Ali Fayez Alkoradees

Predicting the nonlinear stress-strain response of high-performance cementitious materials is essential for the safe and efficient design of underground, geotechnical, and structural systems. Owing to the het erogeneous nature and complex damage evolution of these materials, conventional constitutive models of ten fail to capture their full mechanical behavior under varying loading conditions. This study introduces a Genetic Algorithm Optimized Long Short-Term Memory (GA-LSTM) framework that combines evo lutionary optimization and deep learning to model the complete stress-strain curve of high-performance concrete. Experimental data from uniaxial compression tests were systematically preprocessed using cleaning, normalization, interpolation, and Savitzky-Golay filtering to preserve physical integrity. The genetic algorithm optimized key hyperparameters including network depth, hidden units, dropout rate, and sequence length under a multi-objective, physics-informed strategy. The proposed model achieved superior predictive accuracy (R2 ≈ 0.995, RMSE < 3 MPa) with minimal deviation in peak stress and post-peak toughness. Comparative statistical analyses (ANOVA, Tukey’s HSD, Pearson correlation) ver ified the model’s reliability and robustness. The developed GA-LSTM framework offers a scalable and interpretable tool for mechanical characterization, design optimization, and geomechanical simulation of rock-concrete composite systems in civil, mining, and geotechnical engineering.

The control of a dc motor drive system is performed using two Proportional-Integral (PI) controllers, mostly comprising the outer (speed) and inner (torque) loops. However, the tuning of the PI controllers is challenging, because of the significant overshoot and substantial settling time. The overshoot cannot be absolutely removed without sacrificing the speediness. In this paper, an experimental implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) based high-precision controllers for a dc motor drive system is presented for eliminating the overshoot contemporaneously improving the settling time. In this regard, for fair comparison, Bode Plot method is used for obtaining most satisfactory values of K_p and K_i of both the controllers and the performance of the proposed ANFIS controllers is compared with the tuned PI controllers. For the sake of validation, an experimental setup, a 2-quadrant dc motor drive based on DS1104 R&D controller board from dSPACE is used. The designed ANFIS controllers are implemented and also authenticated using simulations. The obtained simulation as well as experimental results indicated that the ANFIS controller successfully eliminates the overshoot and significantly improves settling time. Thereby, outclass the tuned PI controllers by giving experimental results as 0% overshoot and just 0.18 s settling time.

The use of proton exchange membrane fuel cells (PEMFCs) in sustainable energy applications depends on their high efficiency levels along with their ability to produce low emissions and operation without noise. The optimization of PEMFC design variables faces difficulties because of the complex nonlinear relationships which exist between activation overpotential and concentration overpotential and internal resistance. The optimization methods PSO, DE and WOA face three major setbacks which include their delayed convergence rates as well as their sensitiveness to initial parameter settings and their tendency to lock onto sub-optimal solutions. The study presents the Parrot Optimizer (PO) as a new metaheuristic algorithm which derives its inspiration from the adaptive behaviors of Pyrrhura Molinae parrots to overcome current optimization challenges. The PO serves to optimize six PEMFC stack design variables for BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W. The research performs an extensive comparison between nine advanced algorithms to analyze their performance against PSO, DE, WOA, Rabbit Optimization Algorithm (ROA), Flamingo Herd Optimization (FHO), Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) and Bat Algorithm (BA). The objective function Sum of Squared Error (SSE) for stack voltage is minimized using different algorithms for comparative analysis. Simulation results for I–V and V–P characteristics aligned closely with experimental data under varying temperature and pressure conditions. PO achieved the lowest Mean SSE values across all cases, with values of 0.025519, 0.275211, 0.242413, 0.102915, 0.148632, and 0.283774 for the BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W stacks, respectively. Additionally, PO demonstrated the fastest runtime (RT) in all cases, with values as low as 0.116855 s for the Horizon H-12 stack. The results indicate that PO delivers better performance than existing algorithms because it reaches the lowest Sum of Squared Error for stack voltage outputs across every test scenario. I–V and V–P characteristic simulations match experimental results across different temperature and pressure values which proves the theoretical value and practical usage of PO in solving nonlinear optimization problems. The study demonstrates PO as a dependable optimization method which improves PEMFC design processes while enhancing operational reliability through future research that includes real-time control and algorithm combination and system scalability.