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<p>Subsurface heterogeneity poses a significant challenge for geotechnical experts, particularly in hazard-prone regions susceptible to earthquakes, landslides, and ground subsidence. This study presents the development of geotechnical soil maps (GSMs) by incorporating an improved version of the modified Shepard-based Inverse Distance Weighting (IDW) method utilizing the Google Earth Engine (GEE) platform. This approach integrates monotonization equations to improve accuracy and ensure smoother transitions in complex subsurface formations and compares with the traditional Kriging technique. Essential geotechnical parameters, i.e., SPT-N, shear wave velocity (Vs), soil classification, plasticity index (P.I), and linear shrinkage (L.S), were used to develop GSMs. Analysis revealed that superficial depths (1.5–3.0&#xa0;m) predominantly consist of silt and clay sediments, with SPT-N and Vs values ranging from 0–10 and 0–222&#xa0;m/s, respectively, while L.S ranged between 0 and &lt; 7%. In contrast, deeper layers (&gt; 3.0&#xa0;m) transitioned to cobbles, boulders, and rock strata. However, isolated zones exhibiting low strength or stiffness characteristics may pose stability challenges under hazard conditions. The advanced IDW algorithm demonstrated superior performance, achieving root mean square error (RMSE) and mean absolute error (MAE) values ranging from 0.12 to 1.45, and Nash Sutcliffe Efficiency (NSE) and correlation coefficients (R<sup>2</sup>) between 0.88 and 0.98, along with up to error mitigation of 48% against the conventional techniques. Additionally, it reduced areal coverage differences (2–18%), eliminated sharp ridges, and maintained geotechnical accuracy, aligning well with Tobler’s First Law of Geography. These findings highlight the effectiveness of this approach in enhancing hazard mitigation strategies and supporting the resilience of infrastructure.</p>

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Advanced Geospatial Modeling of Highly Variable Geotechnical Data for Infrastructure Resilience

  • 期刊论文

  • 发表日期：

    2026年2月14日

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  • Nauman Ijaz,
  • Zain Ijaz,
  • Nianqing Zhou,
  • Zia ur Rehman,
  • Hamdoon Ijaz,
  • Aashan Ijaz

摘要

Subsurface heterogeneity poses a significant challenge for geotechnical experts, particularly in hazard-prone regions susceptible to earthquakes, landslides, and ground subsidence. This study presents the development of geotechnical soil maps (GSMs) by incorporating an improved version of the modified Shepard-based Inverse Distance Weighting (IDW) method utilizing the Google Earth Engine (GEE) platform. This approach integrates monotonization equations to improve accuracy and ensure smoother transitions in complex subsurface formations and compares with the traditional Kriging technique. Essential geotechnical parameters, i.e., SPT-N, shear wave velocity (Vs), soil classification, plasticity index (P.I), and linear shrinkage (L.S), were used to develop GSMs. Analysis revealed that superficial depths (1.5–3.0 m) predominantly consist of silt and clay sediments, with SPT-N and Vs values ranging from 0–10 and 0–222 m/s, respectively, while L.S ranged between 0 and < 7%. In contrast, deeper layers (> 3.0 m) transitioned to cobbles, boulders, and rock strata. However, isolated zones exhibiting low strength or stiffness characteristics may pose stability challenges under hazard conditions. The advanced IDW algorithm demonstrated superior performance, achieving root mean square error (RMSE) and mean absolute error (MAE) values ranging from 0.12 to 1.45, and Nash Sutcliffe Efficiency (NSE) and correlation coefficients (R2) between 0.88 and 0.98, along with up to error mitigation of 48% against the conventional techniques. Additionally, it reduced areal coverage differences (2–18%), eliminated sharp ridges, and maintained geotechnical accuracy, aligning well with Tobler’s First Law of Geography. These findings highlight the effectiveness of this approach in enhancing hazard mitigation strategies and supporting the resilience of infrastructure.