Prediction and mitigation of construction noise in an urban environment
Bibliographic record
Abstract
A growing number of construction projects are performed in congested urban areas. Often, the surrounding community finds these projects annoying because of noise, vibration, dust, light, and greenhouse gas emissions. This paper focuses on one type of irritant, noise. Common noise generators on construction sites are identified, and the elements of a generic program for mitigating construction-related noise are outlined. Mitigation strategies including source control, path control, and receiver control are discussed. A deterministic model based on the Monte Carlo simulation technique is used. It is capable of predicting the magnitude and frequency of noise levels generated by construction equipment at receptor locations around a construction site during each construction stage. The utilization of the model as a planning tool for optimizing the composition, geometry, and location of noise barriers around a construction site is demonstrated via a case history, namely the construction of an eight-storey parking garage in London, Ont. The model is validated by comparing its predictions to field measurements undertaken during various construction stages. Predictions agree favourably with field measurements.Key words: construction, noise, mitigation, barriers, modeling, Monte Carlo simulation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".