Skip to main content

Advertisement

SpringerLink
Log in

Application of the optimal regression-based analysis to estimate the deformation of geogrid-reinforced soil structures

  • Original Paper
  • Published:
Multiscale and Multidisciplinary Modeling, Experiments and Design Aims and scope Submit manuscript

Abstract

The analysis of deformation plays a crucial role in the design of Geosynthetic reinforced structure (GRS) constructions. However, the publications emphasize the potential of artificial prediction algorithms in addressing geotechnical engineering challenges. The main aim of the present study is to examine the possible employ of estimation methods in the prediction of the deformation (Dis) of GRS. The present paper shows and approve a new technique which integrates the Dwarf Mongoose Optimizer (DMO) framework with the Multi-layered perceptron (MLP) neural network and Support Vector Regression (SVR) (abbreviated as SVRDMO and MLPDMO). Afterwards, a whole of 166 finite element values performed in the publications were utilized so as to make the data collection. Based on the results obtained, it can be concluded that both SVRDMO and MLPDMO have considerable potential in properly forecasting the Dis. The R2 values for SVRDMO were 0.9835 throughout training and 0.9866 throughout testing. After careful examination of several types of performance tests and their comparison to valid publications, it has been determined which the SVRDMO offers a more suitable framework for calculating the Dis of GRS.

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

Similar content being viewed by others

Explore related subjects

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

Availability of data and materials

Data can be shared upon request.

References

  • Aghayari Hir M, Zaheri M, Rahimzadeh N (2022) Prediction of rural travel demand by spatial regression and artificial neural network methods (Tabriz County). J Transp Res. 20(4):367–386

    Google Scholar 

  • Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570

    MathSciNet  Google Scholar 

  • Al-hnaity B, Abbod M, Alarraj M (2015) Predicting FTSE 100 close price using hybrid model. In: 2015 SAI Intelligent Systems Conference (IntelliSys). IEEE. p 49–54

  • Alias R, Kasa A, Matlan SJ (2017) Comparison of ANN and ANFIS models for stability prediction of cantilever reinforced concrete retaining walls. Int J Eng Adv Technol 7:165–167

    Google Scholar 

  • Armaghani DJ, Faizi K, Hajihassani M, Mohamad ET, Nazir R (2015) Effects of soil reinforcement on uplift resistance of buried pipeline. Measurement 64:57–63

    Google Scholar 

  • Armaghani DJ, Koopialipoor M, Marto A, Yagiz S (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J Rock Mech Geotech Eng 11:779–789

    Google Scholar 

  • Basudhar PK, Vashistha A, Deb K, Dey A (2008) Cost optimization of reinforced earth walls. Geotech Geol Eng 26:1–12

    Google Scholar 

  • Benemaran RS, Esmaeili-Falak M, Kordlar MS (2023) improvement of recycled aggregate concrete using glass fiber and silica fume, multiscale and multidisciplinary Modeling. Experiments and Design. p 1–20.

  • Bilgin Ö, Kim H (2010) Effect of soil properties and reinforcement length on mechanically stabilized earth wall deformations. In: Earth Retention Conference 3. p 556–563.

  • Chen H, Asteris PG, Jahed Armaghani D, Gordan B, Pham BT (2019) Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models. Appl Sci. 9:1042

    Google Scholar 

  • Chew SH, Schmertmann GR, Mitchell JK (1991) Pl/4 Reinforced soil wall deformations by finite element method. Performance of reinforced soil structures. Thomas Telford Publishing, London, pp 35–40

    Google Scholar 

  • Chou J-S, Yang K-H, Pampang JP, Pham A-D (2015) Evolutionary metaheuristic intelligence to simulate tensile loads in reinforcement for geosynthetic-reinforced soil structures. Comput Geotech 66:1–15

    Google Scholar 

  • Collin JG (1996) Design manual for segmental retaining walls, National Concrete Masonry Association.

  • Dawei Y, Bing Z, Bingbing G, Xibo G, Razzaghzadeh B (2023) Predicting the CPT-based pile set-up parameters using HHO-RF and PSO-RF hybrid models. Struct Eng Mech 86:673–686

    Google Scholar 

  • Esmaeili-Falak M, Benemaran RS (2023) Ensemble deep learning-based models to predict the resilient modulus of modified base materials subjected to wet-dry cycles. Geomech Eng 32(6):583

    Google Scholar 

  • Esmaeili-Falak M, Sarkhani Benemaran R (2024) Application of optimization-based regression analysis for evaluation of frost durability of recycled aggregate concrete. Struct Concr 25(1):716–737. https://doi.org/10.1002/suco.202300566

