Skip to main content
SpringerLink

Prediction and validation of geogrid tensile force distribution in back-to-back MSE walls under rail axle load: finite-element and intelligent techniques

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

In this study, an investigation was conducted to assess the performance of artificial intelligence (AI) and machine learning (ML) methods with distinct characteristics in various problem scenarios. Reinforcement tensile forces play a significant role in the design and performance of retaining walls (RWs). These are crucial for the stability and structural integrity of the retaining walls, preventing wall failure. For this, an attempt was made to predict the reinforcement tensile forces of the back-to-back mechanically stabilized earth (MSE) walls under train loading, which are necessary for upkeeping the transportation infrastructure. Six innovative models were created to counter this challenge that combines AI and ML techniques, i.e., LR, SVM, ANN, ANFIS, ANN-GA, and ANFIS-GA. Consequently, the genetic algorithm (GA) technique was also used to integrate new models, such as GA-ANN and GA-ANFIS. The input data for the models were derived from the parametric study conducted in the finite element analyses. Statistical measures, including root-mean-square-error (RMSE), mean-absolute-error (MAE), and coefficient-of-determination (R2), were analyzed and compared across multiple baseline methods to verify the accuracy of the suggested model. Results show that the proposed model's (ANFIS-GA) accuracy (R2) is 0.9876 and errors (RMSE and MAE) are 0.0191 and 0.0122, respectively. This model outperforms the baseline models in all relevant respects and shall precisely predict the tensile forces of the back-to-back MSE walls.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

The data that supports the findings of this study are available from the corresponding author upon reasonable request.

References

  • Abd Elaziz M, Dahou A, Abualigah L, Yu L, Alshinwan M, Khasawneh AM, Lu S (2021) Advanced metaheuristic optimization techniques in applications of deep neural networks: a review. Neural Comput Appl 33(21):14079–14099

    Article  Google Scholar 

  • Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11):e00938

    Article  PubMed  PubMed Central  Google Scholar 

  • Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting. Economet Rev 29(5–6):594–621

    Article  MathSciNet  Google Scholar 

  • Bai C, Nguyen H, Asteris PG, Nguyen-Thoi T, Zhou J (2020) A refreshing view of soft computing models for predicting the deflection of reinforced concrete beams. Appl Soft Comput 97:106831

    Article  Google Scholar 

  • Banerjee L, Chawla S, Dash SK (2020a) Application of geocell reinforced coal mine overburden waste as subballast in railway tracks on weak subgrade. Constr Build Mater 265:120774

    Article  Google Scholar 

  • Banerjee L, Chawla S, Dash SK (2020b) Performance evaluation of coal mine overburden as a potential subballast material in railways with additional improvement using geocell. J Mater Civ Eng 32(8):04020200

    Article  Google Scholar 

  • Banerjee L, Chawla S, Dash SK (2023) Investigations on cyclic loading behavior of geocell stabilized tracks with coal overburden refuse recycled as subballast material. Transp Geotech 40:100969

    Article  Google Scholar 

  • Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33

    Article  Google Scholar 

  • Cain JW (2014) Mathematics of fitting scientific data. Molecular life sciences. Springer, New York, pp 668–673

    Google Scholar 

  • Chawla S (2013) Analyses and experimental investigations of railway tracks with and without geosynthetic reinforcement [PhD Thesis]. IIT Delhi

  • Chawla S, Shahu JT (2016a) Reinforcement and mud-pumping benefits of geosynthetics in railway tracks: model tests. Geotext Geomembr 44(3):366–380

    Article  Google Scholar 

  • Chawla S, Shahu JT (2016b) Reinforcement and mud-pumping benefits of geosynthetics in railway tracks: numerical analysis. Geotext Geomembr 44(3):344–357

    Article  Google Scholar 

  • Chawla A, Pasupuleti S, Chawla S, Rao ACS, Sarkar K, Dwivedi R (2019) Landslide susceptibility zonation mapping: a case study from Darjeeling District, Eastern Himalayas, India. J Indian Soc Remote Sens 47(3):497–511. https://doi.org/10.1007/s12524-018-0916-6

    Article  Google Scholar 

  • Chawla S, Shahu JT, Kumar S (2021) Analysis of cyclic deformation and post-cyclic strength of reinforced railway tracks on soft subgrade. Transp Geotech 28:100535

    Article  Google Scholar 

  • Cheng MY, Wu YW (2009) Evolutionary support vector machine inference system for construction management. Autom Constr 18(5):597–604

    Article  Google Scholar 

  • Chua CG, Goh AT (2005) Estimating wall deflections in deep excavations using Bayesian neural networks. Tunn Undergr Space Technol 20(4):400–409

    Article  Google Scholar 

  • Cohen AI, Yoshimura M (1983) A branch-and-bound algorithm for unit commitment. IEEE Trans Power Appar Syst 2:444–451

