This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm’s competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study’s insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.

Structural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their propagation. Structural damages may arise from various factors, including natural phenomena and human activities. To address this, diverse applications have been developed to enable timely detection of such damages. Among these, vibration-based methods have received considerable attention in recent years. By leveraging advancements in computer processing capabilities, machine learning and deep learning algorithms have emerged as promising tools for enhancing the efficiency and accuracy of vibration-based SHM. This study focuses on the application of convolutional neural networks (CNNs) for the classification and detection of structural damage within a steel-aluminum building model. An experimental platform was devised and constructed to generate data representative of building damage scenarios induced by bolt loosening. Both the typical placement of sensors on each floor and the utilization of only one accelerometer were employed to understand the effect of scarcity of accelerometers. By subjecting the building model to controlled vibrations and environmental conditions, the response data from both sensor configurations were collected and analyzed to evaluate the effectiveness of the CNN approach in detecting structural damage under varying sensor deployment strategies. The findings demonstrate that the CNNs exhibited high accuracy in both damage classification and detection, even under scenarios with limited sensor coverage. Moreover, the proposed method proved effective in identifying structural damage within building structures.

In structural health monitoring (SHM), the scarcity of labeled data representing different structural states and structural configurations remains a key challenge for developing robust and generalizable deep learning models. Building upon a reported attention-enhanced star generative adversarial network with gradient penalty (AT-StarGAN-GP), this study examines its application to generating synthetic structural vibration signals that reflect both cross-structure domain differences and multiple localized structural state scenarios. The framework supports conditional translation across domains, learning from acceleration responses measured on a source structure to generate synthetic responses for a target structure under structural state conditions. The architecture integrates multi-type attention mechanisms, including self-attention, channel-attention, and spatial-attention, and evaluates four conditioning strategies: concatenation (Concat), feature-wise linear modulation (FiLM), adaptive instance normalization (AdaIN), and conditional instance normalization (CIN). A dual-conditional formulation based on joint location and structural state level is employed. The framework is evaluated using two small-scale physical bridge models, each instrumented at six joint locations, with structural state variations simulated at selected joints using three mass levels (no mass, low mass, and high mass). Evaluation across modal, temporal, distributional, and spectral metrics shows that CIN provides improved performance, achieving a modal assurance criterion of 0.894, frequency response assurance criterion of 0.872, the lowest distributional discrepancy (maximum mean discrepancy 0.0006), and an 86% pass rate in Kolmogorov–Smirnov tests. Attention mechanisms improve localization accuracy by 34.35% and reduce entropy by 6.98%, while self-attention reduces cross-domain Kullback-Leibler (KL) divergence by 24.5%. The AdaIN variant shows more consistent behavior under unseen-domain scenarios (MAC 0.8207–0.8405). All evaluated configurations retain over 94% of baseline performance under 25% noise. Five-fold cross-validation confirms training stability, and Friedman–Nemenyi testing indicates statistically significant separation from weaker baselines, although differences relative to mid-tier methods remain limited in some comparisons. Overall, the results indicate that the extended AT-StarGAN-GP framework can generate structurally consistent synthetic vibration responses across different structures and state conditions under controlled laboratory settings.

This paper presents AT-StarGAN-GP, an enhanced StarGAN architecture with gradient penalty and dual-attention mechanisms for structural health monitoring. The proposed model overcomes limitations of conventional GANs in structural vibration analysis by integrating temporal and channel attention to capture global temporal dependencies and local feature interactions. It employs a refined Wasserstein loss with gradient penalty and adaptive λ -scaling to stabilize training and balance multiple objectives. The model achieves high accuracy in modal frequency detection, producing nearly identical real and synthetic estimations across all modes. Damping ratio estimations show deviations below 5%, confirming precise structural response modeling. Frequency-domain and singular value decomposition (SVD) analyses yield mean magnitude-squared coherence (MMSC) values between 0.973 and 0.989, validating stability and fidelity. In terms of computational efficiency, AT-StarGAN-GP processes all 30 joints in 313.45 ms (∼10.45 ms per joint), maintaining real-time feasibility and outperforming CycleGAN (≈9 s) while matching Vanilla StarGAN performance. Although Eigen perturbation methods (Modal Assurance Criterion (MAC), Damage Localization Vector (DLV), Flexibility, Eigenvalue) are faster (0.25–12.67 ms), they lack generative capability essential for data recovery and nonlinear damage modeling. Sensitivity analysis shows optimal coherence (MMSC > 0.97) when λ -parameters are moderately weighted (10–15), and attention is fully enabled. Attention mechanisms significantly enhance temporal and inter-sensor spectral alignment, confirming AT-StarGAN-GP as a real-time, generative, and accurate solution for vibration-based SHM.

As is known, pervious concrete is commonly used in low-traffic-load applications. Therefore, pervious concrete is exposed to flexural loads throughout its economic life. To improve the flexural behavior of pervious concretes, fibers are the most common solution used by researchers. Hence, fiber dosages of 0.1%, 0.2%, and 0.3% (by volume of concrete) are selected for this study. The influence of increased cement content is also discussed. Single-sized limestone aggregates without any mineral admixture were used to produce pervious concretes. The mechanical and hydraulic properties of the pervious specimens were examined. Additionally, image analyses using both CT (Computed Tomography) and scanned sliced samples were conducted. In this study, the samples containing 0.3% polypropylene fiber by volume yielded the highest splitting tensile and flexural strength. Adding fiber reduced the porosity of the pervious concrete. Mixtures with the highest fiber content exhibited the lowest porosity and permeability coefficients. Also, the porosity obtained from image analyses correlates well with volumetric porosity results. The impact of fiber additives on the strength and permeability of pervious concrete has been explored only to a limited extent in existing literature. This study contributes original insights by assessing this impact through a comprehensive approach that examines mechanical, hydraulic, and microstructural aspects, utilizing both experimental methods and image analysis.