Decision Support Tool for Enhancing the Economic Impact of Construction using Offsite Systems
Bibliographic record
Abstract
The construction industry is facing productivity stagnation across the globe, and several hypotheses to explain this phenomenon exist, most often associated with low use of digitisation, skills shortages and unpredictable market trends. Yet the economic context is more multi-faceted and the different economic drivers are closely interconnected, however research typically addresses each in isolation. This research paper aims to decipher the influence of key economic drivers on the economy, using the UK context as a barometer for international trends, and proposes how offsite technologies with varying levels of value added in the factory may be utilised to mitigate economic challenges. To achieve this, two key objectives were set: firstly to propose a theoretical framework supported by previous research on decision-support for offsite systems; and secondly to develop an infographic style visualisation to express this theoretical framework as an interactive tool. Following from the dual nature of the research objectives, the research employs a mixed methodology rooted in qualitative research techniques dealing with complex subject matters. A robust literature review with associated subsequent framework proposal was the first stage of the research, followed by data visualisation experiments and tools usability trials using focus group methods to collect preliminary data. The results indicated that there was existing evidence upon which to base the theoretical framework for enhancing economic impact using offsite solutions, with six key strands: sustainability, culture, human capital, productivity, digitisation and regulatory. However, the visualisation of this framework into an interactive tool was a novel concept and required an inter-disciplinary approach for data representation. Overall, this paper presents a unique qualitative tool, which can be utilised to simplify the concepts behind offsite construction and the potential economic impact of using offsite. The tool is aimed at decision-makers and stakeholders who may not be familiar with modern methods of construction, and those who would like to prioritise offsite benefits in a given scenario.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".