Skip to main content
SpringerLink
Log in

Estimating the ultra-high-performance concrete compressive strength with a machine learning model via meta-heuristic algorithms

  • Original Paper
  • Published:
Multiscale and Multidisciplinary Modeling, Experiments and Design Aims and scope Submit manuscript

Abstract

In recent years, many experimental articles have been conducted to study ultra-high-performance concrete (UHPC). Thus, the relationship between its blend composition and the mechanical properties of UHPC is highly non-linear and challenging to utilize conventional statistical approaches. A robust and sophisticated method is needed to rationalize the variety of relevant experimental datasets, provide insight into aspects of non-linear materials science, and make estimative tools of desirable accuracy. Machine learning (ML) is a potent strategy that can reveal underlying patterns in complex datasets. This study aims to employ state-of-the-art ML methods for predicting the UHPC compressive strength (CS) by operating 165 previously published samples with 8 input characteristics via support vector regression (SVR). In addition, a novel approach has been used based on meta-heuristic algorithms to enhance accuracy, including Dynamic Arithmetic Optimization Algorithm (DAOA), Arithmetic Optimization Algorithm (AOA), and Black Widow Optimization (BWO). Furthermore, the models evaluated the prediction input dataset by some criteria indicators. The results indicated that the represented models obtained suitable estimative efficiency and can be reliable on ML methods in saving time and energy. In general, in comparing hybrid models, SVDA has a more acceptable performance than other hybrid models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The authors do not have permissions to share data.

References

  • Abellán García J, Fernández Gómez J, Torres Castellanos N (2020) Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks. Eur J Environ Civ Eng 1–25

  • Abuodeh OR, Abdalla JA, Hawileh RA (2020) Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. Appl Soft Comput 95:106552

    Article  Google Scholar 

  • Akbarzadeh MR, Ghafourian H, Anvari A, Pourhanasa R, Nehdi ML (2023) Estimating compressive strength of concrete using neural electromagnetic field optimization. Materials (basel) 16:4200

    Article  Google Scholar 

  • Alabduljabbar H, Khan M, Awan HH, Eldin SM, Alyousef R, Mohamed AM (2023) Predicting ultra-high-performance concrete compressive strength using gene expression programming method. Case Stud Constr Mater 18:e02074

    Google Scholar 

  • An S-H, Park U-Y, Kang K-I, Cho M-Y, Cho H-H (2007) Application of support vector machines in assessing conceptual cost estimates. J Comput Civ Eng 21:259–264

    Article  Google Scholar 

  • Awodiji CTG, Onwuka DO, Okere C, Ibearugbulem O (2018) Anticipating the compressive strength of hydrated lime cement concrete using artificial neural network model. Civ Eng J 4:3005–3018

    Article  Google Scholar 

  • Behnam S, Tejani GG, Kumar S (2023) Predict the maximum dry density of soil based on individual and hybrid methods of machine learning. Adv Eng Intell Syst. https://doi.org/10.2203/aeis.2023.414188.1129

    Article  Google Scholar 

  • Castelli M, Vanneschi L, Silva S (2013) Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. Expert Syst Appl 40:6856–6862

    Article  Google Scholar 

  • Çevik A, Kurtoğlu AE, Bilgehan M, Gülşan ME, Albegmprli HM (2015) Support vector machines in structural engineering: a review. J Civ Eng Manag 21:261–281

    Article  Google Scholar 

  • Chang W, Zheng W (2020) Effects of key parameters on fluidity and compressive strength of ultra-high performance concrete. Struct Concr 21:747–760

    Article  Google Scholar 

  • Chen B-T, Chang T-P, Shih J-Y, Wang J-J (2009) Estimation of exposed temperature for fire-damaged concrete using support vector machine. Comput Mater Sci 44:913–920

    Article  Google Scholar 

  • Cheng H, Kitchen S, Daniels G (2022) Novel hybrid radial based neural network model on predicting the compressive strength of long-term HPC concrete. Adv Eng Intell Syst 1(02):1–11

    Google Scholar 

  • Chou JS, Tsai CF, Pham AD, Lu YH (2014) Machine learning in concrete strength simulations: multi-nation data analytics. Constr Build Mater 73:771–780. https://doi.org/10.1016/j.conbuildmat.2014.09.054

    Article  Google Scholar 

  • Ghafari E, Bandarabadi M, Costa H, Júlio E (2012) Design of UHPC using artificial neural networks. Brittle Matrix Compos, vol 10. Elsevier, pp 61–69

    Google Scholar 

  • Graybeal BA (2007) Compressive behavior of ultra-high-performance fiber-reinforced concrete. ACI Mater J 104:146

