On the Multiple Linear Regression and Artificial Neural Networks for Strength Prediction of Soil-Based Controlled Low-Strength Material


Article Preview

This paper presents two approaches, multiple linear regression (MLR) and artificial neural network (ANN), to develop predictive models for unconfined compressive strength of soil-based controlled low-strength material (CLSM). Our obtained laboratory data conducting on the soil-based CLSM were employed for analysis. Two strength prediction models were proposed: (1) strength is assumed to be a function of mix proportion and curing period; and (2) it is estimated from measured ultrasonic pulse velocity combined with effect of mixture parameters and curing ages. In each model, three predicted formulas were developed; one from MLR and two from ANN. It was showed that all the proposed equations have a well-predicted capacity.



Edited by:

Keishi Matsuda, P.S. Pa and Wiseroad Yun




L. J. Huang et al., "On the Multiple Linear Regression and Artificial Neural Networks for Strength Prediction of Soil-Based Controlled Low-Strength Material", Applied Mechanics and Materials, Vol. 597, pp. 349-352, 2014

Online since:

July 2014




* - Corresponding Author

[1] ACI-229R. Controlled-low strength materials (reproved 2005). Farmington Hills (MI)(2005).

[2] Lachemi M, Şahmaran M, Hossain KMA, Lotfy A, Shehata M. Properties of controlled low-strength materials incorporating cement kiln dust and slag. Cement and Concrete Composites. 2010; 32(8): 623-9.

DOI: https://doi.org/10.1016/j.cemconcomp.2010.07.011

[3] Finney AJ, Shorey EF, Anderson J. Use of native soil in place of aggregate in controlled low strength material (CLSM). International Pipelines Conference 2008. Atlanta, Georgia, United States2008. pp.1-13.

DOI: https://doi.org/10.1061/40994(321)124

[4] Howard A, Gaughan M, Hattan S, Wilkerson M. Lean, Green, and Mean: The IPL Project. ICSDEC 2012: American Society of Civil Engineers; 2012. pp.359-66.

DOI: https://doi.org/10.1061/9780784412688.043

[5] ASTM: D2487. Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System). (2006).

[6] ASTM: D4832. Standard Test Method for Preparation and Testing of Controlled Low Strength Material (CLSM) Test Cylinders. (2002).

DOI: https://doi.org/10.1520/d4832-10

[7] ASTM: C597. Test for Pulse Velocity Through Concrete. (2009).

[8] Trtnik G, Kavčič F, Turk G. Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics. 2009; 49(1): 53-60.

DOI: https://doi.org/10.1016/j.ultras.2008.05.001

[9] Yilmaz I, Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications. 2011; 38(5): 5958-66.

DOI: https://doi.org/10.1016/j.eswa.2010.11.027

Fetching data from Crossref.
This may take some time to load.