Speed estimation of a car at impact with a W-beam guardrail using numerical simulations and machine learning - Publikacja - MOST Wiedzy

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Speed estimation of a car at impact with a W-beam guardrail using numerical simulations and machine learning

Abstrakt

This paper aimed at developing a new method of estimating the impact speed of a passenger car at the moment of a crash into a W-beam road safety barrier. The determination of such a speed based on the accident outcomes is demanding, because often there is no access to full accident data. However, accurate determination of the impact speed is one of the key elements in the reconstruction of road accidents. A machine learning algorithm was used to create the speed estimation model. The model was based on regression trees algorithms, with base regressors forming a final voting ensemble. The model was trained, validated, and tested using a database containing results from full-scale crash tests and numerical simulations. The developed machine learning model had a mean absolute error of 6.76 km/h with a standard deviation of 1.01 km/h on the cross-validation set, and a coefficient of determination, R2, of 0.85. This model was used to estimate the impact speed of the vehicle in three real road accidents with the W-beam barrier, and then the determined speeds were used in additional simulations to verify the results. A good quantitative and qualitative agreement between the simulation and accident outcomes was achieved, and this confirmed that the proposed method and the developed ML models combined with numerical simulations and full-scale crash tests can be effective tools for estimating the speed of the vehicle at impact with a roadside barrier.

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
ADVANCES IN ENGINEERING SOFTWARE nr 184,
ISSN: 0965-9978
Język:
angielski
Rok wydania:
2023
Opis bibliograficzny:
Bruski D., Pachocki Ł., Ścięgaj A., Witkowski W.: Speed estimation of a car at impact with a W-beam guardrail using numerical simulations and machine learning// ADVANCES IN ENGINEERING SOFTWARE -,iss. 184 (2023), s.1-12
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.advengsoft.2023.103502
Źródła finansowania:
  • Publikacja jako artykuł Open Access w ramach programu Gold Open Access (due to the "agreement between the Polish Institutions and Elsevier") - koszt po stronie PG - 0 zł
Weryfikacja:
Politechnika Gdańska

wyświetlono 88 razy

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