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Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil

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Abstract

Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg–Muarquardt Backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil’s strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (wL), (plastic limit (wP), plasticity index (IP), optimum moisture content (wOMC), clay activity (AC), and (maximum dry density (δmax). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models’ accuracy.

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References

  • Abdi Y, Momeni E, Khabir RR (2020) A reliable PSO-based ANN approach for predicting unconfined compressive strength of sandstones. Open Construction Building Technol J 2020 14: 237–249. DOI: https://doi.org/10.2174/1874836802014010237

  • Adler J (2010) Parmryd J (2010) Quantifying colocalization by correlation: pearson correlation coeeficient is superior to the Mander, s overlap coefficient. Cytometry A 77(8):733–742

    Article  Google Scholar 

  • Alaneme GU, Onyelowe KC, Onyia ME, Bui Van D, Mbadike EM, Ezugwu CN, Dimonyeka MU, Attah IC, Ogbonna C, Abel C, Ikpa CC, Udousoro IM (2020) Modeling volume change properties of hydrated-lime activated rice husk ash (HARHA) modified soft soil for construction purposes by artificial neural network (ANN). Umudike J Eng Technol (UJET) 6(1):1–12. https://doi.org/https://doi.org/10.33922/j.ujet_v6i1_9

  • Babanajad SK, Gandomi AH, Alavi AH (2017) New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach. Adv Eng Softw 2017(110):55–68

    Article  Google Scholar 

  • Benesty J et al. (2009) Pearson correlation coefficient, in Noise reduction in speech proceeding, 2009, Springer, p. 1–4

  • Benesty J, Chen J, Huang Y (2008) On the importance of the Pearson correlation coefficient in noise reduction. IEEE Trans Audio Speech Language Proc 16(4):757–765

    Article  Google Scholar 

  • BS 1377 - 2, 3, 1990. Methods of Testing Soils for Civil Engineering Purposes, British Standard Institute, London

  • BS 5930, (2015). Methods of Soil Description, British Standard Institute, London

  • BS 1924, (1990). Methods of Tests for Stabilized Soil, British Standard Institute, London

  • Erzin Y, Turkoz D (2016) Use of neural networks for the prediction of the CBR value of some Aegean sands. Neural Comput Applic 27:1415–1426. https://doi.org/10.1007/s00521-015-1943-7

    Article  Google Scholar 

  • Fan X et al. (2002). An evaluation model of supply chain performances using 5DBSC and LMBP neural network algorithm

  • Ferentinou M, Fakir M (2017) An ANN approach for the prediction of uniaxial compressive strength, of some sedimentary and Igneous Rocks in Eastern KwaZulu-Natal. Symp Int Soc Rock Mech Proc Eng 191(2017):1117–1125. https://doi.org/10.1016/j.proeng.2017.05.286

    Article  Google Scholar 

  • Hosseini M, Naeini SARM, Dehghani AA, Zeraatpisheh M (2018) Modeling of soil mechanical resistance using intelligentmethods. J Soil Sci Plant Nutr 18(4):939–951

    Google Scholar 

  • Iqbal MF et al (2020) Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. J Hazard Mater 2020(384):121322

    Article  Google Scholar 

  • Kingston GB, Maier HR, Lambert MF (2016) A Bayesian approach to artificial neural network model selection. Centre Appl Model Water Eng School Civ Environ Eng Univ Adelaide Bull 6(2016):1853–1859

    Google Scholar 

  • Kisi O, Uncuoglu E (2005) Comparison of three back-propagation training algorithms for two case studies. Indian J Eng Materials Sci 12(2005):434–442

    Google Scholar 

  • Nawi NM, Khan A, Rehman MZ, (2013) A new levenberg marquardt based back propagation algorithm trained with cuckoo search. In: The 4th international conference on electrical engineering and informatics (ICEEI 2013), Procedia Technology 11 (2013): p. 18 – 23. https://doi.org/https://doi.org/10.1016/j.protcy.2013.12.157

  • Onyelowe KC, Van Bui D, Ubachukwu O et al (2019) Recycling and reuse of solid wastes; a hub for ecofriendly, ecoefficient and sustainable soil, concrete, wastewater and pavement reengineering. Int J Low-Carbon Technol 14(3):440–451. https://doi.org/10.1093/Ijlct/Ctz028

    Article  Google Scholar 

  • Onyelowe KC, Onyia ME, Onukwugha ER, Nnadi OC, Onuoha IC, Jalal FE (2020) Polynomial relationship of compaction properties of silicate-based RHA modified expansive soil for pavement subgrade purposes Epitőanyag—J Silicate Based Composite Materials 72(6):223–228. https://doi.org/https://doi.org/10.14382/epitoanyag-jsbcm.2020.36

