Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

Hybrid machine learning for predicting strength of sustainable concrete

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Foamed concrete material is a sustainable material which is widely used in the construction industry due to their sustainability. Accurate prediction of their compressive strength is vital for structural design. However, empirical methods are limited to consider simultaneously all influencing factors in predicting the compressive strength of foamed concrete materials. Thus, this study proposed a novel hybrid artificial intelligence (AI) model which couples the least squares support vector regression (LSSVR) with the grey wolf optimization (GWO) to consider effectively the influencing factors and improve the predictive accuracy in predicting the foamed concrete’s compressive strength. Performance of the proposed model was evaluated using a real-world dataset. Comparison results confirm that the proposed GWO–LSSVR model was superior than the support vector regression, artificial neural networks, random forest, and M5Rules with the improvement rate of 144.2–284.0% in mean absolute percentage error (MAPE). Notably, the evaluation results show that the GWO–LSSVR model showed the good agreement between the actual and predicted values with the correlation coefficient of 0.991 and MAPE of 3.54%. Thus, the proposed AI model was suggested as an effective tool for designing foamed concrete materials.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abd AM, Abd SM (2017) Modelling the strength of lightweight foamed concrete using support vector machine (SVM). Case Stud Constr Mater 6:8–15

    Google Scholar 

  • Behnood A, Golafshani EM (2018) Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. J Clean Prod 202:54–64

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Machine Learning 45:5–32

    Article  Google Scholar 

  • Chou J-S, Ngo N-T (2016) Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns. Appl Energy 177:751–770

    Article  Google Scholar 

  • Chou J-S, Ngo N-T (2018) Engineering strength of fiber-reinforced soil estimated by swarm intelligence optimized regression system. Neural Comput Appl 30:2129–2144

    Article  Google Scholar 

  • Chou J-S, Ngo N-T, Chong WK (2017) The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate. Eng Appl Artif Intell 65:471–483

    Article  Google Scholar 

  • de Moura Oliveira PB, Freire H, Solteiro Pires EJJSC (2016) Grey wolf optimization for PID controller design with prescribed robustness margins. Soft Comput 20:4243–4255

    Article  Google Scholar 

  • Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  • Falliano D, De Domenico D, Ricciardi G, Gugliandolo E (2018) Experimental investigation on the compressive strength of foamed concrete: effect of curing conditions, cement type, foaming agent and dry density. Constr Build Mater 165:735–749

    Article  Google Scholar 

  • Hajimohammadi A, Ngo T, Mendis P (2018) Enhancing the strength of pre-made foams for foam concrete applications. Cem Concr Compos 87:164–171

    Article  Google Scholar 

  • Haykin S (1998) Neural networks: a comprehensive foundation. Prentice Hall PTR, Englewood Cliffs

    MATH  Google Scholar 

  • Hejazi M, Singh YP (2013) ONE-CLASS SUPPORT VECTOR MACHINES APPROACH TO ANOMALY DETECTION. Appl Artif Intell 27:351–366

    Article  Google Scholar 

  • Hejazi M, Al-Haddad SAR, Singh YP, Hashim SJ, Aziz AFA (2015) Multiclass support vector machines for classification of ECG data with missing values. Appl Artif Intell 29:660–674

    Article  Google Scholar 

  • Hilal AA, Thom NH, Dawson AR (2015) On void structure and strength of foamed concrete made without/with additives. Constr Build Mater 85:157–164

    Article  Google Scholar 

  • Jiang M, Jiang L, Jiang D, Xiong J, Shen J, Ahmed SH et al (2017) Dynamic measurement errors prediction for sensors based on firefly algorithm optimize support vector machine. Sustain Cities Soc 35:250–256

    Article  Google Scholar 

  • Kearsley EP, Wainwright PJ (2001) The effect of high fly ash content on the compressive strength of foamed concrete. Cem Concr Res 31:105–112

    Article  Google Scholar 

  • Kearsley EP, Wainwright PJ (2002) The effect of porosity on the strength of foamed concrete. Cem Concr Res 32:233–239

    Article  Google Scholar 

  • Khosravi A, Nunes RO, Assad MEH, Machado L (2018) Comparison of artificial intelligence methods in estimation of daily global solar radiation. J Clean Prod 194:342–358

    Article  Google Scholar 

  • Long W, Cai S, Jiao J, Tang MJSC (2019) An efficient and robust grey wolf optimizer algorithm for large-scale numerical optimization. Soft Comput 24:997–1026

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Mustafi D, Sahoo GJSC (2019) A hybrid approach using genetic algorithm and the differential evolution heuristic for enhanced initialization of the k-means algorithm with applications in text clustering. Soft Comput 23:6361–6378

    Article  Google Scholar 

  • Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1996) Applied linear statistical models, 4th edn. McGraw-Hill/Irwin, New York

