Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Muhammad Nouman Amjad Raja, Tarek Abdoun, Waleed El-Sekelly

Research output: Contribution to journalArticlepeer-review


This study introduces a novel application of gene expression programming (GEP) for the reliability analysis (RA) of reinforced soil foundations (RSFs) based on settlement criteria, addressing a critical gap in sustainable construction practices. Based on the principles of probability and statistics, the soil uncertainties were mapped using the first-order second-moment (FOSM) approach. The historical data generated via a parametric study on a validated finite element numerical model were used to train and validate the GEP models. Among the ten developed GEP frameworks, the best-performing model, abbreviated as GEP-M9 ((Formula presented.) = 0.961 and RMSE = 0.049), in the testing phase was used to perform the RA of an RSF. This model’s effectiveness in RA was affirmed through a comprehensive evaluation, including parametric sensitivity analysis and validation against two independent case studies. The reliability index (β) and probability of failure (Pf) were determined across various coefficient of variation (COV) configurations, underscoring the model’s potential in civil engineering risk analysis. The newly developed GEP model has shown considerable potential for analyzing civil engineering construction risk, as shown by the experimental results of varying settlement values.

Original languageEnglish (US)
Article number954
Issue number4
StatePublished - Apr 2024


  • GEP
  • finite-element-based modelling
  • probability of failure
  • reinforced soil foundations
  • reliability analysis
  • settlement analysis

ASJC Scopus subject areas

  • Architecture
  • Civil and Structural Engineering
  • Building and Construction


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