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Hajek, Jerry J., Thomas J. Kazmierowski, and Graham Musgrove. "Switching to International Roughness Index." Transportation Research Record: Journal of the Transportation Research Board 1643, no. 1 (January 1998): 116–24. http://dx.doi.org/10.3141/1643-15.
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The International Roughness Index (IRI) has become a well-recognized standard for measurement of pavement roughness. The main objectives of the study were to evaluate the consequences of switching to IRI roughness measurements, and to develop a procedure for switching from measuring roughness with a response-type device, used for more than 10 years, to an IRI device. The study consisted of two parts. In the first part, the repeatability and consistency of roughness measurements obtained by three different IRI-measuring systems using a 10-section calibration circuit was evaluated. In the second part, transfer functions relating IRI with a subjectively measured ride condition rating for a large pavement network consisting of asphaltic concrete, rigid, and surface-treated pavements were developed. Based on the results of the calibration circuit, the three IRI-measuring systems were proved equally capable of providing repeatable and reliable roughness measurements for network-level monitoring purposes, and their individual results correlated very well. However, because of systematic differences between the results, the IRI-measuring systems cannot be used interchangeably and without proper calibration. Based on the results obtained for the network, different transfer functions were required and developed for the four pavement types (asphaltic concrete, composite, jointed portland cement concrete, and surface-treated). IRI roughness measurements provided better prediction of the ride condition rating than the response-type roughness measurements. These results support the switch to IRI roughness measurements.
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Dahlstedt, Sven. "Smooth Enough?: Estimated Roughness on Roads with Low International Roughness Index Values." Transportation Research Record: Journal of the Transportation Research Board 1860, no. 1 (January 2003): 144–51. http://dx.doi.org/10.3141/1860-16.
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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.
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Setiawan, Arief, Novita Pradani, and Ferra Claudia Masoso. "PEMANFAATAN APLIKASI SMARTPHONE UNTUK MENGUKUR KEMANTAPAN PERMUKAAN JALAN BERDASARKAN INTERNATIONAL ROUGHNESS INDEX." Jurnal Transportasi 19, no. 3 (January 6, 2020): 205–14. http://dx.doi.org/10.26593/jt.v19i3.3673.205-214.
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Abstract An assessment of road surface conditions is needed to determine an appropriate road evaluation program. One of the parameters used is the International Roughness Index or IRI. Currently, technological developments encourage the use of smartphone applications as a tool to determine the value of IRI. Comparisons between IRIs obtained using tools, such as roughometers, and IRIs obtained from software applications have not been made. The purpose of this study was to analyze the relationship between the results of the measurement of the roughometer and the results of the Android application Roadbump Pro. This research was carried out on the Sam Ratulangi Road in Palu City, with a segment length of 600 meters and analyzed per 100 meters. The results of this study indicate that smartphone applications provide good IRI measurement results, so they can be used in road stability assessments. In addition, the type of survey vehicle did not have a significant effect on IRI measurements. Keywords: smartphone, International Roughness Index, roughometer, Roadbump, road stability Abstrak Penilaian kondisi permukaan jalan diperlukan untuk menentukan program evaluasi jalan yang tepat. Salah satu parameter yang digunakan adalah International Roughness Index atau IRI. Saat ini, perkembangan teknologi mendorong penggunaan aplikasi smartphone sebagai alat bantu untuk menentukan nilai IRI. Perbandingan antara IRI yang diperoleh dengan menggunakan alat bantu, seperti roughometer, dan IRI yang diperoleh dari aplikasi perangkat lunak belum dilakukan. Tujuan penelitian ini adalah menganalisis hubungan antara hasil pengukuran alat roughometer dan hasil aplikasi android Roadbump Pro. Penelitian ini dilakukan di ruas Jalan Sam Ratulangi di Kota Palu, dengan panjang segmen 600 meter dan dianalisis per 100 meter. Hasil penelitian ini menunjukkan bahwa aplikasi smartphone memberikan hasil pengukuran IRI yang baik, sehingga dapat digunakan dalam penilaian kemantapan jalan. Selain itu, jenis kendaraan survei tidak memberikan pengaruh yang signifikan terhadap pengukuran IRI. Kata-kata kunci: smartphone, International Roughness Index, roughometer, Roadbump, kemantapan jalan
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Tian, Yu, Shifu Liu, Le Liu, and Peng Xiang. "Optimization of International Roughness Index Model Parameters for Sustainable Runway." Sustainability 13, no. 4 (February 18, 2021): 2184. http://dx.doi.org/10.3390/su13042184.
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Pavement roughness is a critical airport pavement characteristic that has been linked to impacts such as safety and service life. A properly defined roughness evaluation method would reduce airport operational risk, prolong the life of aircraft landing gear, and optimize the decision-making process for pavement preservation, which together positively contribute to overall airport sustainability. In this study, we optimized the parameters of the International Roughness Index (IRI) model to resolve the current poor correlation between the IRI and aircraft vibration responses in order to adapt and extend the IRI’s use for airport runway roughness evaluation. We developed and validated a virtual prototype model based on ADAMS/Aircraft software for the Boeing 737–800 and then employed the model to predict the aircraft’s dynamic responses to runway pavement roughness. By developing a frequency response function for the standard 1/4 vehicle model, we obtained frequency response distribution curves for the IRI. Based on runway roughness data, we used fast Fourier transform to implement the frequency response distribution of the aircraft. We then utilized Particle Swarm Optimization to determine more appropriate IRI model parameters rather than modifying the model itself. Our case study results indicate that the correlation coefficient for the optimized IRI model and aircraft vibration response shows a qualitative leap from that of the original IRI model.