    Article  ADS  Google Scholar 

  • Coppola E Jr, Szidarovszky F, Poulton M, Charles E (2003) Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions. J Hydrol Eng 8(6):348–360

    Article  Google Scholar 

  • Currin C, Mitchell T, Morris M, Ylvisaker D (1991) Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments. J Am Stat Assoc 86(416):953–963

    Article  MathSciNet  Google Scholar 

  • Damians IP, Bathurst RJ, Olivella S, Lloret A, Josa A (2021) 3D modelling of strip reinforced MSE walls. Acta Geotech 16:711–730

    Article  Google Scholar 

  • Dao DV, Ly H-B, Trinh SH, Le T-T, Pham BT (2019) Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials 12(6):983

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  • Deb K (2011) Multi-objective optimisation using evolutionary algorithms: an introduction. Multi-objective evolutionary optimisation for product design and manufacturing. Springer, London, pp 3–34

    Chapter  Google Scholar 

  • Dike HU, Zhou Y, Deveerasetty KK, Wu Q (2018) Unsupervised learning based on artificial neural network: a review. In: 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), pp 322–327

  • Erkaymaz O (2020) Resilient back-propagation approach in small-world feed-forward neural network topology based on Newman-Watts algorithm. Neural Comput Appl 32(20):16279–16289

    Article  Google Scholar 

  • Ghaleini EN, Koopialipoor M, Momenzadeh M, Sarafraz ME, Mohamad ET, Gordan B (2019) A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Eng Comput 35:647–658

    Article  Google Scholar 

  • Ghimire S, Deo RC, Downs NJ, Raj N (2019) Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia. J Clean Prod 216:288–310

    Article  Google Scholar 

  • Goh ATC, Kulhawy FH (2005) Reliability assessment of serviceability performance of braced retaining walls using a neural network approach. Int J Numer Anal Meth Geomech 29(6):627–642

    Article  Google Scholar 

  • Gordan B, Koopialipoor M, Clementking A, Tootoonchi H, Tonnizam Mohamad E (2019) Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques. Eng Comput 35(3):945–954

    Article  Google Scholar 

  • Guler E, Hamderi M, Demirkan MM (2007) Numerical analysis of reinforced soil-retaining wall structures with cohesive and granular backfills. Geosynth Int 14(6):330–345

    Article  Google Scholar 

  • Guzelbey IH, Cevik A, Erklig A (2006) Prediction of web crippling strength of cold-formed steel sheetings using neural networks. J Constr Steel Res 62(10):962–973

    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(4):1066–1085

    Article  Google Scholar 

  • Huang B, Bathurst RJ, Hatami K (2009) Numerical study of reinforced soil segmental walls using three different constitutive soil models. J Geotech Geoenviron Eng 135(10):1486–1498

    Article  Google Scholar 

  • Jalal M, Moradi-Dastjerdi R, Bidram M (2019) Big data in nanocomposites: ONN approach and mesh-free method for functionally graded carbon nanotube-reinforced composites. J Comput Des Eng 6(2):209–223

    Google Scholar 

  • Jan JC, Hung SL, Chi SY, Chern JC (2002) Neural network forecast model in deep excavation. J Comput Civ Eng 16(1):59–65

    Article  Google Scholar 

  • Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  • Jiang Y, Han J, Parsons RL (2020) Numerical evaluation of secondary reinforcement effect on geosynthetic-reinforced retaining walls. Geotext Geomembr 48(1):98–109

    Article  Google Scholar 

  • Kandiri A, Golafshani EM, Behnood A (2020) Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm. Constr Build Mater 248:118676

    Article  Google Scholar 

  • Karaboga D, Kaya E (2019) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev 52(4):2263–2293

    Article  Google Scholar 

  • Karayiannis NB, Mi GW (1997) Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques. IEEE Trans Neural Netw 8(6):1492–1506

    Article  CAS  PubMed  Google Scholar 

  • Karballaeezadeh N, Mohammadzadeh SD, Shamshirband S, Hajikhodaverdikhan P, Mosavi A, Chau K (2019) Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan-Firuzkuh road). Eng Appl Comput Fluid Mech 13(1):188–198

    Google Scholar 

  • Koerner RM (2012) Designing with geosynthetics, vol 1. Xlibris Corporation

    Google Scholar 

  • Koerner RM, Soong T-Y (2001) Geosynthetic reinforced segmental retaining walls. Geotext Geomembr 19(6):359–386

    Article  Google Scholar 

  • Koopialipoor M, Murlidhar BR, Hedayat A, Armaghani DJ, Gordan B, Mohamad ET (2020) The use of new intelligent techniques in designing retaining walls. Eng Comput 36:283–294

    Article  Google Scholar 

  • Kung GT, Hsiao EC, Schuster M, Juang CH (2007) A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays. Comput Geotech 34(5):385–396

    Article  Google Scholar 

  • Liu SW, Huang JH, Sung JC, Lee CC (2002) Detection of cracks using neural networks and computational mechanics. Comput Methods Appl Mech Eng 191(25–26):2831–2845

