Customer Usage Based on Pseudo Damage

2017 ◽  
Author(s):  
Craig T. Altmann ◽  
John B. Ferris
Keyword(s):  
Performance Monitoring ◽  
International Roughness Index ◽  
The Road ◽  
Road Profile ◽  
Road Roughness ◽  
Federal Highway ◽  
Roughness Index ◽  
Average Annual Daily Traffic ◽  
Road Type ◽  
Early Design Stages

Modeling customer usage in vehicle applications is critical in performing durability simulations and analysis in early design stages. Currently, customer usage is typically based on road roughness (some measure of accumulated suspension travel), but vehicle damage does not vary linearly with the road roughness. Presently, a method for calculating a pseudo damage measure is developed based on the roughness of the road profile, specifically the International Roughness Index (IRI). The IRI and pseudo damage are combined to create a new measure referred to as the road roughness-insensitive pseudo damage. The road roughness-insensitive pseudo damage measure is tested using a weighted distribution of IRI values corresponding to the principal arterial (highways and freeways) road type from the Federal Highway Administration (FHWA) Highway Performance Monitoring System (HPMS) dataset. The weighted IRI distribution is determined using the number of unique IRI occurrences in the functional road type dataset and the Average Annual Daily Traffic (AADT) provided in the FHWA HPMS data.

scholarly journals Effect of Road Profile on Suspension System of Heavy Truck

2019 ◽  
Vol 12 (2) ◽  
pp. 71-75
Author(s):  
Salem F. Salman
Keyword(s):  
Steering System ◽  
Suspension System ◽  
The Road ◽  
Road Profile ◽  
Road Roughness ◽  
Road Type ◽  
Heavy Truck ◽  
Main Factors ◽  
On The Road ◽  
The Stability

All vehicles are affected by the type of the road they are moving on it.  Therefore the stability depends mainly on the amount of vibrations and steering system, which in turn depend on two main factors: the first is on the road type, which specifies the amount of vibrations arising from the movement of the wheels above it, and the second on is the type of the used suspension system, and how the parts connect with each other. As well as the damping factors, the tires type, and the used sprungs. In the current study, we will examine the effect of the road roughness on the performance coefficients (speed, displacement, and acceleration) of the joint points by using a BOGE device.

scholarly journals Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City

2020 ◽  
Vol 12 (24) ◽  
pp. 10536
Author(s):  
Shong-Loong Chen ◽  
Chih-Hsien Lin ◽  
Chao-Wei Tang ◽  
Liang-Pin Chu ◽  
Chiu-Kuei Cheng
Keyword(s):  
Threshold Value ◽  
City Government ◽  
International Roughness Index ◽  
The Road ◽  
Standard Scale ◽  
Road Roughness ◽  
Roughness Index ◽  
Taipei City ◽  
Road Surface Roughness ◽  
Asphalt Concrete Pavement

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

Smooth Enough?: Estimated Roughness on Roads with Low International Roughness Index Values

2003 ◽  
Vol 1860 (1) ◽  
pp. 144-151 ◽  
Author(s):  
Sven Dahlstedt
Keyword(s):  
Measurement Errors ◽  
Subjective Experience ◽  
Spectral Composition ◽  
Random Errors ◽  
International Roughness Index ◽  
The Road ◽  
Road Roughness ◽  
Longitudinal Roughness ◽  
Roughness Index ◽  
Subjective Estimates

The reported investigation is one part of a project concerning methods for measurement of the longitudinal roughness of roads and the necessary accuracy. In this study the main focus was on the subjective experience of roughness on roads with low international roughness index (IRI) values, that is, fairly good roads. With the available data it was also studied how much a random error added to the IRI values would influence the correlations with the subjective estimates. The investigation was carried out as a magnitude estimation experiment. Twenty-two observers made their estimates while traveling as passengers first in a car and later in a truck. The roughness estimates were made on 45 sections along a 60-km route. Most of the stretches had an IRI roughness between 0.5 and 3.0 mm/m, with a few of up to IRI = 5.5. The reference section had an even higher roughness, IRI = 6.24, which was given the nominal subjective roughness magnitude of 100. The main results of the study were as follows: subjective roughness seems to be a linear function of roughness according to IRI within the studied roughness range; for some road sections with a nontypical spectral composition of the road roughness, it was found that the correlation between IRI and subjective roughness decreased considerably, and the simulations of random errors added to the IRI values showed that within the studied range and with the fairly large number of observations (45), random measurement errors up to at least ±0.2 IRI unit (mm/m) can be considered insignificant.

