Structural damage and foundation leakage are major concerns for earthen dams. To minimize seepage, cutoff walls are typically installed beneath the dam core to act as impermeable barriers. While concrete cutoff walls are widely used, their limited ductility and strength incompatibility with foundation soil present design challenges. Plastic concrete, a modified form of conventional concrete incorporating bentonite and pond ash, offers improved ductility and reduced brittleness, making it a suitable alternative. This study investigates the use of pond ash-based flowable fill as a replacement for normal concrete in plastic concrete cutoff walls. The unconfined compressive strength (UCS) of plastic concrete mixes was analyzed using four advanced regression machine learning algorithms: multivariate adaptive regression splines, extreme neural network (ENN), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). Several performance indices were used to evaluate model accuracy. The MARS model achieved the highest accuracy, with R2 = 0.990 for training and R2 = 0.963 for testing, followed by XGBoost, GBM, and ENN. SHAP analysis revealed that curing period has the most significant positive effect on UCS, followed by water and cement contents, while bentonite showed the least impact. Key properties were evaluated to determine an optimal mix design. This research enhances the understanding of CLSM-based plastic concrete and supports its application in cutoff walls by developing accurate UCS prediction models, contributing to the improved suitability and sustainability of dam foundation systems.

The recent seismic activity on Turkiye's west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North-South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region-specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three-component strong-ground motion data from accelerometer sites with known VS30 values throughout western Turkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well-known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.