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Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes.

Hong Zhang¹, Yun Pei²

¹Institute of Construction Management, Zhejiang University, Zhejiang 310058, China. jgzhangh@zju.edu.cn.

International Journal of Environmental Research and Public Health

|August 17, 2016

Summary

This study introduces a discrete-event simulation method for predicting construction noise levels. This approach accurately quantifies equivalent continuous noise, aiding in construction planning and noise mitigation strategies.

Keywords:

construction processes discrete-event simulation equivalent continuous noise prediction simulation framework

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Area of Science:

Environmental Engineering
Acoustics
Computational Modeling

Background:

Accurate construction noise prediction is vital for evaluating project plans and managing environmental impact.
Existing methods for measuring and predicting construction noise, especially equivalent continuous noise levels over time, have limitations.
Addressing these limitations requires advanced predictive methodologies.

Purpose of the Study:

To present a novel discrete-event simulation method for quantitative prediction of construction noise.
To develop and incorporate noise-calculating models for synchronization, propagation, and equivalent continuous level.
To establish a simulation framework for modeling noise-affecting factors and calculating noise levels.

Main Methods:

Development of discrete-event simulation framework tailored for construction noise.
Integration of specific noise-calculating models: synchronization, propagation, and equivalent continuous level.
Application study to demonstrate and validate the proposed simulation methodology.

Main Results:

The proposed discrete-event simulation method effectively predicts equivalent continuous noise levels during construction.
The framework successfully models noise-affected factors, incorporating uncertainties, dynamics, and interactions.
Validation through an application study confirms the method's predictive accuracy.

Conclusions:

The study provides a robust simulation methodology for quantitative construction noise prediction.
This approach offers a valuable tool for informed decision-making in construction planning and noise control.
The method enhances the ability to manage and mitigate construction-related noise pollution effectively.