Integrated design technique for materials and structures of vehicle body under crash safety considerations

Chen, Yuan; Liu, Guiping; Zhang, Zheyi; Hou, Shujuan

doi:10.1007/s00158-017-1674-8

Cited by 48 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the approximate model methods, RBF is suitable for tackling highly nonlinear responses and has the best performance for accuracy and robustness. 21 RBF is used to build an approximate model that the analysis model needs for critical panel thickness optimization. RBF can transform a complex multidimensional problem into a simple one-dimensional problem with Euclidean distance as the independent variable.…”

Section: Establishment Of the Approximate Model On The Basis Of The Rbfmentioning

confidence: 99%

Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

Zhi-yong

Zhang

Huang

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

Structure design for reducing noise is essential and widely studied because cab interior noise seriously affects the ride comfort of the driver and passengers, especially for heavy commercial vehicles. This paper proposes an enhancement investigation on the low-noise structure performance of a cab in a heavy commercial vehicle based on an approximation model. A series of vibroacoustic tests with varying running speeds are implemented first to acquire the vibration signals at four cab suspensions and the sound pressure signal at the right ear of the driver. The finite element model and structure-acoustic coupled model of the cab of a heavy commercial vehicle are established sequentially. When the vibration accelerations measured in the tests are converted into excitation signals using the cab structure-acoustic coupled model, the sound pressure of the right ear of the driver is predicted, and the accuracy of the cab structureacoustic coupled model is then verified. After panel acoustic contribution analysis, an approximation model of critical panel thickness and the peak noise near the right ear of the driver is established in accordance with the radial basis function. Genetic algorithm is utilized to solve an optimization model to obtain optimal panel thicknesses. By comparing the right ear sound pressure before and after optimization, the results confirm that optimized panel thicknesses can reduce sound pressure level of the critical frequency.

show abstract

Section: Establishment Of the Approximate Model On The Basis Of The Rbfmentioning

confidence: 99%

Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

Zhi-yong

Zhang

Huang

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

show abstract

“…e common experimental design methods are orthogonal design, uniform design, and Latin hypercube sampling (LHS). However, the selection of experimental design method should be cautious because a set of inappropriate sampling points can bring about low accuracy in the surrogate models and even to a worse result with the failure to construct these models [30]. LHS is a stratified sampling process that can reflect the distribution of variables over the whole design space.…”

Section: Surrogate Modeling and Evaluationmentioning

confidence: 99%

Modeling and Stochastic Model Updating of Bolt‐Jointed Structure

Zhan

Guo

Lin

et al. 2018

Shock and Vibration

View full text Add to dashboard Cite

Bolt-jointed structure is widely used in engineering fields. The dynamic characteristics of bolt-jointed structure are complex, and there is a variety of uncertainties in the jointed structure. In this study, modeling and updating of a typical bolt-jointed structure are investigated. In modeling terms, three-dimensional brick elements are used to represent the substructures, and thin-layer elements with virtual material properties are employed to represent the joint interface. Modal tests and experimental modal analysis of substructures and built-up structure are performed. A hierarchical model updating strategy based on Bayesian inference is applied to identify the unknown parameters in the substructures model and those in the overall model. Radial basis function (RBF) models are used as surrogates of time-consuming finite element model with high resolution to avoid the enormous computational cost. The results indicate that the updated model can reproduce modal frequencies used in updating and can predict those not used in the updating procedure.

show abstract

“…Predictive models of FSI crashes can be complex and include variables from multiple domains. Environmental factors, road conditions, legal factors, licensing factors, and driver characteristics have all been found to contribute to FSI crash involvement [4][5][6][7][8][9][10][11][12]. Offense history (i.e., the number of traffic infringements a driver has incurred) and crash history (i.e., the number of crashes a driver has been involved in) have also frequently been found to be useful predictors of future FSI crashes [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data

et al. 2020

View full text Add to dashboard Cite

Background Expenditure on driver-related behavioral interventions and road use policy is often justified by their impact on the frequency of fatal and serious injury crashes. Given the rarity of fatal and serious injury crashes, offense history, and crash history of drivers are sometimes used as an alternative measure of the impact of interventions and changes to policy. The primary purpose of this systematic review was to assess the rigor of statistical modeling used to predict fatal and serious crashes from offense history and crash history using a purpose-made quality assessment tool. A secondary purpose was to explore study outcomes. Methods Only studies that used observational data and presented a statistical model of crash prediction from offense history or crash history were included. A quality assessment tool was developed for the systematic evaluation of statistical quality indicators across studies. The search was conducted in June 2019. Results One thousand one hundred and five unique records were identified, 252 full texts were screened for inclusion, resulting in 20 studies being included in the review. The results indicate substantial and important limitations in the modeling methods used. Most studies demonstrated poor statistical rigor ranging from low to middle quality. There was a lack of confidence in published findings due to poor variable selection, poor adherence to statistical assumptions relating to multicollinearity, and lack of validation using new data. Conclusions It was concluded that future research should consider machine learning to overcome correlations in the data, use rigorous vetting procedures to identify predictor variables, and validate statistical models using new data to improve utility and generalizability of models. Systematic review registration PROSPERO CRD42019137081

show abstract

Integrated design technique for materials and structures of vehicle body under crash safety considerations

Cited by 48 publications

References 31 publications

Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

Modeling and Stochastic Model Updating of Bolt‐Jointed Structure

A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data

Contact Info

Product

Resources

About