- Advanced tree-based machine learning methods for predicting the seismic response of regular and irregular RC frames. Dans: Structures, v. 64 (juin 2024). (2024):
- Examining the Role of Liquefiable Layer Thickness and Depth on the Seismic Lateral Response of Piles through Numerical Analyses. Dans: International Journal of Geomechanics, v. 23, n. 5 (mai 2023). (2023):
- Numerical Investigation of the Effects of Ground Motion Characteristics on the Seismic Behavior of Liquefiable Soil. Dans: Periodica Polytechnica Civil Engineering. :
- Effect of shear strain compatibility and incompatibility approaches in the design of high modulus columns against liquefaction: A case study in Christchurch, New Zealand. Dans: Bulletin of Earthquake Engineering, v. 20, n. 11 (juin 2022). (2022):
- Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data. Dans: Soil Dynamics and Earthquake Engineering, v. 154 (mars 2022). (2022):
- Numerical investigation of seismic performance of high modulus columns under earthquake loading. Dans: Earthquake Engineering and Engineering Vibration, v. 18, n. 4 (octobre 2019). (2019):
- Sıvılaşmanın UBC3D-PLM Model ile Tahmin Edilmesi: Santrifüj Deneyi Örneği. Dans: Teknik Dergi, v. 30, n. 5 (septembre 2019). (2019):
- Parametric investigation of effectiveness of high modulus columns in liquefaction mitigation. Dans: Soil Dynamics and Earthquake Engineering, v. 139 (décembre 2020). (2020):