The Impact of Risk in Horizontal Directional Drilling
Bibliographic record
Abstract
The risks, associated with horizontal directional drilling (HDD) can have a significant impact on project schedule and cost. Contractors, engineers and owners are generally aware of the potential impact of this risk, but the awareness is largely qualitative in nature, and is thereby limiting for pointed decision making and the development of measured risk mitigation. Without quantitative information, industry stakeholders are without the means to evaluate risk strategies and identify appropriate risk mitigation measures in a manner that adequately develops and supports the business case for risk mitigation. As a result, the industry often has little choice other than to resort to accepting the risk and hoping for the best or transferring the risk using contractual methods. In order to move to a position of active assessment and mitigation, the industry needs quantitative information about the overall impact of risk as well as a comprehensive enumeration of risk events, the probability of occurrence and the impact of individual events. This paper looks at the general impact of risk as has occurred on 100 medium and large HDD projects. The general impact, in terms of schedule (and by extension, cost), serves to illuminate the need for structured risk mitigation. The paper also lists the risk events that have occurred on these 100 projects, as well as the frequency of occurrence, the average schedule impact and the Risk Index of each event type.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".