Contractor default prediction model for surety bonding<sup>1</sup>This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.
Bibliographic record
Abstract
Contractor default is one of the major risks that threaten a project’s success in the construction industry. Previous studies have focused mainly on evaluation of the contractor’s financial aspects to predict contractor default. There remains a need for a comprehensive model that has the ability to incorporate the evaluation of all the project aspects, project team, contractual risks, and project management evaluation criteria to predict the possibility of a contractor’s default on a specific construction project. This paper presents a contractor default prediction model (CDPM) from the surety bonding perspective that incorporates these criteria and uses a fuzzy inference system for reasoning. The CDPM provides a more objective, structured, and comprehensive approach for contractor default prediction for surety practitioners, project owners, and for self-assessment by contractors to reduce the risk of contractor default. The multi-attribute utility function was used to develop a group consensus system (GCS) to aggregate the participating experts’ opinions to build the CDPM. The accuracy of the GCS was found to be 91.1%. A novel approach for fuzzy rule base development is applied to develop the rule base for the CDPM. The CDPM was validated using 30 contractor default prediction cases, and the accuracy was found to be 86.5%.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".