The volume of data generated by the construction industry has increased exponentially following an intense use of modern technologies. The data explosion thus lead towards the big data phenomenon which is envisioned to revolutionize the construction like never before. Like any other technologies, big data is a disruptive paradigm and inevitably will give impact to the construction industry. As the industry is refocusing towards an improved productivity, the appeal to embrace big data is certain given the value it offers. This certainly will benefit construction akin to the manufacturing and the retail industry alike. Nevertheless, a review of the literature suggested a limited coverage on the potential application of big data in construction as compared to other industries. This limits understanding of its potential, where the industry is seemingly unaware thus could not relate and extract its real value. Hence, this study aims to draw insights on the specific areas of construction big data research. The research objectives include: (1) to analyse the current extent of construction big data research; (2) to map out the orientation of the current construction big data research; and (3) to suggest the current directions of construction big data research. The qualitative method through a desk study approach has been carried out to attain the first two objectives. It involved a structured review process which covered articles from the online databases assisted by the Nvivo software. This resulted in the theoretical orientation which was conceptualized as: (1) project management; (2) safety (3) energy management; (4) decision making design framework and (5) resource management. The theoretical orientation discovered from the review process will form the basis to suggest the prospective directions of research on big data in construction. This exploration is substantial as a precursor to a much deeper study on big data. As big data is set to influence the industry, the finding made would be a catalyst for creating an awareness to support the development of big data for the construction industry.

Artificial intelligence (AI) is increasingly being adopted for decision support in sustainable building risk management, yet the trustworthiness of AI-supported sustainability risk decisions depends as much on data quality as on analytical capability. Poor data conditions can amplify sustainability risks by producing unreliable decision support, yet existing studies provide limited insights into which data quality dimensions should be prioritized to enable trustworthy AI outcomes. This study identifies and prioritizes the critical data quality dimensions for trustworthy AI-supported decisions in sustainable building risk management. A questionnaire survey was conducted of accredited sustainable building professionals and their expert judgements were analyzed through an Analytic Hierarchy Process (AHP). The findings reveal that system-dependent dimensions, particularly traceability and interoperability, are prioritized over intrinsic dimensions like accuracy and consistency. The findings suggest that trustworthy AI-supported sustainability decisions depend strongly on a verifiable data provenance, cross-system integration and interpretable outputs rather than data correctness alone. This study reframes data quality from a general prerequisite to a prioritized, context-sensitive construct underpinning trustworthy AI applications, extending data-driven decision theory in the sustainable building domain. Ultimately, a phased data governance approach is recommended to prioritize traceability and interoperability as the foundational conditions for construction organizations implementing trustworthy AI in sustainable building risk management.

An assessment of IJBES's performance since 2015 was presented in this communication using metrics data from Clarivate and the OJS Report Generator. Raw data were analyzed for the purpose of reporting to readers on the journal's performance using performance metrics available to the editor. Key performance metrics such as submissions, acceptance and rejection rates, and citation trends over time were reported and presented to the reader. It has been observed that ensuring balanced content and continuously working on a niche are among the priorities of the journal. It is also necessary to attract relevant and quality manuscripts among the authors to increase citations in other publications. Despite everything, the journal, which is relatively young, was able to withstand the initial test of time and improve its visibility in the scientific community.