Geotechnical Characterization and AI-Based Zonation of Coastal Sabkha Soils for Urban Expansion in the Eastern Province of Saudi Arabia

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@article{1718455,
      author = {Hassan Ahmed Atiah},
      title = {Geotechnical Characterization and AI-Based Zonation of Coastal Sabkha Soils for Urban Expansion in the Eastern Province of Saudi Arabia},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {5262-5272},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718455.pdf},
      abstract = {Purpose: Coastal sabkha terrains in the Eastern Province of Saudi Arabia are increasingly exposed to urban expansion, yet their geotechnical behavior remains variable because salinity, shallow groundwater, evaporite cementation, collapse upon wetting, and reclamation history interact across short distances. This review examines how integrated geotechnical characterization and artificial intelligence can support defensible zonation for expansion planning rather than reactive foundation remediation. Methods: Following a structured review approach inspired by recent Springer review models, this paper synthesizes 2020–2025 literature on sabkha characterization, saline-soil improvement, AI in geotechnical engineering, and geospatial suitability assessment. Search and screening procedures were guided by transparent review logic consistent with PRISMA 2020, while thematic synthesis was used instead of meta-analysis because sabkha definitions, laboratory protocols, and machine-learning inputs remain heterogeneous [1].
Findings: The evidence shows that coastal sabkha cannot be represented by a single index or laboratory test. Reliable characterization requires the joint interpretation of index properties, carbonate and sulfate chemistry, collapse and compressibility behavior, groundwater depth and salinity, stratigraphic variability, geophysics, and land-surface indicators derived from remote sensing [2–8]. For zonation, tree-based ensemble models and explainable AI are promising because they can fuse laboratory, field, geospatial, and environmental layers while still revealing dominant controls [16,18–20,28–30]. However, any AI map that is detached from site investigation, uncertainty reporting, and planning thresholds risks false confidence.
Originality: The paper contributes a review-based framework tailored to the Eastern Province that links sabkha geotechnics to AI-supported zoning for urban expansion. It also provides two practice-oriented artifacts: an Apple-inspired data-to-zonation workflow and an interpretable coastal risk matrix for planning, together with two synthesis tables that can be adapted in consultancy and municipal screening workflows.},
      keywords = {Sabkha Soils, Eastern Province, Saudi Arabia, Urban Expansion, Machine Learning, Geotechnical Characterization, Explainable AI, Zonation, Remote Sensing, Coastal Planning},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1718455}
  }