    Google Scholar 

  • Huang L, Jiang W, Wang Y, Zhu Y, Afzal M (2022) Prediction of long-term compressive strength of concrete with admixtures using hybrid swarm-based algorithms. Smart Struct Syst 29:433–444

    Google Scholar 

  • Kasperkiewicz J, Racz J, Dubrawski A (1995) HPC strength prediction using artificial neural network. J Comput Civ Eng 9:279–284

    Article  Google Scholar 

  • Khodadadi N, Snasel V, Mirjalili S (2022) Dynamic arithmetic optimization algorithm for truss optimization under natural frequency constraints. IEEE Access 10:16188–16208

    Article  Google Scholar 

  • Liang H, Song W (2009) Improved estimation in multiple linear regression models with measurement error and general constraint. J Multivar Anal 100:726–741

    Article  MathSciNet  MATH  Google Scholar 

  • Maity R, Bhagwat PP, Bhatnagar A (2010) Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrol Process an Int J 24:917–923

    Article  Google Scholar 

  • Masoumi F, Najjar-Ghabel S, Safarzadeh A, Sadaghat B (2020) Automatic calibration of the groundwater simulation model with high parameter dimensionality using sequential uncertainty fitting approach. Water Supply 20:3487–3501. https://doi.org/10.2166/ws.2020.241

    Article  Google Scholar 

  • Mattei NJ, Mehrabadi MM, Zhu H (2007) A micromechanical constitutive model for the behavior of concrete. Mech Mater 39:357–379

    Article  Google Scholar 

  • Mukherjee S, Osuna E, Girosi F (1997) Nonlinear prediction of chaotic time series using support vector machines. Neural Networks Signal Process. VII. Proc. 1997 IEEE Signal Process. Soc. Work., IEEE; pp 511–520

  • Samui P (2008) Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Geotech 35:419–427

    Article  MATH  Google Scholar 

  • Shafieifar M, Farzad M, Azizinamini A (2018) A comparison of existing analytical methods to predict the flexural capacity of Ultra High Performance Concrete (UHPC) beams. Constr Build Mater 172:10–18

    Article  Google Scholar 

  • Shi X, Dong Y (2011) Support vector machine applied to prediction strength of cement. 2011 2nd Int. Conf. Artif. Intell. Manag. Sci. Electron. Commer., IEEE; 2011, pp 1585–1588

  • Vapnik V (1999) The nature of statistical learning theory. Springer science & business media

    MATH  Google Scholar 

  • Vapnik V (2013) The nature of statistical learning theory. Springer science & business media

    MATH  Google Scholar 

  • Vapnik V, Golowich S, Smola A (1996) Support vector method for function approximation, regression estimation and signal processing. Adv Neural Inf Proc Syst 9:1–7

    Google Scholar 

  • Wille K, Naaman AE, El-Tawil S, Parra-Montesinos GJ (2012) Ultra-high performance concrete and fiber reinforced concrete: achieving strength and ductility without heat curing. Mater Struct 45:309–324

    Article  Google Scholar 

  • Wu M (2023) Using the two optimization algorithms (BBO and FDA) coupling with radial basis neural network to estimate the compressive strength of high-ultra-performance concrete. J Intell Fuzzy Syst 44(1):827–837

    Article  Google Scholar 

  • Ye Q, Huang Q, Gao W, Zhao D (2005) Fast and robust text detection in images and video frames. Image vis Comput 23:565–576

    Article  Google Scholar 

  • Yin H, Liu S, Lu S, Nie W, Jia B (2021) Prediction of the compressive and tensile strength of HPC concrete with fly ash and micro-silica using hybrid algorithms. Adv Concr Constr 12:339–354

    Google Scholar 

  • 이종재, 김두기, 장성규, 이장호 (2007) Application of support vector regression for the prediction of concrete strength. Comput Concr An Int J 4:299–316

Download references

Author information

Authors and Affiliations

  1. College of Intelligent Manufacturing and Automobile, Chongqing Vocational College of Transportation, Jiangjin, Chongqing, 402247, China

    Bing Liu

Authors
  1. Bing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

BL: writing—original draft preparation, conceptualization, supervision, project administration.

Corresponding author

Correspondence to Bing Liu.

Ethics declarations

Conflict of interest

The authors declare no competing of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, B. Estimating the ultra-high-performance concrete compressive strength with a machine learning model via meta-heuristic algorithms. Multiscale and Multidiscip. Model. Exp. and Des. (2023). https://doi.org/10.1007/s41939-023-00302-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41939-023-00302-5

Keywords

Search

Navigation