Farahnaz Soleimani 
- (2022): State-of-the-Art Review on Probabilistic Seismic Demand Models of Bridges: Machine-Learning Application. In: Infrastructures, v. 7, n. 5 (Mai 2022).
- (2022): Bridge seismic hazard resilience assessment with ensemble machine learning. In: Structures, v. 38 (April 2022).
- (2022): Probabilistic seismic analysis of bridges through machine learning approaches. In: Structures, v. 38 (April 2022).
- (2022): Artificial neural network application in predicting probabilistic seismic demands of bridge components. In: Earthquake Engineering and Structural Dynamics, v. 51, n. 3 (März 2022).
- (2021): Machine-learning-based predictive models for estimating seismically-induced slope displacements. In: Soil Dynamics and Earthquake Engineering, v. 148 (September 2021).
- (2021): Pattern Recognition of the Seismic Demands for Tall Pier Bridge Systems. In: Journal of Earthquake Engineering, v. 26, n. 12 (Juli 2021).
- (2021): Analytical seismic performance and sensitivity evaluation of bridges based on random decision forest framework. In: Structures, v. 32 (August 2021).
- (2020): Propagation and quantification of uncertainty in the vulnerability estimation of tall concrete bridges. In: Engineering Structures, v. 202 (Januar 2020).
- (2016): Cyclic Testing and Assessment of Columns Containing Recycled Concrete Debris. In: ACI Structural Journal, v. 113, n. 5 (September 2016).
- (2017): Identification of the significant uncertain parameters in the seismic response of irregular bridges. In: Engineering Structures, v. 141 (Juni 2017).
- (2017): Bridge classes for regional seismic risk assessment: Improving HAZUS models. In: Engineering Structures, v. 148 (Oktober 2017).
- (2017): A comparative analytical study on the fragility assessment of box-girder bridges with various column shapes. In: Engineering Structures, v. 153 (Dezember 2017).
Structurae kooperiert mit
