Road profile measurement using the two degrees of freedom response-type mechanism

Kavianipour, Omid; Montazeri-Gh, Morteza; Moazamizadeh, M

doi:10.1177/0954406214543096

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…The IRI is negatively correlated with fatigue cracking and patching, while IRI is positively correlated with age, longitudinal and transverse cracking. Figure (4) shows the relationship between actual IRI and predicted IRI. Equation (5) shows the relationship between the IRI and pavement distress as follows:…”

Section: Multiple Linear Regression (Mlr) Resultsmentioning

confidence: 99%

“…ASTM D6433-18 defines pavement roughness as a "deviation of a surface from the true planar surface with characteristic dimensions that affect vehicle dimensions and ride quality" [3]. According to Kaavianipour et al [4] pavement roughness significantly impacts safety. Previous studies have also examined the relationship between IRI and pavement distress [5,6,7].…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs)

Ali¹,

Esekbi

Sreh

2023

International Journal of Advanced Engineering and Business Scie

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Roughness index forecasts are essential for optimizing pavement rehabilitation and treatment programs. The main objective of this study is to investigate the effect of pavement distress on pavement performance and develop International Roughness Index models (IRI) for dry no freeze regions in the U.S. Data for this research was collected from the Long-Term Pavement Performance (LTPP) database. The data include a total of 138 records of pavement distress with no maintenance and rehabilitation. Based on these data, IRI prediction models were developed using two modelling approaches: Multiple Linear Regression analysis (MLR) and Artificial Neural Networks (ANNs). The proposed models predict the IRI as a function of pavement distress variables such as or including fatigue cracking, block cracking, edge cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and ravelling. This study showed that the (ANNs) model yielded a higher prediction accuracy than the (MLR) model.

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Section: Multiple Linear Regression (Mlr) Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs)

Ali¹,

Esekbi

Sreh

2023

International Journal of Advanced Engineering and Business Scie

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“…For collecting this signal, a double integration process from the vertical accelerometer mounted on the sprung mass is used. 29 The design of the ADRC suspension controller form is discussed by Hasbullah et al. 8 and is also used in the present paper.…”

Section: Simulation Resultsmentioning

confidence: 99%

Intelligent PD controller design for active suspension system based on robust model-free control strategy

Haddar

Chaari

Başlamışlı

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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A novel active suspension control design method is proposed for attenuating vibrations caused by road disturbance inputs in vehicle suspension systems. For the control algorithm, we propose an intelligent PD controller structure that effectively rejects online estimated disturbances. The main theoretical techniques used in this paper consist of an ultra-local model which replaces the mathematical model of quarter car system and a new algebraic estimator of unknown information. The measurement of only input and output variables of the plant is required for achieving the reference tracking task and the cancellation of unmodeled exogenous and endogenous perturbations such as roughness road variation, unpredictable variation of vehicle speed and load variation. The performance and robustness of the proposed active suspension algorithm are compared with ADRC control and LQR control. Numerical results are provided for showing the improvement of passenger comfort criteria with model-free control.

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“…The raw acceleration data were collected to be transferred to a computer laptop and then to a computer application based on the MATLAB environment called Vibration Data Toolbox. This application was used to apply different digital filtering techniques such as bandpass filter (0.5–80 Hz) (Kavianipour et al , 2015), moving average filter (Mirtabar et al , 2020) and baseline correction filter (Pan et al , 2016). These filters were used to improve the quality of the roughness measurements and to remove the noise from the collected acceleration data.…”

Section: Methodology and Data Collectionmentioning

confidence: 99%

Development of pavement roughness regression models based on smartphone measurements

Al‐Suleiman

Alatoom

2022

JEDT

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Purpose The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods. Design/methodology/approach Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations. Findings According to the study results, several regression models were developed with a big variation in the coefficients of determination (R2). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R2 equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses. Practical implications The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options. Originality/value To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

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Road profile measurement using the two degrees of freedom response-type mechanism

Cited by 9 publications

References 11 publications

Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs)

Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs)

Intelligent PD controller design for active suspension system based on robust model-free control strategy

Development of pavement roughness regression models based on smartphone measurements

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