A ground-borne noise prediction model for railway traffic in tunnels in bedrock

Licentiate thesis, 2023

Human life has become more manageable by the expansion of railway lines. However, despite providing convenience, railways increase noise and vibration in residential areas. Vibrations and noise generated by railways may harm human health, cause cosmetic damage and have an adverse impact on the environment. In order to reduce the effects of train-induced noise and vibration, efficient and accurate models for the prediction of ground-borne noise and vibration are required.

Various analytical, theoretical, and experimental models have been developed to predict ground-borne noise. There is generally a lack of published information about parameters for ground-borne noise prediction concerning Swedish conditions with bedrock of high quality. Some investigations are reported, and a few consultancy companies have their own developed models, which are generally not publicly available. In fact, overall, the input data used to form these models and the methods of validation are not publicly available. Moreover, the statistical nature of the source and transfer paths requires that uncertainties are accurately handled in the model.

This work aims to develop a model for ground-borne noise prediction for underground tunnels, to be used in Swedish Transport Administration projects. The methodology is formulated for three different stages based on precision and available information: location stage, planning stage, and construction stage. The first two stages correspond to planning and designing a railway track. The third stage involves the construction stage where more detailed information may be acquired. The prediction model presented here is developed for Swedish bedrock up to 1 kHz and formulated as a source term and several correction terms. These terms take into account various aspects, including train speed, distance attenuation, ground-to-building coupling, vibration levels on different floors and walls, how the room properties affect sound pressure levels within rooms, and different track treatments. Moreover, uncertainties are estimated using the standard deviation of each term. The required data are gathered from measurements in the Gårda tunnel in Gothenburg combined with existing data from measurements in the Åsa tunnel close to Varberg.

As a result, a comprehensive model is suggested for ground-borne noise prediction in Swedish Transport Administration projects. However, the model is still under development, will be sent upon referral, and may undergo improvements.