    Article  Google Scholar 

  • Esmaeili-Falak M, Katebi H, Vadiati M, Adamowski J (2019) Predicting triaxial compressive strength and Young’s modulus of frozen sand using artificial intelligence methods. J Cold Reg Eng 33:4019007. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000188

    Article  Google Scholar 

  • Gandomi AH, Kashani AR, Roke DA, Mousavi M (2017) Optimization of retaining wall design using evolutionary algorithms. Struct Multidiscip Optim 55:809–825

    Google Scholar 

  • Han J, Leshchinsky D (2006) General analytical framework for design of flexible reinforced earth structures. J Geotech Geoenviron Eng 132:1427–1435

    Google Scholar 

  • Han J, Leshchinsky D (2010) Analysis of back-to-back mechanically stabilized earth walls. Geotext Geomembr 28:262–267

    Google Scholar 

  • Hassankhani E, Esmaeili-Falak M (2024) Soil-structure interaction for buried conduits influenced by the coupled effect of the protective layer and trench installation. J Pipeline Syst Eng Pract 15(2):04024012. https://doi.org/10.1061/JPSEA2.PSENG-1547

    Article  Google Scholar 

  • Hatami K, Bathurst RJ (2005) Development and verification of a numerical model for the analysis of geosynthetic-reinforced soil segmental walls under working stress conditions. Can Geotech J 42:1066–1085

    Google Scholar 

  • Hatami K, Bathurst RJ (2006) Numerical model for reinforced soil segmental walls under surcharge loading. J Geotech Geoenviron Eng 132:673–684

    Google Scholar 

  • B.S. Institutions (1995) BS8006, code of practice for strengthened/reinforced soils and other fills, british standards.

  • Karballaeezadeh N, Zaremotekhases F, Shamshirband S, Mosavi A, Nabipour N, Csiba P, Várkonyi-Kóczy AR (2020) Intelligent road inspection with advanced machine learning; hybrid prediction models for smart mobility and transportation maintenance systems. Energies (Basel) 13:1718

    Google Scholar 

  • Kashani AR, Saneirad A, Gandomi AH (2020) Optimum design of reinforced earth walls using evolutionary optimization algorithms. Neural Comput Appl 32:12079–12102

    Google Scholar 

  • Koopialipoor M, Tootoonchi H, Marto A, Faizi K, Armaghani DJ (2018) Various effective factors on peak uplift resistance of pipelines in sand: a comparative study. Int J Geotech Eng. 14(7):820–827

    Google Scholar 

  • Lawson CR, Yee TW (2005) Reinforced soil retaining walls with constrained reinforced fill zones. In: Slopes and retaining structures under seismic and static conditions. ASCE, Austin, Texas, USA, pp 1–14

    Google Scholar 

  • Leshchinsky D, Hu Y, Han J (2004) Limited reinforced space in segmental retaining walls. Geotext Geomembr 22:543–553

    Google Scholar 

  • Li D, Zhang X, Kang Q, Tavakkol E (2023) Estimation of unconfined compressive strength of marine clay modified with recycled tiles using hybridized extreme gradient boosting method. Constr Build Mater 393:131992

    Google Scholar 

  • Liang R, Bayrami B (2023) Estimation of frost durability of recycled aggregate concrete by hybridized random forests algorithms. Steel Compos Struct 49:91–107

    Google Scholar 

  • Ling HI, Leshchinsky D (2003) Finite element parametric study of the behavior of segmental block reinforced-soil retaining walls. Geosynth Int 10:77–94

    Google Scholar 

  • Ling HI, Liu H, Mohri Y (2005) Parametric studies on the behavior of reinforced soil retaining walls under earthquake loading. J Eng Mech 131:1056–1065

    Google Scholar 

  • Liu C, Evett JB (1992) Soils and foundations. Prentice Hall International, Hoboken

    Google Scholar 

  • Maiti S, Tiwari RK (2014) A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction, Environ. Earth Sci 71:3147–3160

    Google Scholar 

  • Manahiloh KN, Nejad MM, Momeni MS (2015) Optimization of design parameters and cost of geosynthetic-reinforced earth walls using harmony search algorithm. Int J Geosynth Ground Eng 1:1–12

    Google Scholar 

  • Momeni E, Nazir R, Armaghani DJ, Sohaie H (2015) Bearing capacity of precast thin-walled foundation in sand. Proc Inst Civ Eng-Geotech Eng. 168:539–550

    Google Scholar 

  • Momeni E, Nazir R, Armaghani DJ, Mohamad ET (2015b) Prediction of unconfined compressive strength of rocks: a review paper. J Teknol 77:43–50

    Google Scholar 

  • Momeni E, Yarivand A, Dowlatshahi MB, Armaghani DJ (2021) An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures. Transp Geotech 26:100446

    Google Scholar 

  • Moradi G, Hassankhani E, Halabian AM (2020) Experimental and numerical analyses of buried box culverts in trenches using geofoam. Proc Inst Civ Eng-Geotech Eng. 1–12.

