Effective geotechnical engineering relies on accurately predicting soil parameters such as cohesion and the angle of internal friction, which are critical for ensuring the stability of structures such as small dams. Traditional laboratory testing can be prohibitively expensive and time-consuming, highlighting the need for efficient predictive models. This article aims to develop regression equations that estimate these parameters using easily obtainable soil properties in Chamchamal and Qaradagh Districts, Sulaymaniyah Governorate. Using soil water content, soil density, and plasticity index as key predictors, the one-way analysis of variance analysis achieved an R-squared value of approximately 0.87, with a root mean square error of 0.15 and a bias of about −1.2%, demonstrating high accuracy and robustness across different datasets. The analysis further revealed that increases in plasticity index significantly impacted the angle of internal friction (P = 0.014), while dry density showed a strong positive influence on cohesion. These findings underscore the role of soil parameters in estimating the soil compression index and demonstrate that a simplified, empirically derived model can offer practical insights for geotechnical applications. However, given the moderate correlation levels observed (R2 = 0.38 for cohesion and R2 = 0.61 for internal friction angle), the predictive capability of the models is limited. Therefore, the developed regression models should be regarded as preliminary tools, useful for initial assessments, but must be complemented by thorough field investigations and comprehensive engineering analyses to ensure the reliability and safety of dam structures.
Rahman et al. (Sat,) studied this question.