V. Plevris and A. Ahmad, "Deriving Analytical Solutions Using Symbolic Matrix Structural Analysis: Part 2 – Plane Trusses", Heliyon, 11(4), Article ID e42372, 19 pages (DOI: 10.1016/j.heliyon.2025.e42372), 2025.

Abstract:
This study extends the use of symbolic computation in matrix structural analysis to plane trusses, expanding on previous work that focused on continuous beams. We present a fully open-source MATLAB program, available on GitHub, that performs symbolic analysis of 2D trusses subjected to point loads, applicable to any truss configuration. Using the Symbolic Math Toolbox, the program derives closed-form analytical expressions for displacements, support reactions, and axial forces, providing deeper insight into structural behavior. A key advantage of the symbolic approach is its ability to perform sensitivity analysis efficiently by computing partial derivatives of structural responses with respect to input parameters. This feature enhances design exploration and optimization by allowing direct evaluation of parameter influences. Moreover, the framework is highly scalable, capable of generating symbolic solutions even for large-scale truss structures, something previously unattainable using traditional methods due to computational limitations. The tool serves both engineering practice and education, offering clear insights into parameter relationships and strengthening conceptual understanding in structural mechanics. To ensure accuracy, the symbolic results were rigorously validated against two commercial finite element software programs and results from the literature, with complete agreement. These validations confirm the reliability, scalability, and general applicability of the proposed methodology.

V. Plevris, "Assessing Uncertainty in Image-Based Monitoring: Addressing False Positives, False Negatives, and Base Rate Bias in Structural Health Evaluation", Stochastic Environmental Research and Risk Assessment (DOI: 10.1007/s00477-024-02898-7), 2025.

Abstract:
This study explores the limitations of image-based structural health monitoring (SHM) techniques in detecting structural damage. Leveraging machine learning and computer vision, image-based SHM offers a scalable and efficient alternative to manual inspections. However, its reliability is impacted by challenges such as false positives, false negatives, and environmental variability, particularly in low base rate damage scenarios. The Base Rate Bias plays a significant role, as low probabilities of actual damage often lead to misinterpretation of positive results. This study uses both Bayesian analysis and a frequentist approach to evaluate the precision of damage detection systems, revealing that even highly accurate models can yield misleading results when the occurrence of damage is rare. Strategies for mitigating these limitations are discussed, including hybrid systems that combine multiple data sources, human-in-the-loop approaches for critical assessments, and improving the quality of training data. These findings provide essential insights into the practical applicability of image-based SHM techniques, highlighting both their potential and their limitations for real-world infrastructure monitoring.

G. Papazafeiropoulos and V. Plevris, "OpenSeismoMatlab: New Features, Verification and Charting Future Endeavors", Buildings, 14(1), Article ID 304, 31 pages (DOI: 10.3390/buildings14010304), 2024.

Abstract:
To facilitate the precise design of earthquake-resistant structures, it is imperative to accurately evaluate the impact of seismic events on these constructions and predict their responses. OpenSeismoMatlab, a robust, free ground motion data processing software, plays a pivotal role in this endeavor. It empowers users to compute a wide array of outcomes using input acceleration time histories, encompassing time histories themselves, as well as linear and nonlinear spectra. These capabilities are instrumental in supporting structural design initiatives. This study provides a comprehensive exposition of the latest version (v 5.05) of OpenSeismoMatlab. It delves into intricate facets of the software, encompassing a detailed exploration of the input and output variables integral to each operational category. Comprehensive calculation flowcharts are presented to elucidate the software’s organizational structure and operational sequences. Furthermore, a meticulous verification assessment is conducted to validate OpenSeismoMatlab’s performance. This verification entails a rigorous examination of specific cases drawn from existing literature, wherein the software’s outcomes are rigorously compared against corresponding results from prior studies. The examination not only underscores the reliability of OpenSeismoMatlab but also emphasizes its ability to generate outcomes that closely align with findings documented in the established body of literature. Concluding the study, the paper outlines potential directions for future research, shedding light on avenues where further development and exploration can enhance the utility and scope of OpenSeismoMatlab in advancing seismic engineering and structural design practices.

Keywords:
OpenSeismoMatlab; earthquake; seismic design; nonlinear spectra; pulse; resampling.