Keywords: railway traffic, ground-borne noise and vibrations, prediction tool, environmental impact assessment, machine learning, surrogate model.

The prediction and control of vibrations and noise remain among the most significant environmental challenges associated with the operation of railway lines in urban areas. A national and global paradigm shift is currently under way, in which investment in rail transport is being prioritized over other modes as part of a decisive effort to reduce CO? emissions. This ongoing expansion and upgrading of the railway network, together with the increasingly stringent comfort standards expected by modern society, highlight the need to assess the impact of railway operations on nearby buildings, particularly the ground-borne noise and vibrations experienced by residents during service. This work introduces an efficient tool for the rapid prediction of vibration levels to support the environmental impact assessment of new railway projects. The tool enables the immediate identification of situations requiring more detailed analysis, through advanced numerical modelling, as opposed to those that can be dismissed at an early stage. The proposed solution features a graphical interface integrated with Geographic Information Systems, while its computational engine relies on substitution models supported by a database derived from numerically validated and experimentally verified models.

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