The integration of digital technologies with Artificial Intelligence could serve as a strategic approach to achieving the goals set by the European Union, mainly concerning sustainability, carbon emission reduction, and digitalization in the construction sector. In this regard, this paper aims to examine the major trends in the application of AI integrated with digital technologies to boost the environmental sustainability of the built environment throughout its life cycle. A systematic literature review was conducted, in accordance with the PRISMA guidelines, inspecting the Scopus database from 2015 to 2025. After having applied specific exclusion and inclusion criteria, 102 studies have been examined to identify key trends and transformative innovations enhancing sustainable approaches for the built environment. The results have been systematized based on the phases of the building life cycle which are impacted most by AI-powered digital technologies, and on sustainability areas that are attracting the greatest attention. The main research gaps are identified in the limited exploration of renovation and end-of-life phases of the life cycle, in the lack of technologies interoperability, in data complexity and quality issues, in a lack of cost-effective solutions, and in limited regulation and standardization.

Most electric energy consumption in the building sector is provided by fossil fuels, leading to high greenhouse gas emissions. However, the increasing need for sustainable infrastructure has triggered a significant trend toward smart buildings, which enable optimal and efficient resource usage. In this context, accurate mid-term energy load forecasting is crucial for energy management. This study proposes a hybrid forecasting model obtained through the combination of machine learning (ML) and deep learning (DL) approaches designed to enhance forecasting accuracy at an hourly granularity. The hybrid two-layer architecture first investigates the model’s performance individually, such as decision tree (DT), random forest (RF), support vector regression (SVR), Extreme Gradient Boosting (XGBoost), FireNet, and long short-term memory (LSTM), and then combines them to leverage their complementary strengths in a two-layer hybrid design. The performance of these models is assessed on smart building energy datasets with weather data, and their accuracy is measured through performance metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared ($R^2$). The collected results show that the XGBoost outperformed other ML models. However, the hybrid model obtained by combining FireNet and XGBoost models delivers the highest overall accuracy for the performance parameters. These findings highlight the effectiveness of hybrid models in terms of prediction accuracy. This research contributes to reliable energy forecasting and supports environmentally sustainable practices.

Purpose: The purpose of this research is to determine a comprehensive coursework for teaching quality management in the universities of developing countries i.e. Pakistan pertinent to the construction industry. Additionally, a framework was also developed based on the results.Design/methodology/approach: Factors and course contents of quality management are extracted through a content analysis of the published literature. Further, a questionnaire survey is conducted involving 150 professionals to assess the requirements of the industry and academic professionals of the construction industry for effective quality management. Finally, for getting a more objective perspective, using quality function deployment (QFD) matrix a framework is developed.Findings: The results show the level of importance of each design requirement. We can identify the design requirements through which we can fulfill the needs and demands of the stakeholders. Contents like benchmarking, supply chain management, six sigma and other data analysis methods, costing and auditing of quality, customer satisfaction analysis, TQM: theory and practices and ISO certification processes and procedures should be included in any course program related to quality management pertinent to the construction industry. Along with these contents, workshops and seminars must be arranged to address the user requirement for increased leadership and communication skills.Originality/value: There is a lack of literature available on course content for quality management in the universities for the construction sector, therefore, a huge scope of future research in the field of quality management in the construction industry needs to be investigated. This research highlights the most significant quality management factors and course contents as per the requirement of industry professionals. Therefore, this study adds to the existing body of knowledge by developing a robust framework that will help decision-makers and top management to develop constructive quality management course work at university level that will help in producing qualified and trained quality professionals to the construction industry.