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

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

What Shapes Undergraduate Students’ Satisfaction in Unstable Learning Contexts?

This paper investigates what determinants, and to what extent, they influence students’ satisfaction in unstable learning contexts. Using a national-scaled sample of Vietnamese HEIs with a sound theoretical background, we find that regardless of instabilities from external shocks, the key factors that shape students’ satisfaction are fixed by traditional norms (self-efficacy, infrastructure, lecturer) rather than occasional factors occurring from each event. We find in particular that self-efficacy is the most influential factor for students’ satisfaction and friendship is the most prominent element that enhances students’ self- efficacy. Overall, this paper enriched the literature on student satisfaction, especially during unstable contexts. Thus, it has important implications for educators and HEIs stakeholders in management planning in the time to come

WTC2005-64192 INVESTIGATION ON THE WEAR OF WELL-TUBING IN THE OIL EXPLOITATION

ABSTRACT In this paper, the results of investigation of wear of well-tubing with the sea depth from 3000 to 5000 meters are presented. The theory of erosion for tubing is established basing on the flow of exploited products and the principles of erosion and corrosion, for calculating the wear of thickness of tubing and the influence of environment and exploited oil products flow (pressure, velocity, temperature etc.). The long-life of tubing is determined for the maintenance service. Various proposals for increasing effectiveness of tubing exploitation are presented as materials changing, manufacturing, improving etc

The influences of the number of concrete dowels to shear resistance based on push out tests

To reduce the depth of floor-beam structures and to save the cost of headed-shear studs, many types of shallow composite beam have been developed during the last few years. Among them, the shallow-hollow steel beam consists of web openings, infilled with in-situ concrete (named concrete dowel) has been increasingly focused recently. In this new kind of structure, this concrete dowel plays an important role as the principal shear connector. This article presents an investigation on the shear transferring mechanism and failure behavior of the trapezoid shape concrete dowel. An experimental campaign of static push-out tests has been conducted with variability in the number of web openings (WOs). The results indicate that the mechanical behavior of concrete dowel could be divided into crushing, compression, and tension zones and exhibits brittle behavior. The longitudinal shear resistance and specimen's stiffness are strongly affected by the number of considered WO