scholarly journals Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City

2020 ◽  
Author(s):  
Shong-Loong Chen ◽  
Chih-Hsien Lin ◽  
Chao-Wei Tang ◽  
Liang-Pin Chu ◽  
and Chiu-Kuei Cheng
Keyword(s):  
Threshold Value ◽  
City Government ◽  
International Roughness Index ◽  
The Road ◽  
Standard Scale ◽  
Road Roughness ◽  
Roughness Index ◽  
Taipei City ◽  
Road Surface Roughness ◽  
Asphalt Concrete Pavement

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

scholarly journals Study of Pavement Condition Index (PCI) relationship with International Roughness Index (IRI) on Flexible Pavement.

2019 ◽  
Vol 258 ◽  
pp. 03019 ◽  
Author(s):  
Rijal Psalmen Hasibuan ◽  
Medis Sejahtera Surbakti
Keyword(s):  
Condition Index ◽  
International Roughness Index ◽  
Pavement Condition ◽  
The Road ◽  
Roughness Index ◽  
Pavement Condition Index ◽  
Local Roads ◽  
Actual Field ◽  
Two Parameters

Road is an infrastructure that built to support the movement of the vehicle from one place to another for different purposes. Today, it is often found damage to road infrastructure, both local roads, and arterial roads. Therefore, to keep the pavement condition to remain reliable, in Indonesia has a periodic program by conducting an objective functional inspection of roads regulated by Bina Marga using the International Roughness Index (IRI). However, the IRI examination is not sufficient to represent the actual field condition; it is necessary to perform subjective functional examination by appraising the road one of them is Pavement Condition Index (PCI, ASTM D 6433). This method has been widely applied in some countries because it has many advantages. However, to do this inspection requires considerable cost, then there needs to be a model to get the relationship between these two parameters of the road. The selected case study was arterial road segment in Medan City, that is in Medan inner ring road. Based on the results of the analysis, there is a difference between the functional conditions of PCI and IRI. The equation derived from these two parameters is by exponential regression equation, with equation IRI = 16.07exp-0.26PCI. with R2 of 59% with correlation coefficient value (r) of -0.768. The value of R2 indicates that PCI gives a strong influence on IRI value.

Use of Neural Network and Discrete Wavelet Transformations in Estimation of Road Profile

Volume 2 ◽  
2004 ◽  
Author(s):  
Mohammad Durali ◽  
Alireza Kasaaizadeh
Keyword(s):  
Neural Network ◽  
Optimization Methods ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network ◽  
The Road ◽  
The Neural Network ◽  
Road Profile ◽  
Road Roughness ◽  
Coding Method ◽  
Dynamic Experiments

This paper presents a method for estimation of road profile for automotive research applications with more accuracy and higher speed. Dynamic response of a car equipped with position and velocity sensors and driving on a sample road is used as basic data. A feed-forward neural network, trained with outputs from a car model in ADAMS, is used as the car inverse model. The neural network is capable of estimating the road roughness from the car response during test drives. The ADAMS model is corrected and validated using a series of dynamic experiments on the car, performed on a hydro-pulse test rig. The only problem in this approach, like other identification and optimization methods, is the large volume of generated data in time domain, acquired from car response during road test. This problem is solved using wavelet methods to code the acquired data. Unlike all frequency methods that eliminate a large portion of the data details during processing, the wavelet coding method restores most of the details, while the volume of the stored data is kept to a minimum. The results show that this method can estimate the road profile accurately and with great savings in processing time and effort.

The Research of Road Profile Estimation Based on Acceleration Measurement

2012 ◽  
Vol 226-228 ◽  
pp. 1614-1617 ◽  
Author(s):  
Ye Chen Qin ◽  
Ji Fu Guan ◽  
Liang Gu
Keyword(s):  
Dynamic Responses ◽  
The Road ◽  
Physical Measurements ◽  
Power Spectral ◽  
Road Profile ◽  
Road Roughness ◽  
Power Spectral Densities ◽  
Profile Estimation ◽  
The Relationship ◽  
Unsprung Mass

To get the certain response of vehicle during the driving process, it’s necessary to measure the road irregularities. Existing method of gauging the roughness is based on physical measurements and the instrument is installed under the vehicle, which is expensive and will affect the vehicle dynamic responses. This paper shows an easier method to estimate the road roughness by measuring and calculating the power spectral density (PSD) of unsprung mass accelerations. This approach is possible due to the relationship between these two via a transfer function. By comparing the power spectral densities of estimated road and the standard classes, we can classify the current road classes easily. Besides, this paper also shows that it’s feasible to estimate the road profile by calculating the PSD of unsprung mass accelerations directly.