    Article  ADS  Google Scholar 

  • Lu P, Chen S, Zheng Y (2012) Artificial intelligence in civil engineering. Math Probl Eng. https://doi.org/10.1155/2012/145974

    Article  Google Scholar 

  • McKelvey RD, Zavoina W (1975) A statistical model for the analysis of ordinal level dependent variables. J Math Sociol 4(1):103–120

    Article  MathSciNet  Google Scholar 

  • Midas GTS-NX (2016) Geotechnical and tunnel analysis system reference manual for modelling, integrated design and analysis. Midas Corporation, Itasca, IL

    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

    Article  Google Scholar 

  • Nabipour N, Karballaeezadeh N, Dineva A, Mosavi A, Mohammadzadeh SD, Shamshirband S (2019) Comparative analysis of machine learning models for prediction of remaining service life of flexible pavement. Mathematics 7(12):1198

    Article  Google Scholar 

  • Nazir MS, Alturise F, Alshmrany S, Nazir HMJ, Bilal M, Abdalla AN, Sanjeevikumar P, Ali ZM (2020) Wind generation forecasting methods and proliferation of artificial neural network: a review of five years research trend. Sustainability 12(9):3778

    Article  Google Scholar 

  • Nguyen MD, Pham BT, Ho LS, Ly H-B, Le T-T, Qi C, Le VM, Le LM, Prakash I, Bui DT (2020) Soft-computing techniques for prediction of soils consolidation coefficient. CATENA 195:104802

    Article  Google Scholar 

  • Nhu VH, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, Jaafari A, Chen W, Miraki S, Dou J et al (2020) Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. Int J Environ Res Public Health 17(8):2749

    Article  PubMed  PubMed Central  Google Scholar 

  • Rudovica V, Rotter A, Gaudêncio SP, Novoveská L, Akgül F, Akslen-Hoel LK, Alexandrino DA, Anne O, Arbidans L, Atanassova M et al (2021) Valorization of marine waste: use of industrial by-products and beach wrack towards the production of high added-value products. Front Mar Sci 8:723333

    Article  Google Scholar 

  • Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T (2010) Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Constr Build Mater 24(5):709–718

    Article  Google Scholar 

  • Sravanam SM, Balunaini U, Madhira RM (2020) Behavior of connected and unconnected back-to-back walls for bridge approaches. Int J Geomech 20(7):06020013

    Article  Google Scholar 

  • Sundaravel V, Dodagoudar GR (2020) Deformation and stability analyses of hybrid earth retaining structures. Int J Geosynth Ground Eng 6:1–25

    Article  Google Scholar 

  • Vadavadagi SS, Chawla S (2023) Effect of rail axle load on geosynthetic reinforced back-to-back mechanically stabilized earth walls: experimental and numerical studies. Transp Geotech 38:100907

    Article  Google Scholar 

  • Walia N, Singh H, Sharma A (2015) ANFIS: adaptive neuro-fuzzy inference system—a survey. Int J Comput Appl 123(13):32–38

    Google Scholar 

  • Yang KH, Wu JT, Chen RH, Chen YS (2016) Lateral bearing capacity and failure mode of geosynthetic-reinforced soil barriers subject to lateral loadings. Geotext Geomembr 44(6):799–812

    Article  Google Scholar 

  • Yazdandoust M, Samee AA, Ghalandarzadeh A (2022) Assessment of seismic behavior of back-to-back mechanically stabilized earth walls using 1g shaking table tests. Soil Dyn Earthq Eng 155:106078

    Article  Google Scholar 

  • Yu L, Yu Y (2017) Energy-efficient neural information processing in individual neurons and neuronal networks. J Neurosci Res 95(11):2253–2266

    Article  CAS  PubMed  Google Scholar 

  • Yücel M, Bekdaş G, Nigdeli SM, Kayabekir AE (2021) An artificial intelligence-based prediction model for optimum design variables of reinforced concrete retaining walls. Int J Geomech 21(12):04021244

    Article  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support were received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India

    Shilpa S. Vadavadagi & Sowmiya Chawla

  2. Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India

    Prince Kumar

Authors
  1. Shilpa S. Vadavadagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sowmiya Chawla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prince Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Shilpa S. Vadavadagi: investigation, data curation, formal analysis, validation, and writing–original draft. Sowmiya Chawla: conceptualization, methodology, visualization, resources, supervision, writing–review and editing. Prince Kumar: analysis, writing–review and editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sowmiya Chawla.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declare that there are no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 13 KB)

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

Vadavadagi, S.S., Chawla, S. & Kumar, P. Prediction and validation of geogrid tensile force distribution in back-to-back MSE walls under rail axle load: finite-element and intelligent techniques. Environ Earth Sci 83, 149 (2024). https://doi.org/10.1007/s12665-024-11443-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-024-11443-2

Keywords

Search

Navigation