Bibliographic record
Abstract
This paper describes how the assessment of noise at construction sites is a major concern to environmental and occupational health authorities. The health significance of noise pollution extends to include noise-induced hearing impairment, interference with speech communication, disturbance of rest and sleep, psychological health and performance effects and interference with regular human activities. The paper describes how the noise-induced hearing impairment is the most prevalent irreversible occupational hazard and it is estimated that 120 million people worldwide have developed hearing difficulties because of noise. In developing countries, not only occupational noise but also environmental noise is an increasing risk factor for hearing impairment. Workers in the construction industry are at a particular risk because the construction industry is a major source of noise pollution. The use of heavy vehicles as well as noisy tools and equipment is common in many construction sites. Occupational exposure to high noise levels places hundreds of thousands of construction workers at risk of developing hearing impairment and hypertension. In Singapore, nearly 18 percent of all noise complaints were directly related to noise from construction sites4. A study of construction noise in Ontario, Canada has reported average noise levels ranging from 93.1 dBA to 107.7 dBA. Tools and equipment were found to be the major source of noise at construction sites. Modeling is considered a powerful tool for assessing the environmental impact of noise but the prediction models currently available are limited in their suitability to construction noise patterns6. It is imperative, yet difficult, because of the complex interaction between noise levels, distance from the noise source, project size type of construction equipment used, and construction stage. Moreover, it has been recognized that noise modeling is also a complex task as noise propagation is non-linear. It cannot be simply modeled using traditional mathematical and statistical models. Although a number of studies have been conducted to measure noise at construction sites and determine the exposure of workers and health effects, very limited work has been reported on the modeling and prediction of noise levels at construction sites. Expert systems technology such as artificial neural networks (ANNs) approach has been thought of as a viable alternative method to model noise levels at construction sites. In fact, ANNs have been applied for modeling and prediction in various fields but their application in modeling the construction noise was tested only recently7. This paper examined the application of ANNs as sophisticated techniques having elastic and independent structure to model the variation of noise levels at construction sites. The main objective was to compare some structured networks for their ability to predict the construction noise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".