Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».