Abstract
With the recent emphasis on large-scale civil engineering constructions, artificial intelligence in the construction activities has received importance. Compressibility behavior is an important property in fine soils to find out the settlements in foundation designs. However, compression index (Cc) from one-dimensional swell-consolidation test is time consuming and laborious. Many traditional prediction-stimulated models rely on simplified assumptions, leading to inaccurate Cc estimations. This paper explores, by comparison, the application of invasive weed optimization (IWO) algorithm and particle swarm optimization (PSO) to predict Cc via multiple linear regression models. The predicted model equations have been developed, uses four input parameters namely plasticity index, free swell index, rate of heave and swell potential in both methods. The results confirm that the developed models using IWO provides accurate prediction than standard particle swarm optimization (PSO) algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chen, F.H.: Foundations on Expansive Soils. Elsevier Scientific Publishing Co., Amsterdam (1988)
Terzaghi, K., Peck, R.B., Mesri, G.: Soil Mechanics in Engineering Practice. Wiley, New York (1967)
Phanikumar, B.R., Nagaraju, T.V.: Swell compressibility characteristics of expansive clay lumps and powders blended with GGBS—a comparison. Indian Geotech. J. 1–9, ISSN 2277-3347 (2018)
Azzouz, A.S., Krizek, R.J., Corotis, R.B.: Regression analysis of soil compressibility. Soils and Foundations. Jpn. Soc. Soil Mech. Found. Eng., 16(2), 19–29 (1976)
Bowles, J.E.: Physical and Geotechnical Properties of Soils. McGraw-Hill Companies, New York (1989)
Kalantary, F., Kordnaeij, A.: Prediction of compression index using artificial neural network. Sci. Res. Essays 7(31), 2835–2848 (2012)
Onyejekwe, S., Kang, X., Ge, L.: Assessment of empirical equations for the compression index of fine grained soils in Missouri. Bull. Eng. Geol. Environ. (2014). https://doi.org/10.1007/s10064-014-0659-8
Puri, N., Prasad, H.D., Jain, A.: Prediction of geotechnical parameters using meachine learning techniques. Procedia Comput. Sci. 125, 509–517 (2018)
Jyoti, S.T., Sandeep, N., Chakradhar, I.: Optimum utilization of fly ash for stabilization of sub grade soil using Genetic algorithm. Procedia Eng. 51, 250–258 (2013)
Taskiran, T.: Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv. Eng. Softw. 41, 886–892 (2010)
Yilmaz, I., Kaynar, O.: Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst. Appl. 38(5), 5958–5966 (2011)
Zhu, A.X., Qi, F., Moore, A., Burt, J.E.: Prediction of soil properties using fuzzy membership values. Geoderma 158(3–4), 199–206 (2010)
Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: Data of Plasticity and Swelling Characteristics of Lime-Blended Expansive Clays
Appendix: Data of Plasticity and Swelling Characteristics of Lime-Blended Expansive Clays
Soil location | Properties | Lime content | ||||
|---|---|---|---|---|---|---|
0% | 1% | 2% | 3% | 4% | ||
Amalapuram (AM) | Liquid limit (%) | 83 | 77 | 75 | 73 | 71 |
Plastic limit (%) | 28 | 29 | 30.5 | 32 | 33 | |
Plasticity index (%) | 55 | 48 | 44.5 | 41 | 38 | |
Free swell index (%) | 189 | 128 | 88.4 | 72 | 63.6 | |
Rate of heave (mm) | 4.01 | 2.88 | 2.81 | 2.76 | 2.75 | |
Swell potential | 20 | 14.4 | 14 | 13.8 | 13.75 | |
Compression index | 0.608 | 0.482 | 0.48 | 0.37 | 0.37 | |
Bhimavaram (BM) | Liquid limit (%) | 72 | 70 | 67.5 | 65 | 64.3 |
Plastic limit (%) | 29 | 31 | 32.3 | 34 | 35 | |
Plasticity index (%) | 43 | 39 | 35.2 | 31 | 29.3 | |
Free swell index (%) | 145 | 95 | 88.5 | 56 | 41 | |
Rate of heave (mm) | 1.88 | 1.65 | 1.58 | 1.54 | 1.57 | |
Swell potential | 9.4 | 8.25 | 7.9 | 7.7 | 7.85 | |
Compression index | 0.587 | 0.54 | 0.51 | 0.48 | 0.33 | |
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vamsi Nagaraju, T., Durga Prasad, C., Murthy, N.G.K. (2020). Invasive Weed Optimization Algorithm for Prediction of Compression Index of Lime-Treated Expansive Clays. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_28
Download citation
DOI: https://doi.org/10.1007/978-981-15-0184-5_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0183-8
Online ISBN: 978-981-15-0184-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)