ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Characterization of the information system integrated to the construction project management systems

The construction industry wields significant influence in the economies of various countries. However, compared to sectors like manufacturing and aeronautics, it has lagged in terms of digitalization of processes and project management advancement. This study aims to explore how the integration of Lean principles, Building Information Modeling (BIM), and Project Lifecycle Management methodologies within an information system can enhance decision-making in construction project management as a complex environment. A comprehensive literature review was conducted to establish a conceptual framework and gather necessary information for designing an information system. The design was based on the viable systems model and the soft systems methodology, from a systemic perspective that encourages the synergistic interaction of these methodologies. The resulting abstract model would facilitate a comprehensive understanding of the interconnectedness of these methodologies, emphasizing collaborative work environments for efficient information management. This approach aims to replace the current isolated application of each of those methodologies and promises improved project management performance.

Analysis of Interurban Mobility in University Students: Motivation and Ecological Impact

The management of mobility in large cities is a complex issue of great interest due to its economic, social, and environmental impact. In this work, the interurban mobility of engineering students from two campuses of the University of Seville is studied. Specifically, this work carries out an analysis of the preferences of students in terms of mobility to their study centres and determines the environmental impact of such mobility in terms of kg of CO2 per student. Three constructs can be found to describe the motivation for their choice of transport: those related to comfort and speed, those related to sustainability and price, and those related to safety. Based on the responses obtained, groups of students are established that enable the design of specific actions in accordance with each of the profiles. From the analysis of the results obtained, recommendations are made for policymakers, and a reflection is given on the impact of the COVID-19 pandemic on this issue.

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented.

Early Prediction of a Team Performance in the Initial Assessment Phases of a Software Project for Sustainable Software Engineering Education

Software engineering is a competitive field in education and practice. Software projects are key elements of software engineering courses. Software projects feature a fusion of process and product. The process reflects the methodology of performing the overall software engineering practice. The software product is the final product produced by applying the process. Like any other academic domain, an early evaluation of the software product being developed is vital to identify the at-risk teams for sustainable education in software engineering. Guidance and instructor attention can help overcome the confusion and difficulties of low performing teams. This study proposed a hybrid approach of information gain feature selection with a J48 decision tree to predict the earliest possible phase for final performance prediction. The proposed technique was compared with the state-of-the-art machine learning (ML) classifiers, naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), simple logistic regression (SLR), repeated incremental pruning to produce error reduction (RIPPER), and sequential minimal optimization (SMO). The goal of this process is to predict the teams expected to obtain a below-average grade in software product development. The proposed technique outperforms others in the prediction of low performing teams at an early assessment stage. The proposed J48-based technique outperforms others by making 89% correct predictions.