Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly but also time-consuming. Moreover, the impact of other parameters cannot be easily seen in the behavior of the connectors. This paper aims to investigate the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC). To generate the required data, an experimental project was conducted. Dimensions of the channel connectors and the compressive strength of concrete were adopted as the inputs of the model, and load and slip were predicted as the outputs. To evaluate the ANN-PSO model, an ANN model was also developed and tuned by a backpropagation (BP) learning algorithm. The results of the paper revealed that an ANN model could properly predict the behavior of channel connectors and eliminate the need for conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices.

In the context of green building and sustainable urban development, understanding the mechanical behavior of structural components like concrete-filled steel tube (CFST) columns is crucial due to their improved load-bearing capacity, energy efficiency, and optimized material usage, which enhance structural resilience and sustainability. This research addresses the complex development of confining stress and its impact on the concrete core (CC) behavior within these columns, which are essential for urban infrastructure. Through extensive numerical studies, this study proposes a model to estimate the confining stress in axially loaded CFST short columns. Study findings reveal that the confinement effectiveness is influenced by variables such as compressive strength (CS) of the concrete, cross-sectional shape, and depth-to-wall thickness percentage. Additionally, the confinement is also significantly affected by the yield strain of steel ε_y/ε_c to the peak strain of unconfined concrete ε_c. A three-dimensional finite element model (FEM) was built for the simulation of the columns’ nonlinear behavior and was rigorously validated against experimental data. This model aids in the design and implementation of more efficient and resilient urban structures, supporting the principles of sustainable construction. The study underscores the importance of structural integrity in sustainable urban development and provides valuable insights for improving the design of green building materials.

This research evaluated the effect of different moisture conditions on the ductility, geotechnical and microstructural characteristics of the improved clayey subgrade with various lime contents and geogrid layers. The results of more than 120 tests showed that the combined application of lime and geogrid to the clayey subgrade significantly enhanced the ductility and geotechnical characteristics by increasing the moisture content up to an optimal value. It was observed that higher water pressure led to the separation of the geogrid layers from the improved subgrade; thus, the uniformity, consistency, and geotechnical properties were significantly reduced. Microstructural analysis showed that increasing the amounts of moisture more than the optimal values caused more voids which led to a substantial decrease in the geotechnical properties as well as the physical bonding between geogrid layers and lime-stabilised soil. Finally, a simple prediction model was developed to estimate the geotechnical characteristics of the improved clayey subgrade.