Predicting Seismic-Induced Settlement of Pipelines Buried in Sandy Soil Reinforced with Concrete and FRP Micropiles: A Genetic Programming Approach

Unstable sandy soils pose significant challenges for buried pipelines due to soil–infrastructure interaction, leading to settlement that increases the risk of displacement and stress-induced fractures. In earthquake-prone regions, seismic-induced ground deformation further threatens underground infrastructure. Fiber-reinforced polymer (FRP) composites have emerged as a sustainable alternative to conventional piling materials, addressing durability issues in deep foundations. This paper introduces novel explicit models for predicting the maximum settlement of oil pipelines supported by concrete or polymer micropiles under seismic loading. Using genetic programming (GP), this study develops closed-form expressions based on simplified input parameters—micropile dimensions, pile spacing, soil properties, and peak ground acceleration—improving the models’ practicality for engineering applications. The models were evaluated using a dataset of 610 data points and demonstrated good accuracy across different conditions, achieving coefficients of determination (R2) as high as 0.92, among good values for other evaluation metrics. These findings contribute to a robust, practical tool for mitigating seismic risks in pipeline design, highlighting the potential of FRP micropiles for enhancing infrastructure resilience under challenging geotechnical scenarios.

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Publication Details

Journal
Journal of Composites Science
Published
2025-04-25
DOI
https://doi.org/10.3390/jcs9050207
Citations
2
Primary Topic
Geotechnical Engineering and Underground Structures
Type
article
Field-Weighted Citation Impact
1.91

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article

Predicting Seismic-Induced Settlement of Pipelines Buried in Sandy Soil Reinforced with Concrete and FRP Micropiles: A Genetic Programming Approach

Musab Aied Qissab Al‐Janabi, Luís Filipe Almeida Bernardo, Laith Sadik, Duaa Al-Jeznawi et al.
2 citations
Journal of Composites Science
Geotechnical Engineering and Underground Structures
1.91
article

Predicting Seismic-Induced Settlement of Pipelines Buried in Sandy Soil Reinforced with Concrete and FRP Micropiles: A Genetic Programming Approach

Musab Aied Qissab Al‐Janabi, Luís Filipe Almeida Bernardo, Laith Sadik, Duaa Al-Jeznawi, Jorge Miguel de Almeida Andrade
article en
2 citations

Abstract

Unstable sandy soils pose significant challenges for buried pipelines due to soil–infrastructure interaction, leading to settlement that increases the risk of displacement and stress-induced fractures. In earthquake-prone regions, seismic-induced ground deformation further threatens underground infrastructure. Fiber-reinforced polymer (FRP) composites have emerged as a sustainable alternative to conventional piling materials, addressing durability issues in deep foundations. This paper introduces novel explicit models for predicting the maximum settlement of oil pipelines supported by concrete or polymer micropiles under seismic loading. Using genetic programming (GP), this study develops closed-form expressions based on simplified input parameters—micropile dimensions, pile spacing, soil properties, and peak ground acceleration—improving the models’ practicality for engineering applications. The models were evaluated using a dataset of 610 data points and demonstrated good accuracy across different conditions, achieving coefficients of determination (R2) as high as 0.92, among good values for other evaluation metrics. These findings contribute to a robust, practical tool for mitigating seismic risks in pipeline design, highlighting the potential of FRP micropiles for enhancing infrastructure resilience under challenging geotechnical scenarios.

Journal of Composites ScienceVol. 9(5)
University of Beira Interior (PT), Nahrain University (IQ), University of Cincinnati (US)
Ministério da Ciência, Tecnologia e Ensino Superior, Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 16%
Geotechnical Engineering and Underground Structures
1.91
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