Volume 3, Issue 1 — Year 2026 — Article e100054

ISSN (Online): 3115-8129 Biannually

Identification of Surface Subsidence Risk in Shallow Foundations using Multi-Layer Perceptron (MLP)

Article Type: Research
Pages: e100054
DOI: https://doi.org/10.22034/CGEL.3.1.e100054
Crossmark
Open Access
Authors
Affiliations
  1. Department of Civil Engineering, University Collage of Nabi Akram (UCNA), Tabriz 5183919611, Iran
Corresponding author
Email: hamed.mahmidi75@gmail.com
Received: 19 January 2026 / Accepted: 31 March 2026 / Published: 25 May 2026
Abstract

Surface subsidence is a critical geotechnical concern in shallow foundation systems, especially in urban environments with heterogeneous soil conditions and variable groundwater levels. Excessive or uneven settlement can compromise structural integrity, reduce service life, and increase maintenance costs. Therefore, timely identification and prediction of surface subsidence risk are essential in foundation design and site planning. In this study, a data-driven approach using Multi-Layer Perceptron (MLP) neural networks is proposed to predict the risk of surface subsidence associated with shallow foundations. A comprehensive dataset was compiled, incorporating soil physical and mechanical properties, foundation geometry, groundwater depth, load characteristics, and historical subsidence measurements from multiple construction sites. The MLP model was trained using supervised learning techniques and validated with a test dataset to ensure accuracy and generalization capability. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R² were used to evaluate the model's predictive capability. The results show that the MLP model successfully captures the complex nonlinear interactions among the influencing factors and achieves high predictive accuracy. This research demonstrates the effectiveness of MLP neural networks as a practical tool for geotechnical risk assessment and decision-making. The proposed method can support engineers and planners in identifying high-risk zones early in the design process and implementing appropriate mitigation strategies.

Keywords

Subsidence, Shallow foundation, Multi-layer perceptron (MLP), Settlement prediction, Machine learning

Data availability statement

The data supporting the findings of this study are available within article. No publicly archived dataset was generated.

Funding

This research received no external funding.

Highlights
  • MLP predicts subsidence risk in shallow foundations
  • Model captures soil, load, groundwater effects
  • Results improve design and decisions
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How to cite
Mahmodi, H. (2026). Identification of Surface Subsidence Risk in Shallow Foundations using Multi-Layer Perceptron (MLP). Civil and Geoengineering Letters, 3(1), e100054. https://doi.org/10.22034/CGEL.3.1.e100054
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