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- Title
Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations.
- Authors
Raja, Muhammad Nouman Amjad; Abdoun, Tarek; El-Sekelly, Waleed
- Abstract
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 ( R 2 = 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.
- Subjects
STOCHASTIC learning models; REINFORCED soils; MACHINE learning; SUSTAINABILITY; SUSTAINABLE construction
- Publication
Buildings (2075-5309), 2024, Vol 14, Issue 4, p954
- ISSN
2075-5309
- Publication type
Article
- DOI
10.3390/buildings14040954