Finite element procedures for nonlinear structures in moving coordinates. Part II: Infinite beam under moving harmonic loads

International audienceThis paper presents a numerical approach to the stationary solution of infinite Euler-Bernoulli beams posed on Winkler foundations under moving harmonic loads. The procedure proposed in Part 1 [1], which has been applied to consider the longitudinal vibration of rods under constant amplitude moving loads in moving coordinates, is enhanced herein for the case of moving loads with time-dependent amplitudes. Firstly, the separation of variables is used to distinguish the convection component from the amplitude component of the displacement function. Then, the stationary condition is applied to the convection component to obtain a dynamic formulation in the moving coordinates. Numerical examples are computed with a linear structure to validate the proposed method. Finally, nonlinear elastic foundation problems are presented

Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique

Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 38% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses

The role of environmental, social, and governance responsibilities and economic development on achieving the SDGs: evidence from BRICS countries

Sustainable development goal (SDG) achievement has gained increasing trend due to the current economic uncertainty that demands the attention of scholars, practitioners, and regulators. Hence, the study examines the environmental, social, and governance (ESG) responsibilities and economic development such as economic growth, net national income and FDI on the SDG achievements in BRICS countries. The secondary data is considered for the study which was collected from various resources like SDG reports published by the united nation and World Bank Indicators (WDI) from 1991 to 2020. The current research has checked them without structural breaks stationarity using Dickey-Fuller (ADF) test, Phillips–Perron (PP) test, and Kwiatkowski–Phillips–Schmidt–Shin (KPSS), while stationarity with structural breaks has been examined using the ‘zivot-andrews’ test. The study also employed the ARDL technique to verify the association among the constructs. The findings revealed that ESG responsibilities, economic growth, net national income, FDI, and inflation positively correlate with SDG achievements in BRICS countries. This article provides help to the regulators while making policies related the SDG achievemen

Evaluating structural safety of trusses using Machine Learning

In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed framework. Firstly, several truss cases with different cross-sectional areas are generated by employing the Latin Hypercube Sampling method. Stresses inside truss members as well as displacements of nodes are determined through finite element analyses and obtained values are compared with design constraints. According to the constraint verification, the safety state is assigned as safe or unsafe. Members’ sectional areas and the safety state are stored as the inputs and outputs of the training dataset, respectively. Three popular machine learning classifiers including Support Vector Machine, Deep Neural Network, and Adaptive Boosting are used for evaluating the safety of structures. The comparison is conducted based on two metrics: the accuracy and the area under the ROC curve. For the two first examples, three classifiers get more than 90% of accuracy. For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately 98%. In terms of the area under the ROC curve, the comparative results are similar. Overall, the Adaptive Boosting model outperforms the remaining models. In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model

VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification

The increasing reliance of software projects on third-party libraries has raised concerns about the security of these libraries due to hidden vulnerabilities. Managing these vulnerabilities is challenging due to the time gap between fixes and public disclosures. Moreover, a significant portion of open-source projects silently fix vulnerabilities without disclosure, impacting vulnerability management. Existing tools like OWASP heavily rely on public disclosures, hindering their effectiveness in detecting unknown vulnerabilities. To tackle this problem, automated identification of vulnerability-fixing commits has emerged. However, identifying silent vulnerability fixes remains challenging. This paper presents VFFINDER, a novel graph-based approach for automated silent vulnerability fix identification. VFFINDER captures structural changes using Abstract Syntax Trees (ASTs) and represents them in annotated ASTs. VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models to extract structural features. We conducted experiments to evaluate VFFINDER on a dataset of 36K+ fixing and non-fixing commits in 507 real-world C/C++ projects. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109% in F1. Especially, VFFINDER speeds up the silent fix identification process by up to 47% with the same review effort of 5% compared to the existing approaches.Comment: Accepted by IEEE KSE 202

n-Gram-based text compression

We propose an efficient method for compressing Vietnamese text using n-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it into n-grams and then encodes them based on n-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Each n-gram is encoded by two to four bytes accordingly based on its corresponding n-gram dictionary. We collected 2.5 GB text corpus from some Vietnamese news agencies to build n-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total. In order to evaluate our method, we collected a testing set of 10 different text files with different sizes. The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods.Web of Scienceart. no. 948364