What is it about?

In a groundbreaking study, scientists have introduced a novel regression method, Residual Augmented Least Squares (RALS), to revolutionize the prediction of soil consistency properties, particularly in special clayey soils. This pioneering research, which finds its roots in econometrics, has been successfully applied to model the relationship between the plasticity index (PI) and the liquid limit (wL) of clayey soils, especially when traditional linear regression methods fall short due to the non-normal distribution of residuals. The team, harnessing data from 400 soil investigation reports, created a comprehensive database to define the characteristic properties of special soils in Istanbul. This dataset, containing an impressive 2890 liquid limit and plastic limit test results, was categorized into two subsets: high plastic clayey soils (CH) and low plastic clayey soils (CL), alongside a combined dataset. Initial attempts to model the PI-wL relationship using linear regression faced challenges. Notably, a significant portion of the data had to be discarded as outliers, identified through box-whisker plots, leading to residuals that did not adhere to a normal distribution. This is where the RALS method demonstrated its prowess. RALS-based regression analyses, conducted as a next step, showcased a higher degree of accuracy and reliability in dealing with non-normal residual distributions when compared to traditional linear regression. This marks a significant advancement in soil science, offering a more robust tool for predicting soil behavior, essential for civil engineering, construction, and environmental management.

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Why is it important?

This innovative approach is not just a leap forward in soil science, but also showcases the potential for interdisciplinary applications of econometric methods in environmental studies. The success of the RALS method in this context could pave the way for its application in other areas where non-normal residual distributions pose a challenge. As urban development and infrastructure projects continue to expand, the importance of accurately predicting soil properties cannot be overstated. The implementation of the RALS method in soil consistency prediction represents a critical step forward, ensuring safer and more efficient engineering practices in challenging soil


I'm impressed with the application of the Residual Augmented Least Squares (RALS) method in predicting soil consistency. This study not only overcomes the limitations of traditional linear regression models when dealing with non-normal residual distributions but also opens new avenues for interdisciplinary research. It's a significant advancement in geotechnical engineering, offering a more nuanced and accurate approach to understanding soil behavior, crucial for both construction and environmental conservation

Ümit Işıkdağ
Mimar Sinan Guzel Sanatlar Universitesi

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This page is a summary of: The application of Residual Augmented Least Squares method to predict the consistency properties of special clayey soils, Arabian Journal of Geosciences, February 2022, Springer Science + Business Media,
DOI: 10.1007/s12517-022-09715-x.
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