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.

Foam concrete (FC) serves as an efficient construction material that combines well thermal insulation and structural properties. The studies of material characteristics, including the mechanical, physical, rheological, and functional properties of lightweight concrete, have been conducted rigorously. However, a lack of knowledge on the design efficiency of reinforced FC (RFC) was found in current research trends, compared to reinforced lightweight aggregate concrete. Therefore, this paper presents a review of the performance and adaption in structures for RFC. According to the code specifications, the feasibility investigation was preliminarily determined in structural use through the summary for the mechanical properties of FC of FC’s mechanical properties. For reinforced concrete design, a direct method of reduction factors is introduced to design lightweight aggregate concrete, which is also suggested to be adapted into a lightweight FC design. It was found that flexural shear behavior is a more complex theoretical analysis than flexure. However, a reduction factor of 0.75 was recommended for shear, torsion, and compression; meanwhile, 0.6 for flexural members. Serviceability limit states design should be applied, as the crack was found predominant in RFC design. The deflection controls were recommended as 0.7 by previous research. Research on RFC’s compression members, such as a column or load load-bearing wall, were rarely found. Thus, further study for validating a safe design of RFC applications in construction industries today is highly imperative.

This paper presents a parametric study on single cold-formed channel lipped section subject to axial compression load. The aim of this study is to investigate the influences of yield strength and depth of the section towards the compression capacity of a thin channel section. The study started with the calculation of effective area of a channel section followed by the calculation of compression capacity in accordance to BS EN 1993-1-1. The capacity of the section is then checked with experimental results for validation purpose. Parametric study is conducted to investigate the relationship between the yield strength, section depth and compression capacity. It is concluded that increment in yield strength tend to reduce the effective area of a thin channel section. On the other hand, the increment in section depth has a very little influence to the effective area of a cold-formed channel lipped section.