This study aimed to rigorously evaluate the impact of tuned mass dampers (TMDs) on structural response under seismic excitation. By strategically placing TMDs at various levels within the structures, the research sought to determine their effectiveness in mitigating structural movement. A single-degree-of-freedom (SDOF) system incorporating TMDs was utilized to model structures of 10, 13, and 16 stories, each configured with TMDs at different heights. The structures were subjected to near-fault earthquakes to assess the efficacy of TMDs in reducing structural response. The findings revealed significant enhancements in structural performance when TMDs were optimally positioned. Specifically, a 50% reduction in both acceleration and displacement, alongside a 65% decrease in maximum drift, underscored the effectiveness of TMD deployment. Furthermore, the study demonstrated that distributing multiple TMDs along the height of the structure provided superior drift control. Notably, positioning TMDs within the upper one-third of the structure yielded the most pronounced improvements in acceleration, displacement, and maximum drift. Finally, the research indicates that the strategic incorporation of TMDs can significantly enhance the seismic resilience of structures. The results highlight the substantial benefits of TMDs in optimizing acceleration, displacement, and drift, thereby affirming their critical role in seismic design and retrofitting strategies.

Non-destructive testing (NDT) is a crucial method for detecting damages in concrete structures. Structural damage can lead to functional changes, necessitating a range of damage detection techniques. Non-destructive methods enable the pinpointing of the location of the damage without causing harm to the structure, thus saving both time and money. Damaged structures exhibit alterations in their static and dynamic properties, primarily stemming from a reduction in stiffness. Monitoring these changes allows for the determination of the failure location and severity, facilitating timely repairs and reinforcement before further deterioration occurs. A systematic approach to damage detection and assessment is pivotal for fortifying structures and preventing structural collapse, which can result in both financial and human losses. In this study, we employ image processing to categorize damaged beams based on their crack growth and propagation patterns. We also utilize support vector machine (SVM) and k-nearest neighbor (KNN) methods to detect the type, location, and extent of failures in reinforced concrete beams. To provide context and relevance for the laboratory specimens, we will compare our findings to the results from controlled experiments in a controlled laboratory setting.

Shear strength prediction in FRP-bonded reinforced concrete beams is crucial for ensuring structural integrity and safety. In this extensive investigation, advanced machine learning algorithms are harnessed to achieve precise shear strength predictions for rectangular RC beams reinforced with FRP sheets. The aim of this research is to enhance the accuracy and reliability of shear strength estimation, providing valuable insights for the design and assessment of FRP-strengthened structures. The primary contributions of this study lie in the meticulous comparison of various machine learning algorithms, including Xgboost, Gradient Boosting, Random Forest, AdaBoost, K-nearest neighbors, and ElasticNet. Through comprehensive evaluation based on predictive performance, the most suitable model for accurately estimating the shear strength of FRP-reinforced rectangular RC beams is identified. Notably, Xgboost emerges as the superior performer, boasting an impressive R² value of 0.901. It outperforms other algorithms and demonstrates the lowest RMSE, MAE, and MAPE values, establishing itself as the most accurate and reliable predictor. Furthermore, a sensitivity analysis is conducted using artificial neural networks to assess the influence of input variables. This additional research facet sheds light on the critical factors shaping shear strength outcomes. The study, as a whole, represents a substantial contribution to advancing the development of accurate and dependable prediction models. The practical implications of this work are far-reaching, particularly for engineering applications in the realm of structures reinforced with FRP. The findings have the potential to transform the approach to the design and assessment of such structures, elevating safety, efficiency, and performance to new heights.