@article {Shamas:2025:0736-2935:1146,
title = "Enhancing Construction Noise Monitoring with AI and Machine Learning",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2025",
volume = "271",
number = "1",
publication date ="2025-07-25T00:00:00",
pages = "1146-1156",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2025/00000271/00000001/art00016",
doi = "doi:10.3397/NC_2025_0195",
author = "Shamas, Rayan and Khandalkar, Prathamesh",
abstract = "Traditional noise monitoring in construction often relies solely on decibel readings, providing limited context and potentially leading to unnecessary work restrictions in high ambient noise environments. This case study presents the successful implementation of an Artificial Intelligence
(AI) and Machine Learning (ML) powered automated noise monitoring solution to meet the requirements of a critical infrastructure construction site in Rhode Island, USA. With the project requiring extended evening and nighttime work, accurate community noise monitoring was essential, along
with the ability to distinguish between construction-related and non-construction noise sources. By leveragin automated noise monitors with audio recording and machine learning algorithm to effectively distinguish between the cause of the noise exceedances. Strategically placed noise monitors
across the site enabled isolation of these various sounds, giving the project team and stakeholders confidence that any exceedances were correctly attributed. By isolating construction noise levels, we derived precise parameters specific to on-site activity and ensured that the community did
not endure any excessive disturbance caused by the night work. This accuracy not only justified the extension of work hours but also ensured compliance with community noise thresholds, mitigating unnecessary disruptions. This successful implementation highlights possible future applications
of AI&ML in Noise Monitoring.",
}