals of Operations Research, Springer, vol. 251(1), pages 7-40, April.
    13. Kengpol, Athakorn & Meethom, Warapoj & Tuominen, Markku, 2012. "The development of a decision support system in multimodal transportation routing within Greater Mekong sub-region countries," International Journal of Production Economics, Elsevier, vol. 140(2), pages 691-701.
    14. Ya Lan WANG & Tainyi LUOR & Pin LUARN & Hsi-peng LU, 2015. "Contribution and Trend to Quality Research—a literature review of SERVQUAL model from 1998 to 2013," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 19(1), pages 34-45.
    15. Yurdakul, Mustafa & Ic, Yusuf Tansel, 2004. "AHP approach in the credit evaluation of the manufacturing firms in Turkey," International Journal of Production Economics, Elsevier, vol. 88(3), pages 269-289, April.
    16. Ayfer Basar & Özgür Kabak & Y. Ilker Topcu, 2017. "A Decision Support Methodology for Locating Bank Branches: A Case Study in Turkey," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 59-86, January.
    17. Bertolini, Massimo & Bevilacqua, Maurizio, 2006. "A combined goal programming—AHP approach to maintenance selection problem," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 839-848.
    18. Łuczak, Aleksandra & Kozera, Agnieszka, 2021. "A model to assess the development priorities of local administrations through the hierarchy of strategic factors," Journal of Policy Modeling, Elsevier, vol. 43(2), pages 474-492.
    19. Özcan, Evren Can & Ünlüsoy, Sultan & Eren, Tamer, 2017. "A combined goal programming – AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1410-1423.
    20. Edward Sanneh & Allen Hu & Chia-Wei Hsu & Momodou Njie, 2014. "Prioritization of climate change adaptation approaches in the Gambia," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 19(8), pages 1163-1178, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15338-:d:1268282. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.