Modular construction provides numerous benefits over traditional methods, especially when combined with digital technologies, offering a faster, safer, leaner, and more sustainable construction environment. This literature review follows the PRISMA method to assess recent advancements in digital-oriented modular construction towards a sustainable and climate-neutral built environment, identifying research trends and gaps based on three pillars: digital tools, building solutions, and environmental sustainability. This review examines the integration of digital technologies with modular construction methods, extending the analysis to circular and bioclimatic efforts, renewable energy sources, and passive building design strategies. While most articles focus on BIM uses, there is an increasing emphasis on IoT applications that leverage real-time data to achieve sustainability goals. However, no full-scale automated Digital Twin was found in this context. Additionally, Building Energy Modelling (BEM) and Life Cycle Assessment (LCA) tools are frequently discussed, reflecting the push for climate-friendly housing. Despite the interest in parametric and generative design, the integration of machine learning and artificial intelligence applications for sustainable modular construction strategies remains underexplored. Only a few papers acknowledged reaching nZEB requirements despite the great emphasis on passive building solutions and renewable energy sources that contribute to this goal. However, material circularity has yet to achieve its full potential for sustainable modular construction. Moreover, there is some interest in off-grid modular buildings, although further research should be undertaken to analyse the modular construction feasibility for sustainable off-grid communities. Furthermore, the findings highlight the potential of digitalisation in modular construction to enhance efficiency and ensure environmental sustainability within the Architecture, Engineering, and Construction (AEC) sector.

The Digital Twin (DT), as a real-time and data-connected virtual replica of a physical asset, introduces a new paradigm in the construction industry. To date, the use of DT in the construction phase has not been addressed sufficiently. Hence, this research studies the implementation of DT during the construction phase of projects to support general contractors’ decisions and operations. Starting from existing literature, a Construction Digital Twin (CDT) framework has been developed from a general contractor’s perspective and a case study was implemented as an application of the proposed CDT to validate the framework and demonstrate its benefits. In the selected project, the simulation of the construction operations in evaluating various “what-if” scenarios for optimum resource allocation and operation management proved the benefits of using a CDT in the construction phase of projects for general contractors. By implementing the proposed CDT framework, several impacts such as reduced costs, improved collaboration and information exchange and data-driven construction management can be anticipated.

This study identifies and classifies the root causes (i.e., the primary precursors) of cost overruns in oil and gas construction projects. Past studies generally focused on identifying the most immediate precursors of cost overruns and failed to address the need to identify their root causes. Specific root causes are inherent in projects with certain characteristics (e.g., projects in a particular domain or sector). Accordingly, research is needed to identify various cost overrun factors and their root causes in a context-specific manner. This study used content analysis to identify 38 recurrent cost overrun factors and 11 root causes in oil and gas construction projects. The cost overrun factors were classified based on their common root causes. The Delphi method was used to verify this classification. The evidence from 12 construction projects in the oil and gas sector that had faced cost overruns was used to verify the outcome of this study. The findings of the study will assist practitioners in mitigating the risk of cost overruns in oil and gas projects and meeting their budget goals.

With the growth of Building Information Modelling (BIM) applications in the Architecture, Engineering, Construction and Operation (AECO) industry worldwide, a tremendous volume of data has been generated, providing a unique opportunity to extract and utilise valuable information for various purposes. For instance, BIM authoring software usually records details of the design modelling process in log files. Data mining techniques and learning algorithms are utilised to discover knowledge from data and assist stakeholders with various applications such as predicting design commands, project bottleneck diagnosis, progress prediction, and discovering design social networks. This work presents a systematic review of the studies in the field of BIM-embedded knowledge discovery and analyses them from an application-based perspective. As a result, seven major applications of BIM-embedded knowledge were identified. It was revealed that most applications mainly use BIM log files compared to other file formats, which can limit their applicability in other application domains. The K-means algorithm was also deployed to cluster similar features used in the relevant studies. It turned out that Start time , User (ID) , Duration , and Command features are the frequently used attributes in BIM knowledge extraction applications. This study demonstrates the use of hidden knowledge in BIM models to enhance team efficiency and data-driven decision-making while also providing insights for researchers on existing applications and potential applications of BIM data mining and knowledge discovery for the AECO industry.