Acoustic performance evaluation of railway boundary walls using a computational fluid dynamics-based simulation approach.

Railway noise has become a significant concern for trackside residents due to increased volume of high-speed passenger and freight train traffic. To address this, active measures, such as reducing noise at the source, and passive measures, such as installing noise barriers along the transmission path, are widely being used. In urban areas, railway boundary walls are constructed to prevent encroachments of railway lands and to avoid pedestrian trespassing of railway tracks. This study aims to evaluate the effectiveness of such a boundary wall for reducing noise and proposes an improved alternative through computational fluid dynamics (CFD) simulations. Various noise barriers with different geometry, shape, and surface materials were simulated and validated with the field conditions based on a rectangular wall of height 2.75 m. Noise attenuation was evaluated by measuring railway noise spectra at different positions, including 0.5 m in front and behind the barrier and at the facade of the residential area. The insertion loss based on field measurements for a rectangular barrier of height 2.75 m was observed to be 5.2 dBA. The simulation results indicated a positive correlation between barrier height and insertion loss, with a maximum attenuation of 17 dBA achieved with a barrier of height 6 m. The most effective noise barrier for reducing railway noise was a T-shaped barrier with a height of 6 m and a projection length of 2 m, with an insertion loss of 22 dBA. This study recommends constructing the barrier with soft materials on its surface to reflect and absorb sound waves effectively. These findings have potential implications for urban planners and policymakers in designing effective noise barriers in residential areas near railway lines.

Quantifying the influence of transmission path characteristics on urban railway noise.

Every ambient noise study employs the source-path-receiver structure to explore the overall behaviour of sound. Noise levels are affected by changes in distance, intervening barriers, and atmospheric conditions along the transmission path between the source and the receiver. The objective of this study is to quantify the influence of transmission path characteristics for a realistic time-varying moving line source. In this context, railway noise was considered to explore the variance of noise in an urban setting over a variety of measuring distances, including 25, 50, 100, and 200 m, with variables such as air temperature, humidity, and wind condition. The data corresponding to 106 trains was collected for analysis, and it was observed that the effect of the wind was more significant for larger distances between the source and the receiver. When the sound levels were measured in two opposite wind directions, a considerable noise level difference was observed. For every 1 m/s increase in wind speed, within a distance of 50 m, the average sound attenuation induced by the upwind phenomena was 0.2 dBA. The impact of air temperature changes on received sound level from a moving source was insignificant within the range of temperatures considered in the study. The effect of humidity is less at shorter distances but at larger distances, increasingly attenuates noise levels. Analysis of variance was performed on the selected variables to determine whether the means of each group were significantly different from each other and found that train speed had a more significant impact on railway noise compared to other parameters.

Use of artificial neural networks to assess train horn noise at a railway level crossing in India.

Urban environment noise is a complex mixture of transportation, industrial, household, and recreational noise, which is identified as an emerging environmental threat. Present study monitors and evaluates a noise pollution hotspot: a railway level crossing, where several activities related to transportation noise were involved. Train honking, train movement, road vehicles, and pedestrians contribute to the noise level at a railway level crossing. Train horns are generally performed as train approach railway level crossings and they are mandatorily used to alert road users. However, the train horns are regarded as nuisance to the nearby residents. A detailed evaluation of train horn effectiveness is very much essential in the current contemporary environment. Thus, the main objective of this study is to measure noise levels emanating from train horns at a level crossing with due consideration to train types and climatic conditions. A comprehensive noise monitoring survey was conducted at an access-controlled level crossing. Furthermore, an artificial neural network (ANN)-based railway noise prediction model was developed to forecast maximum ([Formula: see text]) and equivalent (Leq) noise levels. Results revealed that train horn produced impulsive sound signals which fall under high frequency one-third octave bands causing severe irritation to trackside inhabitants. The proposed ANN models produced accurate results for [Formula: see text] and Leq noise levels and this model is identified as a vital tool for railway noise abatement. The results from this study are helpful to the urban planning and development authorities to implement strategic laws and policies to eradicate the urban environment noise.