Noise barriers are mainly applied to reduce road traffic noise on highways; however, noise control methods need to be diversified owing to structural limitations and economic problems. In this study, the diamond grinding (DG) and next generation concrete surface (NGCS) methods applied to improve performance due to aging concrete pavement were proposed as noise control methods. It is expected to be more effective as a noise reduction countermeasure because it is easier to construct, cheaper than existing noise barriers, and can reduce the vehicle wheel noise, which is the majority of road traffic noise. The pass-by noise is measured and analyzed on five construction points of the concrete road improvement methods and the respective existing pavement points simultaneously. The CPX noise was also measured on seven measurement points by connecting wheel noise measurement equipment to the vehicle, and the wheel noise between the existing and improved pavement surfaces while driving at a design speed (100km/h) of the highway were compared. From this study, it was confirmed that noise reduction by concrete pavement surface improvement methods can have an effect of 3.9dB(A)to 5.8dB(A).

Soundproof tunnels block direct sound and minimize reflected sound, resulting in an excellent noise reduction effect. However, soundproof tunnels have not been thoroughly examined in terms of economic feasibility, stability, and usability. In particular, their high cost of installation and heavy weight are considered disadvantages. Therefore, it is essential to ensure economic feasibility and reduce the weight of soundproof tunnels. However, in South Korea, panels used in soundproof tunnels can be used only if they satisfy the required standards (sound transmission loss). Therefore, in this study, while the acoustic performance standards (sound transmission loss) for soundproofing panels were not met, a lighter and thinner membrane material was proposed for a soundproof tunnel, and its noise reduction effect, weight, and economic feasibility were examined. To ensure marketability, the noise reduction effect of the soundproof tunnel with the membrane structure was investigated using 3D modeling and real-scale experiments, and the findings were compared and analyzed with existing noise barriers.

Noise barriers are mainly applied to reduce road traffic noise on highways; however, noise control methods need to be diversified owing to structural limitations and economic problems. In this study, the diamond grinding (DG) and next generation concrete surface (NGCS) methods applied to improve performance due to aging concrete pavement were proposed as noise control methods. It is expected to be more effective as a noise reduction countermeasure because it is easier to construct, cheaper than existing noise barriers, and can reduce the vehicle wheel noise, which is the majority of road traffic noise. The pass-by noise is measured and analyzed on five construction points of the concrete road improvement methods and the respective existing pavement points simultaneously. The CPX noise was also measured on seven measurement points by connecting wheel noise measurement equipment to the vehicle, and the wheel noise between the existing and improved pavement surfaces while driving at a design speed (100km/h) of the highway were compared. From this study, it was confirmed that noise reduction by concrete pavement surface improvement methods can have an effect of 3.9dB(A)to 5.8dB(A).

Soundproof tunnels block direct sound and minimize reflected sound, resulting in an excellent noise reduction effect. However, soundproof tunnels have not been thoroughly examined in terms of economic feasibility, stability, and usability. In particular, their high cost of installation and heavy weight are considered disadvantages. Therefore, it is essential to ensure economic feasibility and reduce the weight of soundproof tunnels. However, in South Korea, panels used in soundproof tunnels can be used only if they satisfy the required standards (sound transmission loss). Therefore, in this study, while the acoustic performance standards (sound transmission loss) for soundproofing panels were not met, a lighter and thinner membrane material was proposed for a soundproof tunnel, and its noise reduction effect, weight, and economic feasibility were examined. To ensure marketability, the noise reduction effect of the soundproof tunnel with the membrane structure was investigated using 3D modeling and real-scale experiments, and the findings were compared and analyzed with existing noise barriers.

In this study, we developed a statistical method to predict environmental noise using machine learning models trained on measured data from a noise monitoring network in Seoul, Korea. Daytime and nighttime annual equivalent noise levels were used as dependent variables, and traffic, climate, topographic, landscape, and land-use characteristics were used as explanatory variables. Feature variables were aggregated within buffer distances of 20 to 80 m around monitoring sites to identify the optimal range of influence. Among several models that we evaluated, an extremely randomized trees (Extra-Trees) model showed the highest predictive performance with a coefficient of determination of 0.729 and a root mean square error of 3.4dB(A) for daytime noise at a buffer radius of 30m. We then applied Shapley additive explanations (SHAP) to analyze the contribution of each variable, and the results showed that factors related to traffic were the most influential, followed by land-use characteristics. The trained model was applied to a 10m×10m grid to generate a statistical noise map. This study highlights the potential of explainable machine learning-based statistical noise mapping for urban noise management and land-use planning.

Current noise-evaluation practices for military firing ranges are based on weapon caliber: small firearms (less than 20mm) apply A-weighting with a +12dB impulse correction, whereas large firearms (20mm or more) apply C-weighting with a +18dB impulse correction. However, in combined firing ranges where both small and large firearms are used, this caliber-based approach does not adequately reflect the distinct acoustic characteristics of each weapon type. In particular, practice rounds from large firearms typically exhibit acoustic patterns similar to those of small firearms, thus rendering accurate classification challenging under the existing system. Hence, this study developed a machine learning-based classification model and verified its ability to classify noise more accurately by ammunition type. The Random Forest model achieved a high accuracy of 98.7%, and when practice-round data were reclassified, 90.9% were categorized as having small-firearm acoustic characteristics, thus confirming that large-firearm practice rounds resemble small firearms in terms of sound. These findings enhance the objectivity of noise analysis and noise-impact assessment for military firing ranges and are beneficial for improving noise-evaluation criteria in combined firing ranges in the future.

The purpose of this study was to calculate a correction value for estimating the noise levels in the classrooms of a newly planned school for use in environmental-impact and educational-environment assessments. The study involved measuring and analyzing the outdoor and indoor noise levels in the classrooms of 32 elementary and middle schools located in Yongin-si and Seongnam-si, Gyeonggi-do. In each of the 55 cases, the noise level was measured at a receiver point located 1m in front of the school building facade. At the same time, the indoor noise levels were measured at 2 receiver points located 1m in front of a window and 1m in front of a hallway in the classroom, with all the windows open. These measurements were averaged to calculate the noise level in the classroom. Then, a correction value was calculated based on the difference between the outdoor noise level and noise level in the classroom. Therefore, by considering this correction value for the estimated outdoor noise level during the planning stage, the noise level in the classroom could be determined. The final calculated correction values were −6.4dB(A) for the parallel type, −6.9dB(A) for the vertical type, and −6.5dB(A) for the mixed type. The use of these correction values will reduce the noise impact and resource wastage that could result from over- or underestimating, creating a calm and reasonable educational environment.

Complaints related to construction-site noise account for the largest proportion of noise-related complaints and are consistently raised annually, necessitating effective management and countermeasures. However, managing construction-site noise is challenging because of the diverse variables that arise during different stages of construction. Furthermore, there are various limitations in the prediction technique applied in environmental-impact assessments, leading to actual damage in surrounding areas due to the noise from construction sites. Therefore, in this study, we reviewed the existing literature on noise- monitoring systems installed at construction sites as one noise management strategy. We determined that the optimal installation position for a noise-monitoring system was at the boundary line of a construction site based on this review. However, it was concluded that when installing a noise-monitoring system along a construction-site boundary, it is necessary to consider all the cardinal directions (north, south, east, west). Additionally, a method was proposed to predict and manage construction- site noise by integrating the installed noise-monitoring system with 3D modeling, which was explained in three stages.