This paper presents a novel numerical procedure based on the combination of an edge-based smoothed finite element (ES-FEM) with a phantom-node method for 2D linear elastic fracture mechanics. In the standard phantom-node method, the cracks are formulated by adding phantom nodes, and the cracked element is replaced by two new superimposed elements. This approach is quite simple to implement into existing explicit finite element programs. The shape functions associated with discontinuous elements are similar to those of the standard finite elements, which leads to certain simplification with implementing in the existing codes. The phantom-node method allows modeling discontinuities at an arbitrary location in the mesh. The ES-FEM model owns a close-to-exact stiffness that is much softer than lower-order finite element methods (FEM). Taking advantage of both the ES-FEM and the phantom-node method, we introduce an edge-based strain smoothing technique for the phantom-node method. Numerical results show that the proposed method achieves high accuracy compared with the extended finite element method (XFEM) and other reference solutions.

This paper presents a novel approach for typhoon track prediction that potentially impacts a region using ensemble k-Nearest Neighbor (k-NN) in a GIS environment. In this work, the past typhoon tracks are zonally split into left and right classes by the current typhoon track and then grouped as an ensemble member containing three (left-center-right) typhoons. The proximity of the current typhoon to the left and/or right class is determined by using a supervised classification k-NN algorithm. The track dataset created from the current and similar class typhoons is trained by using the supervised regression k-NN to predict current typhoon tracks. The ensemble averaging is performed for all typhoon track groups to obtain the final track prediction. It is found that the number of ensemble members does not necessarily affect the accuracy; the determination of similarity at the beginning, however, plays an important key role. A series of tests yields that the present method is able to produce a typhoon track prediction with a fast simulation time, high accuracy, and long duration.

A series of earthquake events give impetus to research on the ductile fracture behavior of steel materials. In the last decades, many fracture models have been developed and utilized in the mechanical or aerospace engineering. Nevertheless, very little application to structural members used in the construction industry has been made due to the lack of a suitable model for the fracture behavior of constructional steel. This paper presents the experimental and finite element (FE) technique to predict ductile fracture in mild carbon structural steel (SS275) sheets, which has been widely used in building structures. The post-necking true stress–strain responses were successfully estimated using the weighted-average method. The Bao and Wierzbicki (BW) model, which requires only two model parameters, was selected for the identification of fracture locus. Each model parameter was calibrated from uniaxial tension and in-plane shear specimens with the aid of digital image correlation (DIC) and finite element analysis. Fracture simulation was then performed and validated based on the experimental results of the specimens under combined tension and shear stress state.

Today, the world is experiencing a tremendous catastrophic disaster that can lead to potential environmental damage. However, awareness of how to deal with this catastrophic situation still remains very low. One of the most critical issues in disaster response is assigning disaster victims to the best emergency shelter location. This article reviews various existing studies to develop a new approach to determining emergency shelter locations. There are four evaluation criteria that are reviewed: optimization objective, decision variable, methodology, and victim identification. From the investigation, there are two major evaluations that can be further developed. In terms of decision variables, most of the previous research applies direct distance (Euclidean Distance) in the analysis process. However, the application of travel distance can represent a real evacuation process. Another interesting point is the victim identification process. Recent research applies grid-based partitioning and administrative-based partitioning. However, this method leads to a bias in the assignment process. This article recommends the application of K-Means clustering method as one of the unsupervised machine learning methods that is rapidly developing in many engineering fields. For better understanding, an example of K-Means clustering application is also provided in this article. Finally, the combination of travel distance and K-Means clustering will be proposed method for any further research.