    Google Scholar 

  • Nguyen TT, Bui HH, Ngo TD, Nguyen GD (2017) Experimental and numerical investigation of influence of air-voids on the compressive behaviour of foamed concrete. Mater Des 130:103–119

    Article  Google Scholar 

  • Pan Z, Hiromi F, Wee T (2007) Preparation of high performance foamed concrete from cement, sand and mineral admixtures. J Wuhan Univ Technol Mater Sci Ed 22:295–298

    Article  Google Scholar 

  • Qi C, Fourie A, Chen Q, Zhang Q (2018) A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill. J Clean Prod 183:566–578

    Article  Google Scholar 

  • Ramamurthy K, Kunhanandan Nambiar EK, Indu Siva Ranjani G (2009) A classification of studies on properties of foam concrete. Cem Concr Compos 31:388–396

    Article  Google Scholar 

  • SAS (2018) Artificial intelligence. https://www.sas.com/en_sg/insights/analytics/what-is-artificialintelligence

  • Sayadi AA, Tapia JV, Neitzert TR, Clifton GC (2016a) Effects of expanded polystyrene (EPS) particles on fire resistance, thermal conductivity and compressive strength of foamed concrete. Constr Build Mater 112:716–724

    Article  Google Scholar 

  • Sayadi AA, Juan Vilches T, Neitzert TR, Charles Clifton G (2016b) Effectiveness of foamed concrete density and locking patterns on bond strength of galvanized strip. Constr Build Mater 115:221–229

    Article  Google Scholar 

  • She W, Du Y, Miao C, Liu J, Zhao G, Jiang J et al (2018) Application of organic- and nanoparticle-modified foams in foamed concrete: reinforcement and stabilization mechanisms. Cem Concr Res 106:12–22

    Article  Google Scholar 

  • Suriavel Rao RS, Malathi PJSC (2019) A novel PTS: grey wolf optimizer-based PAPR reduction technique in OFDM scheme for high-speed wireless applications. Soft Comput 23:2701–2712

    Article  Google Scholar 

  • Suykens JAK, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Least squares support vector machines. World Scientific, Singapore

    Book  Google Scholar 

  • Tang T, Chen S, Zhao M, Huang W, Luo JJSC (2019) Very large-scale data classification based on K-means clustering and multi-kernel SVM. Soft Comput 23:3793–3801

    Article  Google Scholar 

  • Techopedia (2018) What does artificial intelligence (AI) mean? https://www.techopedia.com/definition/190/artificial-intelligence-ai

  • Teng Z-j, Lv J-l, L-wJSC Guo (2019) An improved hybrid grey wolf optimization algorithm. Soft Comput 23:6617–6631

    Article  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • Wang M, Chen H, Li H, Cai Z, Zhao X, Tong C et al (2017) Grey wolf optimization evolving kernel extreme learning machine: application to bankruptcy prediction. Eng Appl Artif Intell 63:54–68

    Article  Google Scholar 

  • Wikiversity (2018) Algorithm models/Grey Wolf optimizer. https://en.wikiversity.org/wiki/Algorithm_models/Grey_Wolf_Optimizer

  • Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H et al (2007) Top 10 algorithms in data mining. Knowl Inf Syst 14:1–37

    Article  Google Scholar 

  • Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S et al (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125

    Article  Google Scholar 

  • Zhao J, Lam KP (2012) Influential factors analysis on LEED building markets in U.S. East Coast cities by using Support Vector Regression. Sustain Cities Soc 5:37–43

    Article  Google Scholar 

  • Zhao Y-t, Li W-g, Liu AJSC (2019) Improved grey wolf optimization based on the two-stage search of hybrid CMA-ES. Soft Comput 24:1097–1115

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by The University of Danang, University of Science and Technology, the code number of the project: T2019-02-37.

Author information

Authors and Affiliations

  1. Faculty of Project Management, The University of Danang – University of Science and Technology, Danang City, Vietnam

    Anh-Duc Pham, Ngoc-Tri Ngo, Quang-Trung Nguyen & Ngoc-Son Truong

Authors
  1. Anh-Duc Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ngoc-Tri Ngo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quang-Trung Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ngoc-Son Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ngoc-Tri Ngo.

Ethics declarations

Conflict of interest

Ngoc-Tri Ngo declares that he has no conflict of interest. Quang-Trung Nguyen declares that he has no conflict of interest. Ngoc-Son Truong declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by V. Loia.

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pham, AD., Ngo, NT., Nguyen, QT. et al. Hybrid machine learning for predicting strength of sustainable concrete. Soft Comput 24, 14965–14980 (2020). https://doi.org/10.1007/s00500-020-04848-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04848-1

Keywords

Search

Navigation