  • Motalleb Nejad M, Manahiloh KN (2015) A modified harmony search algorithm for the optimum design of earth walls reinforced with non-uniform geosynthetic layers. Int J Geosynth Ground Eng 1:1–15

    Google Scholar 

  • Nabipour N, Karballaeezadeh N, Dineva A, Mosavi A, Mohammadzadeh D, Shamshirband S (2019) Comparative analysis of machine learning models for prediction of remaining service life of flexible pavement. Mathematics 7:1198

    Google Scholar 

  • Nazir R, Momeni E, Marsono K, Sohaie H (2013) Precast spread foundation in industrialized building system, in: Proceedings of the 3rd International Conference on Geotechnique, Construction Materials and Environment, Nagoya, Japan. p 13–15.

  • Ozturk T (2014) Artificial neural networks approach for earthquake deformation determination of geosynthetic reinforced retaining walls. Int J Intell Syst Appl Eng 2:1–9

    Google Scholar 

  • Pierson MC, Parsons RL, Han J, Brennan JJ (2011) Laterally loaded shaft group capacities and deflections behind an MSE wall. J Geotech Geoenviron Eng 137:882–889

    Google Scholar 

  • Rezaei H, Nazir R, Momeni E (2016) Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study. J Zhejiang Univ-Sci A 4:273–285

    Google Scholar 

  • Sagiroglu S, Colak I, Bayindir R (2006) Power factor correction technique based on artificial neural networks. Energy Convers Manag 47:3204–3215

    Google Scholar 

  • Sarkhani Benemaran R (2023) Application of extreme gradient boosting method for evaluating the properties of episodic failure of borehole breakout. Geoenergy Sci Eng. https://doi.org/10.1016/j.geoen.2023.211837

    Article  Google Scholar 

  • Sarkhani Benemaran R, Esmaeili-Falak M (2023) Predicting the Young’s modulus of frozen sand using machine learning approaches: State-of-the-art review. Geomech Eng. 34:507–527

    Google Scholar 

  • Shi X, Yu X, Esmaeili-Falak M (2023) Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation. Compos Struct 306:116599. https://doi.org/10.1016/j.compstruct.2022.116599

    Article  Google Scholar 

  • Vapnik V (1999) The nature of statistical learning theory. Springer science & business media, New York

    Google Scholar 

  • Wartman J, Rondinel-Oviedo EA, Rodriguez-Marek A (2006) Performance and analyses of mechanically stabilized earth walls in the Tecoman, Mexico earthquake. J Perform Constr Facil 20:287–299

    Google Scholar 

  • Xu C, Gordan B, Koopialipoor M, Armaghani DJ, Tahir MM, Zhang X (2019) Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access 7:94692–94700

    Google Scholar 

  • Yalcin Y, Orhon M, Pekcan O (2019) An automated approach for the design of mechanically stabilized earth walls incorporating metaheuristic optimization algorithms. Appl Soft Comput 74:547–566

    Google Scholar 

  • Zhu Y, Huang L, Zhang Z, Bayrami B (2022) Estimation of splitting tensile strength of modified recycled aggregate concrete using hybrid algorithms. Steel Compos Struct. https://doi.org/10.12989/scs.2022.44.3.389

    Article  Google Scholar 

  • Zornberg JG, Leshchinsky D (2003) Comparison of international design criteria for geosynthetic-reinforced soil structures. Landmarks Earth Reinf 2:1095–1106

    Google Scholar 

Download references

Acknowledgements

Scientific research project of Sichuan Provincial Department of Education (16ZB0405) Research on Physical Property Indexes of Red Clay in South Sichuan; Research on Physical Property Indexes of Red Clay in South Sichuan (C122015012), funded by Young Scientists Fund of School of Engineering and Technology of Chengdu University of Technology.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Resource Exploration and Civil Engineering, The Engineering & Technical College of Chengdu University of Technology, Leshan, 614000, Sichuan, China

    Hongwei Ren

Authors
  1. Hongwei Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The author contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Hongwei Ren”.

Corresponding author

Correspondence to Hongwei Ren.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, H. Application of the optimal regression-based analysis to estimate the deformation of geogrid-reinforced soil structures. Multiscale and Multidiscip. Model. Exp. and Des. 7, 3695–3708 (2024). https://doi.org/10.1007/s41939-024-00446-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41939-024-00446-y

Keywords

Search

Navigation