The construction industry is experiencing changes in its processes and work methods, and the advancement of new technologies in recent decades has led to a new concept known as Construction 4.0, coined in 2016 in Germany. Since its definition is still diffuse, it was deemed necessary to conduct a review on the publications in this field to grasp how this concept is being understood. For that purpose, a bibliometric analysis was conducted among 260 research articles using seven keywords. The results reveal that the number of publications is growing exponentially, with the USA, the UK, and China being leaders in this field; besides, four technologies are essential to understand Construction 4.0 at present time: 3D printing, big data, virtual reality, and Internet of Things. The results of this review suggest that further reviews should be conducted every 3 years to grasp the rapid evolution of Construction 4.0.

Contribution: To guide engineering educators on the complex path of understanding how to teach future engineers and how they can help engineering students to obtain the professional competencies required by frameworks and syllabus, based on didactic techniques that determine an orderly learning process. Background: University education is committed to change and adaptation to the current and new realities of the world. The framework for measuring the Accreditation Board for Engineering and Technology (ABET) outcomes and Conceive- Design-Implement-Operate (CDIO) syllabus have developed a list of competencies required by engineering students, encouraging universities to meet real-world needs. Intended Outcomes: Rethinking the role of engineering education and the purpose of education in the engineer's professional development, proposing a roadmap for engineering educators, helping them to define which teaching-learning techniques to apply to develop the competencies and skills that university and industry require from future engineers. Application Design: A relationship is shown between the competencies that today's engineers must have according to ABET criterion 3 (i.e., student outcomes), which are required for continuous accreditation of the engineering and CDIO syllabus, and the respective didactic techniques considered in this research. Findings: To develop competencies, students should be taught based on different teaching-learning techniques in higher education. A relationship is proposed for the engineering educator, showing them which didactic technique could be adequate to be applied in the classroom to develop in students the specific competencies established by ABET (SO1 to SO7) and CDIO (SL1 to SL4).

Products derived from trees have been used by mankind for thousands of years, where timber has a long tradition as an ecological construction material. There is currently an increasing trend in multi-storey timber buildings, because of the projected growth in the demand for housing in urban areas between now and 2050, along with the urgent need for a more sustainable and productive construction industry. The construction of these buildings is now possible thanks to the new advances in architecture, engineering, and construction (AEC) and the new technological developments around timber construction. Its industrialization requirements imply a paradigm shift for the construction industry, which requires, among other aspects, the early and collaborative integration of stakeholders in its design and construction process. According to this, the objective of this review article is to determine the main advances and limitations related to the design and construction of multi-storey timber buildings, categorizing them in aspects such as sustainability, engineering and construction sciences, and collaborative design. The methodology of this article was based on the review of 266 articles published in Web of Science (WoS), as indexed scientific journals, between 2017 and mid-2022, performing a comparative and cooccurrence analysis of the contents. The results evidenced that 73% of the articles showed advances and limitations corresponding to the engineering and construction sciences category, 23% to sustainability, and the remaining 4% to collaborative design. The main advances in the development of multi-storey timber buildings are related to seismic analysis, connections design, fire performance, and fire design. While the main limitations are related to social sustainability, the results are not conclusive due to the low number of publications that support them.

In the transition toward a more digital and data-driven construction industry, understanding how Artificial Intelligence (AI) and Building Information Modeling (BIM) are integrated is key to planning, delivering, and operating projects effectively. This review examines recent studies to identify usage patterns of AI and BIM. Searches were conducted on the Web of Science Core Collection from 2022 to 2025. After running a reproducible review protocol aligned with PRISMA 2020, which began with 1212 articles, and after a funneling process, 12 studies met the predefined eligibility criteria. In the present study, the synthesis was non-meta-analytic; instead, the information was analyzed by using standardized tabulation with a consistent format and compared using a two-level weighting scheme. The methodological approach combines full-text reading and descriptive coding with a reproducible weighting scheme that accounts for mentions per study and integrates them at the corpus level using open-source tools. The results show a strong focus on Deep Learning (DL), with a greater presence in Digital Twins (DT) and BIM Modeling (BIMM); Multidimensional BIM (4D/5D) appears as a secondary line, while the Common Data Environment (CDE) and Clash detection (CD) are sporadic. The coupling of DL-DT and DL-BIMM predominates. Simultaneously, Machine Learning (ML) provides explainable analysis on structured data, and Generative Adversarial Networks (GAN) and Automated Machine Learning (AutoML) with Machine Learning Operations (MLOps) act as enablers for data generation/adaptation and deployment with traceability. It is concluded that advancing metrics and shared datasets, especially for CDE and CD, along with developing reproducible workflows oriented toward MLOps, are key to scaling AI in real-world environments.