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Data-driven optimization and pressuremeter modulus prediction using response surface methodology for smarter geotechnical design
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  • Published: 19 January 2026

Data-driven optimization and pressuremeter modulus prediction using response surface methodology for smarter geotechnical design

  • Ghania Boukhatem1,
  • Messaouda Bencheikh2,
  • Souhila Rehab Bekkouche3,
  • Mehmet Serkan Kırgız4,5,
  • D. Manikandan6 &
  • …
  • Ramaswamy Krishnaraj7,8 

Scientific Reports , Article number:  (2026) Cite this article

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Subjects

  • Engineering
  • Mathematics and computing

Abstract

Accurate evaluation of soil mechanical behavior remains a cornerstone of reliable geotechnical engineering and optimized foundation design. Among the key parameters that define soil deformability, the pressuremeter modulus (Ep) plays a crucial role in assessing the ground response and structural stability. However, conventional approaches for determining Ep through empirical correlations or in situ measurements are often labor intensive, costly, and subject to significant uncertainty. These limitations underscore the necessity for advanced predictive frameworks that can provide both accuracy and efficiency in geotechnical characterization. In the present work, response surface methodology (RSM) is applied as an advanced statistical modeling and optimization technique to improve the predictive capability of the pressuremeter modulus. The experimental investigation is structured via a central composite design (L35) framework, enabling the systematic evaluation of nonlinear interactions among the principal geotechnical variables, namely, depth (D), cohesion (C), internal friction angle (φ), and unit weight (γ). The developed model was rigorously validated through analysis of variance, which revealed a high level of statistical significance with an excellent coefficient of determination (R2 = 96.50%). The desirability function approach is subsequently applied to determine the optimal combination of soil parameters that maximize Ep, providing valuable guidance for design and site investigation strategies. The findings highlight the transformative potential of data-driven statistical modeling in refining soil property estimation and advancing geotechnical design practices. By demonstrating the robustness and predictive capacity of the RSM, this research establishes a methodological foundation for intelligent, efficient, and cost-effective geotechnical engineering applications.

Data availability

The data used to support the findings of this study are included within the article.

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Funding

This work was carried out without financial support from any public, commercial, or non-profit funding organization.

Author information

Authors and Affiliations

  1. Materials Geomaterials and Environment Laboratory (LMGE), Department of Civil Engineering, Faculty of Technology, Badji Mokhtar-Annaba University, P.O. Box 12, 23000, Annaba, Algeria

    Ghania Boukhatem

  2. Laboratory of Civil Engineering and Hydraulics (LGCH), Faculty of Science and Technology, University 8 May 1945 Guelma, Guelma, Algeria

    Messaouda Bencheikh

  3. Materials Geotechnics Housing and Urbanism Laboratory (LMGHU), Department of Civil Engineering, University of Skikda, P.O. Box 26, 21000, Skikda, Algeria

    Souhila Rehab Bekkouche

  4. Department of Civil, Architectural and Construction Engineering and Management, Academia of ACMCEN, 4017, Beaverton, OR, 97075, USA

    Mehmet Serkan Kırgız

  5. Department of Civil Engineering, Faculty of Engineering, İstanbul University-Cerrahpaşa, Istanbul, Turkey

    Mehmet Serkan Kırgız

  6. Department of Mechanical Engineering, SRM TRP Engineering College, Tiruchirappalli, Tamil Nadu, 621105, India

    D. Manikandan

  7. Department of Mechanical Engineering, College of Engineering and Technology, Dambi Dollo University, Dambi Dollo, Ethiopia

    Ramaswamy Krishnaraj

  8. Center for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India

    Ramaswamy Krishnaraj

Authors
  1. Ghania Boukhatem
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  2. Messaouda Bencheikh
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  3. Souhila Rehab Bekkouche
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  4. Mehmet Serkan Kırgız
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  5. D. Manikandan
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  6. Ramaswamy Krishnaraj
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Contributions

G.B.: Conceptualization; Data curation; formal analysis; investigation; methodology; project administration; resources; software; supervision; validation; M.B.: Conceptualization; data curation; formal analysis; resources; software; supervision; validation; S.R.B.: Investigation; methodology; project administration; resources; M.S.K.: Conceptualization, methodology, validation, formal analysis, resources, data curation, writing—original draft, writing—review and editing, visualization, supervision, project administration, investigation; KR: Conceptualization, methodology, validation, formal analysis, resources, data curation, writing—original draft, writing—review and editing, visualization, supervision, project administration; D.M.: Conceptualization, methodology, validation, formal analysis, resources, data curation, writing—original draft, writing—review and editing, visualization.

Corresponding author

Correspondence to Ramaswamy Krishnaraj.

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Boukhatem, G., Bencheikh, M., Bekkouche, S.R. et al. Data-driven optimization and pressuremeter modulus prediction using response surface methodology for smarter geotechnical design. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36262-2

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  • Received: 06 November 2025

  • Accepted: 12 January 2026

  • Published: 19 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-36262-2

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Keywords

  • Optimization
  • Pressiometric modulus (Ep)
  • Response surface methodology (RSM)
  • Statistical analysis (ANOVA)
  • Geotechnical properties
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