Review and Survey of 4D Simulation Applications in Forensic Investigation of Delay Claims in Construction Projects
Bibliographic record
Abstract
Four-dimensional (4D) simulation is not frequently used in courtrooms because it is considered costly, complex, and risky. The experience in litigation systems reveals that many expert witnesses at present are not familiar with 4D simulation and are reserved about using technology. The objectives of this article are to discuss the efficiency and value of 4D simulation in construction claims as a tool for supporting legal arguments, stakeholder’s viewpoints, and interrogatory considerations. In delay claims, conventional methods, tools, and 4D simulation concepts were used. The case study considered the appropriate level of development (LOD) for claims: summary for the full scope, and detailed for event specific information. This case study was presented to seven lawyers. A survey, extended with semistructured interviews, was sent to 17 additional external construction litigation lawyers. The results show the advantages and conditions of using 4D simulation for different contractual dispute resolution situations considered in construction claims for avoidance, resolution, and litigation. The 4D simulation is developed based on building information modeling (BIM) that can be binding or nonbinding. In the latter case, it can be developed to strengthen the position of one party’s argument and visualize multiple scenarios (as-built, as-planned, claim events). Other results considering the rules of law show a list of influence factors with 4D simulation in delay claims, types of evidence, and limitations, such as suitable formats and courts for 4D simulations.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".