Perforated cold-formed steel (PCFS) beams are increasingly used in constructional industry and become popular due to their advantages and economic benefit. Apart from the light weight, the openings in PCFS beams can also be used for services such as pipe utilities to release more building space. However, beams with web perforations may collapse as the result of web-distortional buckling or flange/web distortional buckling due to the web weakness caused by perforations. In this paper a study on the flange/web distortional buckling of PCFS beams is presented. An analytical solution to determine the critical stress of flange/web distortional buckling of PCFS beams is derived by using energy method. To demonstrate the analytical solution, finite element analysis is also carried out. The finite element analysis results show that the analytical solution provided can give good and reliable prediction for the flange/web distortional buckling of PCFS beams.

The durability degradation of reinforced concrete was mainly caused by chloride ingress. Former studies have used component parameters of concrete to predict chloride diffusion by machine learning (ML), but the relationship between microstructure and macroparameter of concrete need to be further clarified. In this study, multi-layer perceptron (MLP) and support vector machine (SVM) were used to establish the prediction model for chloride diffusion coefficient in concrete, especially for the solid waste concrete. A database of concrete pore parameters and chloride diffusion coefficients was generated by the algorithm based on the Gaussian mixture model (GMM-VSG). It is shown that both MLP and SVM could make good predictions, in which the data using the normalization preprocessing method was more suitable for the MLP model, and the data using the standardization preprocessing method was more adapted to the SVM model.