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This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.

This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.

This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.

This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.

Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks.

This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge.

Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing.

The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.