A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings - Publikacja - MOST Wiedzy

Wyszukiwarka

A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings

Abstrakt

Traffic-induced vibrations may cause various damages to buildings located near the road, including cracking of plaster, cracks in load-bearing elements or even collapse of the whole structure. Measurements of vibrations of real buildings are costly and laborious. Therefore the aim of the research is to propose the original numerical algorithm which allows us to predict, with high probability, the nega-tive dynamic impact of traffic-induced vibrations on the examined building. The model has been based on machine learning. Firstly, the experimental tests have been conducted on different buildings using specialized equipment taking into ac-count six factors: distance from the building to the edge of the road, type of sur-face, condition of road surface, condition of the building, the absorption of soil and the type of vehicle. Then, the numerical algorithm based on machine learning (using support vector machine) has been created. The results of the conducted analysis clearly show that the method can be considered as a good tool for pre-dicting the impact of traffic-induced vibrations on buildings, being characterized by high reliability.

Cytowania

  • 1

    CrossRef

  • 0

    Web of Science

  • 0

    Scopus

Cytuj jako

Pełna treść

pełna treść publikacji nie jest dostępna w portalu

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Aktywność konferencyjna
Typ:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Tytuł wydania:
Computational Science – ICCS 2020 strony 444 - 451
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Jakubczyk-Gałczyńska A., Jankowski R.: A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings// Computational Science – ICCS 2020/ : , 2020, s.444-451
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/978-3-030-50420-5_33
Weryfikacja:
Politechnika Gdańska

wyświetlono 22 razy

Publikacje, które mogą cię zainteresować

Meta Tagi