Resumen Está firmemente demostrada la importancia de realizar mantenimiento preventivo en los edificios, para evitar que se degraden y aparezcan lesiones graves. También está demostrado que con el mantenimiento preventivo se ahorra dinero frente al mantenimiento correctivo. En el marco del mantenimiento cabe decir que, para realizar las inspecciones periódicas de los edificios, es de gran utilidad el poder cuantificar hasta qué punto las deficiencias existentes son graves o no, con objeto de facilitar la toma de decisiones y priorizar las intervenciones terapéuticas. De hecho, se han utilizado y utilizan numerosas escalas diferentes entre sí para valorar el grado de gravedad de los elementos constructivos. Pero no existe consenso común y estas escalas son diferentes entre sí según el estudio a que pertenezcan. Por ejemplo, en las diferentes normas ITE existentes en España se utilizan diferentes escalas y formas de valorar las deficiencias existentes y no hay consenso común en el método de valoración. El objetivo del presente artículo es proponer, en base a un largo y riguroso proceso metodológico, una escala que sirva para valorar el grado de gravedad de los daños en edificios, que pueda ser utilizada de manera generalizada. Abstract The importance of performing preventive maintenance on buildings is clearly demonstrated, in order to prevent them from deterioration and severe damages. It is also demonstrated that preventive maintenance saves money versus corrective maintenance. In the framework of maintenance, to make periodic inspections of the buildings is useful to quantify the extent to which deteriorations are severe or not, in order to facilitate decision making and prioritize interventions. To this purpose many scales have been used and are used to assess the severity of damage and deterioration of the building components. But it appears evident that there is not common consensus and these scales are different between them, according to the study they belong to. Everything referred shows the need to propose and validate a scale that serves to assess the degree of severity of construction elements in buildings, which is widely used. Therefore, the objective of this article is to propose, based on a long and rigorous methodological process, a scale that serves to assess the degree of severity of damage in buildings, which can be widely used.

In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties such as data quality, contextual information, and time lapses. We demonstrate the efficacy of our method through a case study in Barcelona, which permitted the use of advanced analysis methods such as windowed cross-correlation and peak picking. The resulting time series data can be used to determine traffic patterns and has potential uses in other smart city sectors, such as air quality, energy efficiency, and public safety. Interactive dashboards enable stakeholders to visualize and summarize key insights and patterns.

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an `Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.

EngiMath is a 3 ECTS online course in engineering mathematics, in seven different languages, and it is the main and practical output of the ERASMUS+ project entitled “Mathematics online learning model in engineering education” in which the authors were participating. The course is integrated with Learning Management Systems such as Moodle and it is compatible with other platforms using Learning Tools Interoperability. Once the project is finished, authors undertake, with the support of the Institute of Education Sciences at the Universitat Politècnica de Catalunya-BarcelonaTECH (UPC), an innovation project, EngiMath@UPC, with three practical objectives: a) to incorporate EngiMath into the teaching activity of the widest as possible range of students at UPC, b) to gather students and faculty feedback regarding the tracking of materials and their performance in the student training process, and c) to statistically analyze the data collected in order to validate and adjust the follow-up of the materials. In connection with the above mentioned objective b) a training course addressed to the math professors has been prepared at UPC. The paper introduces, on the one hand, the EngiMath course as an online open educational resource for the academic community benefit, and on the other hand, analyzes the valuable feedback given by the training activity participants. Details on the online course implementation as well as main conclusions will be presented and discussed throughout the document.