Vibrations observed as a result of moving vehicles can potentially affect both buildings and the people inside them. The impacts of these vibrations are complex, affected by a number of parameters, like amplitude, frequency, and duration, as well as by the properties of the soil beneath. These factors together lead to various effects, from slight disruptions to significant structural damage. Occupants inside affected buildings may experience discomfort, disrupted sleep patterns, and increased stress levels due to the pervasive nature of vibrations. Low-frequency vibrations, typically ranging from 5 to 25 Hz, are of particular concern since they can exacerbate these effects by resonating with internal human organs. To effectively mitigate these issues, a comprehensive approach is required, starting with some interventions at the source. This may involve strategic choices in road construction materials and advancements in vehicle design to reduce the transmission of vibrations through the ground to the surrounding environment. Understanding the complexities of vibration dynamics is essential in urban planning, serving as a fundamental consideration in the development of modern infrastructure that prioritizes the well-being and safety of its inhabitants. Therefore, the aim of the present study is to consider artificial neural networks to assess the potential impact of traffic-induced vibrations on a building’s residents. The results of the study indicate that the proposed method of utilizing machine learning can be effectively applied for such purposes.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.3390/app15041689
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach
- Language
- angielski
- Publication year
- 2025
