José Penadés

Graph theory is a common topic that is introduced as part of the curricula of computing courses such as Computer Science, Computer Engineering, Data Science, Information Technology and Software Engineering. Understanding graphs is fundamental for solving many real-world problems, such as network routing, social network analysis, and circuit design; however, many students struggle to grasp the concepts of graph theory, as they often have difficulties in visualising and manipulating graphs. To overcome these difficulties, educational software can be used to aid in the teaching and learning of graph theory. This work focuses on the development of a Java system for graph visualisation and computation, called MaGraDa (Graphs for Discrete Mathematics), that can help both students and teachers of undergraduate or high school courses that include concepts and algorithms related to graphs. A survey on the use of this tool was conducted to explore the satisfaction level of students on a Discrete Mathematics course taken as part of a Computer Engineering degree at the University of Alicante (Spain). An analysis of the results showed that this educational software had the potential to enhance students’ understanding of graph theory and could enable them to apply these concepts to solve practical problems in the field of computer science. In addition, it was shown to facilitate self-learning and to have a significant impact on their academic performance.

Lightweight aggregate concrete (LWAC) is an increasingly important material for modern construction. However, although it has several advantages compared with conventional concrete, it is susceptible to segregation due to the low density of the incorporated aggregate. The phenomenon of segregation can adversely affect the mechanical properties of LWAC, reducing its compressive strength and its durability. In this work, several machine learning techniques are used to study the influence of the segregation of LWAC on its compressive strength, including the K-nearest neighbours (KNN) algorithm, regression tree-based algorithms such as random forest (RF) and gradient boosting regressors (GBRs), artificial neural networks (ANNs) and support vector regression (SVR). In addition, a weighted average ensemble (WAE) method is proposed that combines RF, SVR and extreme GBR (or XGBoost). A dataset that was recently used for predicting the compressive strength of LWAC is employed in this experimental study. Two different types of lightweight aggregate (LWA), including expanded clay as a coarse aggregate and natural fine limestone aggregate, were mixed to produce LWAC. To quantify the segregation in LWAC, the ultrasonic pulse velocity method was adopted. Numerical experiments were carried out to analyse the behaviour of the obtained models, and a performance improvement was shown compared with the machine learning models reported in previous works. The best performance was obtained with GBR, XGBoost and the proposed weighted ensemble method. In addition, a good choice of weights in the WAE method allowed our approach to outperform all of the other models.