International Concrete Abstracts Portal

This paper discusses the seismic performance of precast coupled structural walls with the influence of connections and their location. Full-scale quasi-static tests were conducted on the coupled structural walls by varying the number of connections. The test results show that the number of connections and their position along the height of the coupled wall significantly influence the lateral strength, stiffness, energy dissipation, and failure modes. Walls with two connections seem to improve the strength and hysteretic response, exhibiting superior cyclic performance. Increasing the number of connections improves the initial stiffness to a certain extent, but the designs are expensive. Walls with connections closer to lateral loading lines exhibit vulnerability, requiring design to optimize energy dissipation and crack control. Connections with over-strength may need to be avoided as they may not increase the energy dissipation under earthquake loading. The outcomes of the study help in designing precast systems with better seismic resilience, good ductility, and ease of replacement after an earthquake hits the system.

This study employs advanced nonlinear finite element modeling to investigate Interface Shear Transfer (IST) behavior in RC connections, a crucial factor for bridge durability and safety. The research examines shear transfer mechanisms at the interface between precast girders and cast-in-place deck segments through three experimental methods: beam, push-off, and Iosipescu four-point bending tests. FE simulations evaluated stress distributions, IST capacity, and failure mechanisms. Validation against experimental data shows that the Iosipescu test provides the most accurate representation of IST behavior, exhibiting a stress distribution error margin of only 1%, closely aligning with observed failure patterns. In contrast, the push-off test showed a 30% deviation from empirical data, indicating reduced accuracy in predicting real-world IST behavior. These findings highlight the importance of incorporating the Iosipescu test into IST evaluation protocols, as its greater precision enhances design methodologies for concrete bridges, reduces structural failure risks, and informs future updates to IST-related codes.

Linear structural analysis is the method of choice commonly used by practicing engineers to support the seismic design of a structure. The structural models are developed in commercial software and incorporate stiffness modifiers, which lower the stiffness of the members, in recognition of all the sources of flexibility that occur upon cracking of the concrete. This paper describes a mechanics-based model to compute the stiffness modifiers for columns with a circular cross-section. The mechanics-based model accounts for five modes of deformation observed. Calibration of this model was performed with a database of tests reported in the literature on twenty-two circular-section columns that exhibited ductile response. The paper ends by describing a simplified method for use in design. The mechanics-based model and the design method yield an effective column lateral stiffness that closely aligns with the values obtained from the column database.

Despite advancements in machine learning (ML) that have boosted structural performance prediction, current ML models can still struggle to generalize to unseen situations, leading to performance degradation. This vulnerability arises from their overreliance on data, neglecting established engineering principles like mechanical priors. Models trained on specific data distributions can suffer significant accuracy degradation when encountering inputs that fall outside those distributions. To overcome the limitations of data-driven models with unseen data, A mechanics-guided Gaussian process (MGGP) for accurate prediction of shear strength in reinforced-concrete (RC) beams is proposed. The complex variation of shear strength in RC beams was captured using a Gaussian process (GP) model with a mean function derived from mechanical principles and a hybrid kernel to account for inherent prediction variability. This combination allows for accurate prediction of shear strength while considering the underlying physical mechanisms. This approach leverages domain knowledge from mechanics by incorporating a relevant design equation into the mean function of a GP model. This integration significantly enhances the model's ability to predict shear strength by capturing the underlying physical principles governing the shear strength. Cross-validation studies have shown that the MGGP offers consistent performance compared to traditional GPs in predicting the shear strength of RC beams.