Application of System Dynamics in Construction Engineering and Management: Content Analysis and Systematic Literature Review
Why this work is in the frame
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Bibliographic record
Abstract
Researchers have increasingly used system dynamics (SD) as a modelling tool to understand the behaviour of systems with varying degrees of dynamism and complexity. SD has had a particularly significant impact in improving system representation, modelling, and abstraction of problems within the construction domain. However, there is a lack of comprehensive systematic literature review and content analysis on application of SD in construction engineering and management (CEM). In this study, a systematic literature review and content analysis were used to investigate 213 journal articles published from 1995 through 2021, presenting applications of SD in CEM research. This included analysis of SD research in terms of contributing authors and their affiliations; identifying the major CEM research areas and patterns of SD research within those areas; study of the current focus of SD research, future trends, and potential for future research in these CEM areas; investigating the SD modelling paradigm in terms of hybridization with other modelling techniques; and a review of issues and challenges of SD modelling. This study contributes to the body of knowledge by (1) addressing the lack of a comprehensive systematic review and content analysis in the application of SD in CEM research, (2) providing construction researchers and practitioners with the state-of-the-art in SD research and application within the construction industry, and (3) assessing the potential for SD hybridization with other modelling approaches and proposing areas of future research to improve SD modelling capabilities. This study found that (1) the concept of SD was mostly used in the research areas of decision making and policy analysis, performance, and rework and change, (2) the areas of scheduling and health and safety have acquired more interest in SD relative to previous trends, and (3) researchers have the lowest interest in the research area of bidding and procurement.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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