Decentralized and Collaborative Information Management System in Construction Contract Administration: A change management case study
Bibliographic record
Abstract
Data quality and lack of reliable information about changes, claims, and risks are ongoing challenges in construction contract administration (CCA). These issues along with ineffective information management, can result in poor decision-making, and eventually, cost overruns and delays. The CCA processes involve complex communications and management of information flow among stakeholders. Blockchain and ontology have been widely studied for their capabilities to improve collaborative information management and foster decision-making, but their integration potentials are mostly unexplored within the context of CCA information management. This study aims to develop a contract information management system leveraging the benefits of blockchain and ontology. The system is demonstrated using change management as a representative CCA process, with two main objectives: (1) to overcome issues hindering trust and collaboration for the effective management of CCA-related information flow, and (2) to improve accessibility to reliable data for effective decision-making. A decentralized and collaborative contract information management (DCCIM) system was proposed encompassing two main layers of blockchain and ontology-based information management. Additionally, a novel framework was developed for designing the smart contract and blockchain network based on a standardized ontology and business process model. The system was evaluated using a change management process as a case study to demonstrate its applicability within CCA workflows. The blockchain infrastructure enables reliable, collaboratively managed information flow, while the ontology facilitates the development of smart contracts aligned with the management process and enhances access to information by supporting complex queries and automated reasoning over captured data. The proposed DCCIM system could be a promising building block for future developments and full automation of CCA processes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".