Human Factors in Large Capital Projects
Bibliographic record
Abstract
Abstract Considering human interaction in a project's design is not new and is traditionally applied at various design reviews and during construction to ensure operability. Human factors are routinely considered in industry for a facilities operating phase including walking working surfaces, access for maintenance, and general biometric requirements. Including human factors in construction and constructability reviews is the next step-out in human factors program implementation. This requires careful consideration of human factors and the design implications to allow for safe construction execution processes and overall safety of construction workers. This paper will discuss ExxonMobil's systematic application of human factors considerations throughout the life cycle of a project including engineering design, construction planning, and execution phases to allow safe operations throughout the facility's life cycle. Definition of Human Factors and Programs Human Factors affects the design and engineering of the interface between work environment, technology, and human interaction to increase safety, performance, and productivity. More specifically, human factor programs provide a systematic process to design that applies and integrates knowledge of the capabilities, limitations, and needs of people with respect to:• worksite design (facilities layout, configuration, and accessibility)• equipment and tools used• environments worked in (temperature, noise, lighting) All of this while considering the biometrics of the workforce that will be employed at the worksite. Human factors reviews normally will focus on human interfaces (physical and cognitive factors) that improve a workplace designed for human use (working environments and machines and equipment used). Major consideration must be given so that procedures can be written and employed that do not require performance beyond the operators capabilities, produce fatigue, provide appropriate information flow and do not violate the operators expectations. Human Factors in Engineering Design Incident rates have fallen since the implementation of process safety management regulations and process safety systems. These systems and regulations target facility information, risk characterization, and development of sound operating procedures for a facility. The implementation of these programs and management systems did create a step change decrease in industrial accidents as they were implemented as illustrated in Figure 1. Application of human factors programs created a reduction in accident rates in operating facilities. These programs were also implemented in the design of new facilities considering the operating phase of the facilities. Human Factors is now considered integral to the design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".