Dimensions of Interoperability in the AEC Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Often cited as a major barrier to the seamless exchange of data and information among project team members evolving in the architecture, engineering, and construction (AEC) industry, technological interoperability has been the focus of many ongoing research efforts within the AEC field. In other knowledge fields, such as information systems (IS) and military research, the interoperability construct has evolved beyond the purely technological domain to encompass multiple dimensions. Within the AEC industry, these dimensions of interoperability have yet to take root. This paper introduces a conceptual framework that develops the interoperability construct across multiple dimensions. The framework defines emerging collaborative project delivery systems within the AEC industry by relating the technological, organization and procedural dimensions and situating them within the contextual dimension. The framework is underpinned by an information processing systems approach to project delivery in the AEC industry. Based on a two-part systematic literature review, a rigorous and structured process aimed at answering a very specific and targeted question within a given field, this paper presents the conceptual framework and discusses the various dimensions of interoperability. The paper concludes by presenting opportunities for future research through gaps identified in the literature. It is believed that by adopting this broader view of the interoperability construct in the AEC industry, the deployment of seamless collaborative project delivery systems and emerging technologies and processes, such as Building Information Modeling (BIM) will be better informed and structured and thus more effective and efficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it