Mohamed El Amine Ben Seghier 
- (2023): Application of machine learning approaches for modelling crack growth rates. In: ce/papers, v. 6, n. 3-4 (September 2023).
- (2023): Assessment of artificial intelligence‐ based techniques for the estimation of pile group scour depth. In: ce/papers, v. 6, n. 5 (September 2023).
- (2023): Predicting Wall Thickness Loss in Water Pipes Using Machine Learning Techniques. In: ce/papers, v. 6, n. 5 (September 2023).
- (2023): Scrutinizing the Performances of Hybrid ANN Models for Forecasting Condition of Bridges. In: ce/papers, v. 6, n. 5 (September 2023).
- (2023): A Novel Chaotic Optimization‐Oriented Model for Bridge Maintenance and Rehabilitation Planning. In: ce/papers, v. 6, n. 5 (September 2023).
- (2023): Metaheuristic‐based machine learning modeling of the compressive strength of concrete containing waste glass. In: Structural Concrete, v. 24, n. 4 (5 July 2023).
- (2022): Integrity of API 5L X56 Offshore Pipeline Subjected to Girth Weld Anomalies and Corrosion Using Probabilistic Methods. In: Journal of Performance of Constructed Facilities (ASCE), v. 36, n. 5 (October 2022).
- (2022): Random forest-based algorithms for accurate evaluation of ultimate bending capacity of steel tubes. In: Structures, v. 44 (October 2022).
- (2019): Sensitivity of reliability-based fatigue analysis to crack shape development in cracked pipeline. In: Procedia Structural Integrity, v. 22 ( 2019).
- (2021): Accurate Structural Reliability Analysis Using an Improved Line-Sampling-Method-Based Slime Mold Algorithm. In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, v. 7, n. 2 (June 2021).
- (2021): Modeling the nonlinear behavior of ACC for SCFST columns using experimental-data and a novel evolutionary-algorithm. In: Structures, v. 30 (April 2021).
Structurae cooperates with
