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Sensitivity analysis and prediction of erodibility of treated unsaturated soil modified with nanostructured fines of quarry dust using novel artificial neural network

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Abstract

Sensitivity and error analyses and machine-based prediction have been conducted on the erodibility response of erodible unsaturated soil (degree of saturation 60%) treated with local cement and modified with nanostructured quarry fines. The machine-based exercise has become necessary because of the incessant washing away of soil on erosion watersheds causing devastating gullies around the developing world and the need to propose model equations to study, design and proffer future solutions to this environmental problem. Also, in order to overcome complex experimental setup needed to repeatedly study erosion problems, there is also need to forecast model equations by employing variables that can easily be determined as predictors of the model. This work was aimed at the prediction of erodibility and generating a model equation using the ANN learning technique. The erodible soil was collected and classified as poorly graded, highly plastic and as an A–7–6 group. 121 datasets were generated from multiple experiments for the input parameters and deployed in model training and testing in the ratio of 70 to 30%, respectively. The model performance was validated and error analysis was conducted using R2, MAE, MSE, RMSE and MAPE indices. The performance showed that the model has R2 of more than 0.95 in both training and testing between the predicted and measured values. Also, the error indices showed significantly small values, which showed good performance. Finally, the sensitivity analysis outcome showed that the liquid limit was the most influential on the erodibility model results. Generally, ANN technique has shown to be very flexible in forecasting civil engineering problems and fundamentally in proposing model equations.

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Data availability

The data supporting the results of this research work have been presented in the manuscript.

Abbreviations

Er (K):

Erodibility

[Er]max :

Maximum values of predicted erodibility

[Er]min :

Minimum values of predicted erodibility

ANN:

Artificial neural network

GP:

Genetic programming

GEP:

Gene expression programming

EPR:

Evolutionary polynomial regression

ANFIS:

Adaptive neuro-fuzzy inference system

NQF:

Nanostructured quarry fines

ANNS:

Agile neural network software

SA:

Sensitivity analysis

EA:

Error analysis

r:

Correlation coefficient

R2:

Coefficient of determination

MSE:

Mean square error

MAE:

Mean absolute error

RMSE:

Root mean square error

MAPE:

Mean absolute percentage error

MATLAB:

Matrix laboratory

MFNN:

Multilayer feed-forward neural network

CDW:

Construction and demolition waste

HC:

Hybrid cement

CL:

Clay content

Ac :

Clay activity

Cc :

Coefficient of curvature

Cu :

Coefficient of uniformity

\(\delta\) max :

Maximum dry density

Wmax :

Optimum moisture content

\(\delta\) part :

Partial maximum dry density

WL :

Liquid limit

Ip :

Plasticity index

Nc :

Cohesion

\(\varnothing ^\circ\) :

Frictional angle

\(\gamma\) unsat :

Unsaturated unit weight

Doc :

Degree of compaction

References

  1. Onyelowe KC, Van Bui D, Ikpemo OC, Ubachukwu OA, Van Nguyen M (2018) Assessment of rainstorm induced sediment deposition, gully development at Ikot Ekpene, Nigeria and the devastating effect on the environment. Environ Technol Innov 10:194–207. https://doi.org/10.1016/j.eti.2018.02.008

    Article  Google Scholar 

  2. Onyelowe KC (2017) The menace of the Geo-Environmental hazard caused by gully erosion in Abia State, Nigeria. Environmental Technology & Innovation 8; 343–348.www.elsevier.com/locate/eti. https://doi.org/10.1016/j.eti.2017.08.006.

  3. NEWMAP (2017). Abia State Nigeria Erosion and Watershed Management Project, Volume 11, GIS Mapping of Abia State Erosion Watershed. ABS/NEWMAP/QCBS/CON/16/01.

  4. Behera RN, Patra CR (2018) Ultimate bearing capacity prediction of eccentrically inclined loaded strip footings. Geotech Geol Eng 36(2018):3029–3080

    Article  Google Scholar 

  5. Dutta RK, Dutta K, Jeevanandham S (2015) Prediction of deviator stress of sand reinforced with waste plastic strips using neural network. Int J Geosynth Ground Eng 1(2):1–12

    Article  Google Scholar 

  6. Gnananandarao T, Khatri VN, Dutta RK (2020) Prediction of bearing capacity of H shaped skirted footings on sand using soft computing techniques. Archive Mater Sci Eng 103(2):62–74

    Google Scholar 

  7. Dutta RK, Gnananandarao T, Ladol S (2020) Soft computing based prediction of friction angle of clay. Archive Mater Sci Eng 104(2):58–68. https://doi.org/10.5604/01.3001.0014.4895

    Article  Google Scholar 

  8. Onyelowe KC, Iqbal M, Jalal F, Onyia M, Onuoha I (2021) Application of 3 algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil. Multiscale Multidiscip Model Exp Des. https://doi.org/10.1007/s41939-021-00093-7

    Article  Google Scholar 

  9. Onwuka DO, Awodiji TGC (2013) Artificial neural network for the modulus of rupture of concrete. Adv Appl Sci Res 4(4):214–223

    Google Scholar 

  10. Khan SU, Ayub T, Rafeeqi SFA (2013) Prediction of compressive strength of plain concrete confined with ferro-cement using artificial neural network (ANN) and comparison with existing mathematical models. Am J Civil Eng Archit 1(1):7–14. https://doi.org/10.12691/ajcea-1-1-2

    Article  Google Scholar 

  11. Das S, Pal P, Singh RM (2015) Prediction of concrete mix proportion using ANN technique. Int Res J Eng Technol 2(5):820–825

