Prediction and mitigation of construction noise in an urban environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it