The primary objective of this study was to probabilistically assess the spatial distribution of soil suction using the Electrical Resistivity Tomography (ERT) technique. Under field conditions, it is almost impossible to characterize the soil suction distribution in a deterministic manner. Furthermore, various variables in field conditions that impact the suction distribution are particularly difficult to assess in large-scale earth infrastructure. Understanding the distribution of the key constitutive variable is critical in geotechnical engineering practice. Thus, an accurate and broader scale estimate is important, accordingly constituting the soil water characteristic curve. However, since the distribution of soil suction varies under field conditions, it is unlikely that suction distribution can be characterized deterministically in the field. Therefore, probabilistic analysis is practically the ideal option for identifying the suction distribution in the field. For probabilistic analysis, having sufficient data points is crucial to increasing the reliability of the probabilistic model. Electrical resistivity tomography is one of the most effective options for generating extensive spatial and temporal data in the field. In this study, a simple but efficient probabilistic approach, Gaussian distribution analysis, was conducted using field monitoring suction and resistivity data to correlate soil suction with resistivity. The field-monitored data were first deterministically analyzed. The Spearman Rank Correlation Coefficient was used to check the statistical dependence between the two variables. Based on the study’s analyses, soil suction and resistivity were highly scattered at higher resistivity values (>70 Ω-m). Probabilistically, the soil resistivity (>70 Ω-m) was reasonably correlated with suction. The study revealed the importance of the probabilistic correlation of unsaturated soil variables (soil suction) with geophysical parameters (soil resistivity).
Published in the ASCE Library – Publication: Geotechnical Frontiers 2025. You can share or reference the paper here using the icons at the bottom of the page, if you like.
Authors: Md. Jobair Bin Alam, Ph.D., P.E., M.ASCE, Asif Ahmed, Ph.D., P.E., M.ASCE, and Naima Rahman, Ph.D., M.ASCE.
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