    Google Scholar 

  12. Panagiotis GA, Ioannis A, Liborio C, Hugo R, Humberto V, Job T, Paulo BL (2019) Masonry compressive strength prediction using artificial neural networks. TMM_CH. https://doi.org/10.1007/978-3-030-12960-6_14

    Article  Google Scholar 

  13. Rama MP, Rao HS (2012) Prediction of compressive strength of concrete with different aggregate binder ratio using ANN model. Int J Eng Res Technol 1(10):1–10

    Google Scholar 

  14. Ogbodo MC, Dumde DK (2017) Prediction of concrete strength using artificial neural network. Int J Adv Res Publ 1(6):74–77

    Google Scholar 

  15. Chandan MK, Raghu PB, Amarnath K (2017) Design of reinforced concrete structures using neural networks. Int Res J Eng Technol (IRJET) 4(7):2012–2018

    Google Scholar 

  16. Noorzaei J, Hakim SJS, Jaafar MS, Thanoon WAM (2007) Development of artificial neural network for prediction of compressive strength of concrete. Int J Eng Technol 4(2):141–153

    Google Scholar 

  17. Dantas ATA, Leite MB, Nagahama KJ (2013) Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Constr Build Mater 38:717–722

    Article  Google Scholar 

  18. Krishna AS, Rao VR (2019) Strength prediction of geopolymer concrete using ANN. Int J Recent Technol Eng (IJRTE) 7:661–667

    Google Scholar 

  19. Iyeke SD, Eze EO, Ehiorobo JO, Osuji SO (2016) Estimation of shear strength parameters of lateritic soils using artificial neural network. Niger J Technol (NIJOTECH) 35(2):260–269. https://doi.org/10.4314/njt.v35i2.5

    Article  Google Scholar 

  20. Sharmila S, Lekha G, Kaushik S (2016) Cost and time effective prediction of soil characteristics using ANN model. Int J Innov Res Sci Eng Technol 5(3):3829–3834. https://doi.org/10.15680/IJIRSET.2016.0503087

    Article  Google Scholar 

  21. Sharad KJ, Singh VP, Genuchten M (2004) Analysis of soil water retention data using artificial neural networks. J Hydrol Eng 9(5):415–420. https://doi.org/10.1061/~ASCE1084-0699~20049:5~415

    Article  Google Scholar 

  22. Sarmadian S, Mehrjardi RJ (2010) Development of pedotransfer functions to predict soil hydraulic properties in golestan province, Iran. 19th World Congress of Soil Science Soil Solutions for a Changing World, 59–62.

  23. Kumar VP, Rani SC (2004) Prediction of compression index of soils using artificial neural networks (ANNs). Int J Eng Res Appl (IJERA) 1(4):1554–1558

    Google Scholar 

  24. Kuo YL, Jaksa MB, Lyamin AV, Kaggwa WS (2009) ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil. Comput Geotech 36:503–516

    Article  Google Scholar 

  25. Kurnaz TF, Dagdeviren U, Yildiz M, Ozkan O (2016) Prediction of compressibility parameters of the soils using artificial neural network. Springer Plus 5:1–11. https://doi.org/10.1186/s40064-016-3494-5

    Article  Google Scholar 

  26. Onyelowe KC, Van Bui D, Ubachukwu O, Ezugwu C, Salahudeen B, Van Nguyen M, Ikeagwuani C, Amhadi T, Sosa F, Wu W, Duc Thinh Ta, Eberemu A, Duc Tho Pham, Barah O, Ikpa C, Orji F, Alaneme G, Amanamba E, Ugwuanyi H, Sai Vishnu, Kadurumba C, Selvakumar S, Ugorji B (2019) Recycling and reuse of solid wastes; a hub for eco-friendly, 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 

  27. American Standard for Testing and Materials (ASTM) C618 (1978) Specification for Pozzolanas. ASTM International, Philadelphia, USA

  28. BS 1377–2, 3 (1990) Methods of Testing Soils for Civil Engineering Purposes. British Standard Institute, London

  29. A Standard for Testing and Materials (ASTM) E1621–13 (2013) Standard guide for elemental analysis by wavelength dispersion x-ray fluorescence spectrometry. ASTM International, West Conshohocken, PA. https://doi.org/10.1520/E1621-13

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

  31. Garson GD (1991) Interpreting neural-network connection weights. AI Expert 6(4):46–51

  32. Olden JD, Jackson DA (2002) Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecol Model 154:135–150

  33. Gnananandarao T, Dutta RK, Khatri VN (2019) Application of artificial neural network to predict the settlement of shallow foundations on cohesionless soils. Geotech Appl Lect Notes Civil Eng 13(2019):51–58. https://doi.org/10.1007/978-981-13-0368-5_6

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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 Mechanicsal Engineering, Kampala International University, Kampala, Uganda

    Kennedy C. Onyelowe

  3. Department of Civil Engineering, Aditya College of Engineering & Technology, Aditya Nagar, Surampalem, Andhra Pradesh, 533437, India

    Tammineni Gnananandarao

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

    Chidobere Nwa-David

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  1. Kennedy C. Onyelowe
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Onyelowe, K.C., Gnananandarao, T. & Nwa-David, C. Sensitivity analysis and prediction of erodibility of treated unsaturated soil modified with nanostructured fines of quarry dust using novel artificial neural network. Nanotechnol. Environ. Eng. 6, 37 (2021). https://doi.org/10.1007/s41204-021-00131-2

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