V. Plevris and A. Ahmad, “Deriving analytical solutions using symbolic matrix structural analysis for continuous beams”, Scientific Reports, 15, Article ID 15897 (DOI: 10.1038/s41598-025-98023-x), 2025.

Abstract:
This study investigates the use of symbolic computation in Matrix Structural Analysis (MatSA) for continuous beams, using the MATLAB Symbolic Math Toolbox. By employing symbolic MatSA, analytical expressions for displacements, support reactions, and internal forces are derived, offering deeper insights into structural behavior. This approach facilitates efficient and scalable sensitivity analysis, where partial derivatives of outputs concerning input parameters can be directly computed, enhancing design exploration. The development includes an open-source MATLAB program, hosted on GitHub, enabling symbolic analysis of continuous beams subjected to point and uniform loads. This approach is valuable for both engineering practice and pedagogy, enriching the understanding of structural mechanics and aiding in education by illustrating clear parameter relationships. The program supports deriving influence lines and identifying maximum response values.

V. Plevris, L. Hadji and H. Ait Atmane, “An n-order refined plate theory for Bending and Buckling of Functionally Graded Polymer Composite Plates Reinforced with Graphene Nanoplatelets”, Advances in Nano Research, 19(1), pp, 41-51 (DOI: 10.12989/anr.2025.19.1.041), 2025.

Abstract:
This study investigates the bending and buckling behavior of functionally graded multilayer graphene nanoplatelet (GPL)/polymer composite plates using an n-order refined plate theory. The theory introduces a higher-order polynomial displacement field that ensures variational consistency and eliminates the need for shear correction factors. In this formulation, shear stresses vary parabolically through the plate thickness, and stress-free conditions are satisfied at both the top and bottom surfaces, resulting in improved accuracy compared to conventional plate theories. A key innovation of this work lies in the layer-wise variation of GPL weight fractions, enabling the design of functionally graded nanocomposites with both uniform and non-uniform reinforcement patterns-specifically, UD, FG-O, FG-X, and FG-A. While most existing studies are limited to uniformly distributed GPLs or rely on lower-order theories, this study addresses these limitations by proposing an analytically tractable higher-order model that can accurately capture shear deformation effects and by systematically analyzing the mechanical influence of different GPL distribution patterns. This dual advancement fills an important gap in the literature, particularly in understanding the performance of non-uniformly graded nanocomposites under bending and buckling. The effective Young's modulus is predicted using the Halpin-Tsai micromechanics model, and the rule of mixtures is used to determine the effective Poisson's ratio and mass density. Analytical solutions for static deflection and buckling are derived for simply supported plates using the Navier solution technique. The results show that non-uniform GPL distributions, particularly FG-X and FG-O, significantly enhance bending stiffness and buckling resistance by concentrating reinforcement near high-stress regions. Additionally, increasing the GPL weight fraction and optimizing GPL geometry further improve structural performance. This study offers new insights into the tailored design of functionally graded nanocomposite plates and provides practical guidance for lightweight, high-performance structural components in aerospace, automotive, and civil engineering applications.

H. Hosamo, V. Plevris, D. Kraniotis and C.N. Rolfsen, “Can Quantum Computing Surpass Classical Algorithms in Optimizing Building Performance? A Benchmark Study with 15000 Simulations”, Energy & Buildings, 346(1), Article ID 116156 (DOI: 10.1016/j.enbuild.2025.116156), 2025.

Abstract:
Optimizing building performance is essential for enhancing energy efficiency and occupant comfort. This study evaluates the applicability of quantum computing–based optimization methods in the Architecture, Engineering, and Construction (AEC) industry by comparing the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) with classical multi-objective optimization algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). A dataset of 15,000 building simulations was used to train an Extreme Gradient Boosting (XGBoost) model for predicting total energy consumption (kWh/m²/year) and Predicted Percentage of Dissatisfied (PPD) occupants. These predictions were then used in the optimization phase. NSGA-II produced the best trade-offs, achieving energy consumption between 17.84 and 19.84 kWh/m²/year and PPD below 5.2 %, with strong diversity and convergence. QAOA executed faster (0.54 min) than NSGA-II (18.9 min) but resulted in higher energy values (31.85–55.62 kWh/m²/year) and weaker solution quality. Quantum Annealing completed in 0.37 min but returned clustered solutions near 45.88 kWh/m²/year. While the current limitations of quantum methods constrain their effectiveness, the findings indicate their potential as fast solvers in future building performance optimization workflows, particularly as hardware and algorithmic capabilities mature.