scholarly journals Assessment of the road based on PCI and IRI roadroid measurement

2018 ◽  
Vol 195 ◽  
pp. 04006 ◽  
Author(s):  
Donny A. Putra ◽  
Mamok Suprapto
Keyword(s):  
Good Condition ◽  
Visual Assessment ◽  
Condition Index ◽  
Assessment Method ◽  
Visual Methods ◽  
International Roughness Index ◽  
Pavement Condition ◽  
The Road ◽  
Roughness Index ◽  
Pavement Condition Index

There are two methods of road assessment, ie, visually and using tools. Visual assessment makes use of the PCI (Pavement Condition Index), while assessment with the Roadroid app can be used to obtain the value of IRI (International Roughness Index) with less cost. Functional assessment of roads in the field more use of visual methods. This method is influenced by the subjectivity of surveyors. Therefore, the assessment using the visual method should be correlated with the assessment method using tools, in order to reduce the subjectivity of road assessment. The research location used is Magetan District Road consisting of 5 road segments. The result shows that the r road assessment using the PCI method has a very good condition, and using IRI Roadroid has a Medium condition. There is a negative (r) correlation between PCI and IRI Roadroid, valued at -0.23. The negative correlation shows that both judgments reversed. Comparison of PCI assessment with IRI Roadroid has a low correlation value and with ttest, yields no comparison of correlation. This result is because the PCI and IRI equally assess the pavement, using different methods.

scholarly journals TINJAUAN KONDISI PERKERASAN JALAN DENGAN KOMBINASI NILAI INTERNATIONAL ROUGHNESS INDEX (IRI) DAN SURFACE DISTRESS INDEX (SDI) PADA JALAN TAKENGON – BLANGKEJEREN

2018 ◽  
Vol 1 (3) ◽  
pp. 543-552
Author(s):  
Baihaqi Baihaqi ◽  
Sofyan M. Saleh ◽  
Renni Anggraini
Keyword(s):  
Road Surface ◽  
Road Segment ◽  
International Roughness Index ◽  
Damage Level ◽  
The Road ◽  
Road Pavement ◽  
Roughness Index ◽  
Surface Distress ◽  
Observation Method ◽  
Pavement Damage

Abstract: Takengon - Blangkejeren road is one of the cross national roads connecting Central Aceh Regency with Gayo Lues Regency. This road is in the mountainous terrain and often passed by heavy loaded vehicles so that often damaged. To overcome the frequent damage to this road segment, it is necessary to conduct a research on road pavement damage. The purpose of this research is to know the condition of road damage based on the combination of International Roughness Index (IRI) and Surface Distress Index (SDI). This study uses direct observation method in the field by conducting a visual survey of road pavement conditions. The result of the research shows that the total damage level of road surface is 30,54% while the road surface is not damaged by 69,46% from total of road that become research object, that is 12,63 Km divided into 6 road segment. For the overall condition of roads reviewed 45.02% good, 45.81% medium, 6.87% lightly damaged, 2.29% heavily damaged.Abstrak: Ruas jalan Takengon – Blangkejeren merupakan salah satu ruas jalan nasional lintas tengah yang menghubungkan Kabupaten Aceh Tengah dengan Kabupaten Gayo Lues. Jalan ini berada pada medan pegunungan dan sering dilalui kendaraan dengan beban yang berat sehingga sering mengalami kerusakan. Untuk mengatasi kerusakan yang sering terjadi pada ruas jalan ini perlu diadakan suatu penelitian mengenai jenis kerusakan perkerasan jalan. Tujuan dari penelitian ini adalah untuk mengetahui kondisi kerusakan jalan berdasarkan kombinasi nilai International Roughness Index (IRI) dan Surface Distress Index (SDI). Penelitian ini menggunakan metode pengamatan langsung dilapangan dengan melakukan survey secara visual terhadap kondisi perkerasan jalan. Dari hasil penelitian diperoleh tingkat kerusakan keseluruhan permukaan jalan adalah sebesar 30,54% sedangkan permukaan jalan yang tidak mengalami kerusakan sebesar 69,46 % dari total panjang jalan yang menjadi objek penelitian, yaitu 12,63 Km yang dibagi menjadi 6 buah segmen jalan. Untuk kondisi keseluruhan jalan yang ditinjau 45,02 % baik, 45,81 % sedang, 6,87 % rusak ringan, 2,29 % rusak berat.

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