This paper describes the results of a model updating study conducted on a historical aqueduct, called Veziragasi, in Turkey. The output-only modal identification results obtained from ambient vibration measurements of the structure were used to update a finite element model of the structure. For the purposes of developing a solid model of the structure, the dimensions of the structure, defects, and material degradations in the structure were determined in detail by making a measurement survey. For evaluation of the material properties of the structure, nondestructive and destructive testing methods were applied. The modal analysis of the structure was calculated by FEM. Then, a nondestructive dynamic test as well as operational modal analysis was carried out and dynamic properties were extracted. The natural frequencies and corresponding mode shapes were determined from both theoretical and experimental modal analyses and compared with each other. A good harmony was attained between mode shapes, but there were some differences between natural frequencies. The sources of the differences were introduced and the FEM model was updated by changing material parameters and boundary conditions. Finally, the real analytical model of the aqueduct was put forward and the results were discussed.

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.

This paper presents the performance of reinforced concrete beam‐column connections strengthened with carbon fiber‐reinforced plastic (CFRP) sheets externally and steel bars internally. The work emphasized joint behavior under reverse cyclic loading to assess deformation capacity and strength. The study aims the existing buildings designed inadequately in joint sections. For experimental analysis, an exterior joint application was used. In strengthening, four different strengthening configurations were used. Each configuration was designed to illustrate the effect of strengthening at joint sections of the samples. Cyclic performance of the retrofitted samples compared to the control sample satisfies the current building code requirements. Test results indicated that bearing capacity and ductility of the connections were closely related to the original condition of the element and strengthening application.

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.