  • Onyelowe KC, Onyia M, Onukwugha ER, Bui Van D, Obimba-Wogu J, Ikpa C (2020) Mechanical properties of fly ash modified asphalt treated with crushed waste glasses as fillers for sustainable pavements. Epitőanyag–Journal of Silicate Based and Composite Materials 72(6):219–222. https://doi.org/https://doi.org/10.14382/epitoanyag-jsbcm.2020.35

  • Onyelowe KC, Alaneme GU, Onyia ME, Bui Van D, Diomonyeka MU, Nnadi E, Ogbonna C, Odum LO, Aju DE, Abel C, Udousoro IM, Onukwugha E (2021) Comparative modeling of strength properties of hydrated-lime activated rice-husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (ANN) and fuzzy logic (FL). Jurnal Kejuruteraan 33(2)

  • Quan S, Sun P, Wu G, Hu J (2015) One bayesian network construction algorithm based on dimensionality reduction. In: 5th international conference on computer sciences and automation engineering (ICCSAE 2015), Atlantis Publishers, p. 222–229

  • Rezaei K, Guest B, Friedrich A, Fayazi F, Nakhaei M, Beitollahi A et al (2009) Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam. Iran Acta Geophys 2009(57):271–293. https://doi.org/10.2478/s11600-008-0073-3

    Article  Google Scholar 

  • Salahudeen AB, Sadeeq JA, Badamasi A, Onyelowe KC (2020) Prediction of unconfined compressive strength of treated expansive clay using back-propagation artificial neural networks. Nigerian Journal of Engineering, Faculty of Engineering Ahmadu Bello University Samaru - Zaria, Nigeria. Vol. 27, No. 1, April 2020. ISSN: 0794 – 4756. Pp. 45 – 58

  • Saldaña M, Pérez-Rey JGI, Jeldres M, Toro N (2020) Applying statistical analysis and machine learning for modeling the UCS from P-Wave velocity, density and porosity on dry travertine. Appl Sci 10:4565. https://doi.org/10.3390/app10134565

    Article  Google Scholar 

  • Sariev E, Germano G (2019). Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 20: 2, 311–328, doi: https://doi.org/10.1080/14697688.2019.1633014

  • Shi BH, Zhu XF (2008) On improved algorithm of LMBP neural networks. Control Eng China 2008(2):016

    MathSciNet  Google Scholar 

  • Van B, Duc and Onyelowe, K.C. (2018) Adsorbed complex and laboratory geotechnics of Quarry Dust (QD) stabilized lateritic soils. Environ Technol Innovation 10:355–368. https://doi.org/10.1016/j.eti.2018.04.005

    Article  Google Scholar 

  • Van Bui D, Onyelowe KC, Van Nguyen M (2018) Capillary rise, suction (absorption) and the strength development of HBM treated with QD base Geopolymer. Int J Pavement Res Technol [in press]. https://doi.org/10.1016/j.ijprt.2018.04.003

    Article  Google Scholar 

  • Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30(1):79–82

    Article  Google Scholar 

  • Willmott CJ, Matsuura K, Robeson SM (2009) Ambiguities inherent in sums-of-squares-based error statisitics. Atmosp Environ 43(3):749–752

    Article  Google Scholar 

  • Zhan Z, Fu Y, Yang RJ et al. (2012) A Bayesian inference based model interpolation and extrapolation. SAE Int J Materials Manuf 5(2). Doi: https://doi.org/10.4271/2012-01-0223

Download references

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Authors and Affiliations

  1. Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria

    Kennedy C. Onyelowe

  2. Department of Civil and Mechanical Engineering, Kampala International University, Kampala, Uganda

    Kennedy C. Onyelowe

  3. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shangai Jiao Tong University, Shanghai, China

    Mudassir Iqbal

  4. Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai, China

    Fazal E. Jalal

  5. Department of Civil Engineering, Faculty of Engineering, University of Nigeria, Nsukka, Nigeria

    Michael E. Onyia

  6. Department of Environmental Technology, Federal University of Technology, Owerri, Nigeria

    Ifeanyichukwu C. Onuoha

Authors
  1. Kennedy C. Onyelowe
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  2. Mudassir Iqbal
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  3. Fazal E. Jalal
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  4. Michael E. Onyia
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  5. Ifeanyichukwu C. Onuoha
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Corresponding author

Correspondence to Kennedy C. Onyelowe.

Appendix

Appendix

See Appendix Table 8.

Table 8 121 datasets of input and output parameters
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Onyelowe, K.C., Iqbal, M., Jalal, F.E. et al. Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil. Multiscale and Multidiscip. Model. Exp. and Des. 4, 259–274 (2021). https://doi.org/10.1007/s41939-021-00093-7

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  • DOI: https://doi.org/10.1007/s41939-021-00093-7

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