Abstract
Reliability analysis of a geosynthetic reinforced retaining wall (GRRW) is performed under seismic conditions. GRRW is analysed deterministically using the horizontal slice method (HSM). To rule out the limitations of deterministic approach, a comprehensive probabilistic analysis is carried out using a biologically inspired evolutionary algorithm called genetic programming (GP). GP, commonly known as symbolic regression, automatically evolves both the structure and the parameters of the considered mathematical model. The tension mode of failure is considered in the probabilistic analysis. The stochastic parameters involved in the study include internal friction angle of soil (ϕ) and the unit weight (γ). The results are validated using the Monte Carlo simulation (MCS) method, for the same set of parameters. This is done to scrutinize the efficiency and accuracy of the proposed method. A parametric study including the influence of coefficient of variation of ϕ on the probability of failure of GRRW is conducted. The results depict the efficiency and robustness of the proposed methodology. The genetic programming is a precise evolutionary method that delivers high performance in estimating the probability of failure of GRRW.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elias V, Christopher BR, Berg RR, Berg RR (2001) Mechanically stabilized Earth walls and reinforced soil slopes: design and construction guidelines (Updated Version) (No. FHWA-NHI-00–043). Federal Highway Administration, United States
AASHTO (2007) LRFD bridge design specifications. 4th ed. American Association of State Highway and Transportation Officials, Washington, D.C.
Basha BM, Babu GS (2010) Reliability assessment of internal stability of reinforced soil structures: a pseudo-dynamic approach. Soil Dyn Earthq Eng 30(5):336–353
Basha BM, Babu GS (2012) Target reliability-based optimization for internal seismic stability of reinforced soil structures. Geotechnique 62(1):55–68
Agarwal E, Pain A, Mukhopadhyay T, Metya S, Sarkar S (2021) Efficient computational system reliability analysis of reinforced soil-retaining structures under seismic conditions including the effect of simulated noise. In: Engineering with computers, pp1–23
Agarwal E, Pain A, Sarkar S (2021) Stochastic stability analysis of geosynthetic reinforced slopes subjected to harmonic base shaking. Transp Geotech 29:100562
Nouri H, Fakher A, Jones CJFP (2006) Development of horizontal slice method for seismic stability analysis of reinforced slopes and walls. Geotext Geomembr 24(3):175–187
Metenidis MF, Witczak M, Korbicz J (2004) A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem. Eng Appl Artif Intell 17:363–370
Koza JR, Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT press
Shahgholi M, Fakher A, Jones CJFP (2001) Horizontal slice method of analysis. Geotechnique 51(10):881–885
Ling HI, Leshchinsky D, Perry EB (1997) Seismic design and performance of geosynthetic-reinforced soil structures. Geotechnique 47(5):933–952
Giustolisi O, Doglioni A, Savic DA, Webb BW (2007) A multi-model approach to analysis of environmental phenomena. Environ Model Softw 22(5):674–682
Rodriguez-Coayahuitl L, Morales-Reyes A, Escalante HJ (2019) A comparison among different levels of abstraction in genetic programming. In: IEEE international autumn meeting on power, electronics and computing (ROPEC), pp 1–6
Zhang Q, Barri K, Jiao P, Salehi H, Alavi AH (2021) Genetic programming in civil engineering: advent, applications and future trends. Artif Intell Rev 54(3):1863–1885
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027
Javadi AA, Rezania M, Nezhad MM (2006) Evaluation of liquefaction induced lateral displacements using genetic programming. Comput Geotech 33(4–5):222–233
Rezania M, Javadi AA (2007) A new genetic programming model for predicting settlement of shallow foundations. Can Geotech J 44(12):1462–1473
Alavi AH, Aminian P, Gandomi AH, Esmaeili MA (2011) Genetic-based modeling of uplift capacity of suction caissons. Expert Syst Appl 38(10):12608–12618
Gandomi AH, Alavi AH (2021) A new multi-gene genetic programming approach to non-linear system modeling Part II: geotechnical and earthquake engineering problems. Neural Comput Appl 21(1):189–201
Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. In: Proceedings of the International multiconference of engineers and computer scientists, vol 1, pp 77–80
Searson DP (2015) GPTIPS 2: an open-source software platform for symbolic data mining. Handbook of genetic programming applications. Springer, Cham, pp 551–573
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Agarwal, E., Verma, A.K., Pain, A., Sarkar, S. (2023). Probabilistic Analysis of a Geosynthetic Reinforced Soil Retaining Wall Under Seismic Conditions Using Genetic Programming. In: Muthukkumaran, K., Ayothiraman, R., Kolathayar, S. (eds) Soil Dynamics, Earthquake and Computational Geotechnical Engineering. IGC 2021. Lecture Notes in Civil Engineering, vol 300. Springer, Singapore. https://doi.org/10.1007/978-981-19-6998-0_20
Download citation
DOI: https://doi.org/10.1007/978-981-19-6998-0_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6997-3
Online ISBN: 978-981-19-6998-0
eBook Packages: EngineeringEngineering (R0)