Effects of Seated Postural Sway on Visually Induced Motion Sickness: A Multiple Regression and RUSBoost Classification Approach

Park, Sangin; Mun, Sungchul

doi:10.1080/10447318.2023.2200638

Cited by 2 publications

(2 citation statements)

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“…Regular random forest model was also tried; however, the testing accuracy was not good enough due to the high skewness in data. Previous researchers (Blackard and Dean, 1999;Seiffert et al 2008;Seiffert et al, 2010;Mounce et al, 2017;Kinoshita et al, 2020;Malek et al, 2022;Park and Mun, 2023) have mentioned the effectiveness of RUSBoost (Hearn, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…Herein, RUSBoost based random forest models were developed. RUSBoost algorithm is well suited to handle highly imbalanced data (Blackard and Dean, 1999;Seiffert et al, 2010;Mounce et al, 2017;Kinoshita et al, 2020;Park and Mun, 2023) which is the case of NBI database. Furthermore, herein, uncertainties associated with the prediction of the data-based models was also quantified.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning approach for predicting bridge components’ condition ratings

Mia,

Kameshwar

2023

Front. Built Environ.

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Information on bridge condition rating is critical to make decisions regarding rehabilitation or replacement of bridges. Currently, bridge components’ condition ratings are evaluated manually using inspection reports. Markov chain and Petri net models are most commonly used for predicting future values of bridge parameters, however, applicability of these models for a regional or statewide portfolio of bridges may be limited. The existing data based models have low prediction accuracy. Hence, a data and machine learning based approach is presented herein for predicting the future condition values of major components—deck, superstructure and substructure—in a portfolio of bridges with an objective to develop a more accurate approach. National Bridge Inventory (NBI) was used to get information on current and past bridge components’ condition from year 1992–2019 along with other parameters such as ownership, maintenance responsibility and age. After selecting important parameters, this data was used to train three RUSBoost based random forest models for predicting future values of deck, superstructure, and substructure conditions, respectively. The prediction accuracy of the developed models were found above 93%, thereby addressing the limitation of poor prediction accuracy of the existing studies. Additionally, the uncertainties associated with the random forest based predictions were quantified at the regional level and for individual bridges. On-system concrete pre-cast slab units and steel I-beam bridges in Louisiana were selected to demonstrate the proposed approach and predict bridge components condition ratings for years 2020 and 2021.

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