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Cumbaa, Steven L. "Using the International Roughness Index for Profilograph Trace Reduction." Transportation Research Record: Journal of the Transportation Research Board 1536, no. 1 (January 1996): 90–93. http://dx.doi.org/10.1177/0361198196153600113.
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Current Louisiana acceptance specifications for 100 percent payment require Profile Indexes (PIs) completed pavement surface to as low as 3.0 in./mi for flexible pavements and 6.0 in./mi for rigid pavements. These acceptance specifications are currently among the most stringent in the United States. Requiring an acceptance PI value of less than 7.0 in./mi when using the California-style profilograph and the 0.2 in. blanking band is unacceptable unless the blanking band is eliminated from the procedure. Louisiana's efforts to replace the blanking-band evaluation procedure with a procedure that inputs the profile trace into the quarter-car-based International Roughness Index (IRI) model are presented. The key step in this process is the scanning and digitization of the profilo-gram before determining the IRI based on the filtered profile trace. A much better correlation exists between the rideability of the finished surface and the profilograph IRI than that of the profilograph PI. New profilograph IRI specifications are recommended to replace the existing PI specifications.
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Pawar, Prashant R., Arun Tom Mathew, and M. R. Saraf. "IRI (International Roughness Index): An Indicator Of Vehicle Response." Materials Today: Proceedings 5, no. 5 (2018): 11738–50. http://dx.doi.org/10.1016/j.matpr.2018.02.143.
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Pembuain, Ardilson, Sigit Priyanto, and Latif Budi Suparma. "Evaluasi Kemantapan Permukaan Jalan Berdasarkan International Roughness Index Pada 14 Ruas Jalan di Kota Yogyakarta." TEKNIK 39, no. 2 (March 14, 2019): 132. http://dx.doi.org/10.14710/teknik.v39i2.21459.
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Jalan yang memiliki kekasaran (roughness) permukaan yang buruk dapat menyebabkan ketidaknyamanan bagi pengguna jalan, kecelakaan lalu lintas, peningkatan beban dinamis pada permukaan jalan sehingga mempercepat proses kerusakan jalan, serta kerusakan kendaraan. Penelitian ini bertujuan untuk mengevaluasi kemantapan kondisi jalan berdasar nilai international roughness index (IRI). Evaluasi kondisi kemantapan jalan dilakukan pada 14 ruas jalan di Kota Yogyakarta, dengan perincian 4 ruas jalan arteri sekunder dan 10 ruas jalan kolektor sekunder. Data nilai kekasaran permukaan jalan (IRI) diperoleh dengan menggunakan alat NAASRA roughness meter yang mengacu pada SNI 03-3426-1994. Evaluasi kemantapan kondisi jalan dilakukan dengan membandingkan nilai IRI hasil survei dan batasan nilai IRI yang ditetapkan oleh Direktorat Jenderal Bina Marga. Hasil penelitian menunjukkan 14 ruas jalan yang dievauasi 64% dalam kondisi sedang dan 36% dalam kondisi baik. Dari ke-14 ruas jalan tersebut, ruas jalan Sisingamangaraja, Lowanu, dan Sugeng Jeroni memiliki nilai IRI tertinggi secara bururutan sehingga ketiga ruas jalan tersebut lebih diprioritaskan untuk mendapatkan penanganan
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Abed, Muataz Safaa. "Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index." Journal of Engineering 26, no. 12 (December 1, 2020): 81–94. http://dx.doi.org/10.31026/j.eng.2020.12.05.
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Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of each observed distress, the pavement condition surveys were conducted by actually walking through all the sections. Using these data, PCI was calculated utilizing Micro PAVER software. Dynatest Road Surface Profiler (RSP) was used to collect IRI data of all the sections. Using the SPSS software, linear and nonlinear regressions have been used for developing two models between PCI and IRI based on the collected data. These models have the coefficients of determination (R2) equal to 0.715 and 0.722 for linear and quadratic models. Finally, the results indicate the linear and quadratic models are acceptable to predict PCI from IRI directly.
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Du, Yuchuan, Chenglong Liu, Difei Wu, and Shengchuan Jiang. "Measurement of International Roughness Index by UsingZ-Axis Accelerometers and GPS." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/928980.
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The International Roughness Index (IRI) is a well-recognized standard in the field of pavement management. Many different types of devices can be used to measure the IRI, but these devices are mainly mounted on a full-size automobile and are complicated to operate. In addition, these devices are expensive. The development of methods for IRI measurement is a prerequisite for pavement management systems and other parts of the road management industry. Based on the quarter-car model and the vehicle vibration caused by road roughness, there is a strong correlation between the in-carZ-axis acceleration and the IRI. The variation of speed of the car during the measurement process has a large influence on IRI estimation. A measurement system equipped withZ-axis accelerometers and a GPS device was developed. Using the self-designing measurement system based on the methodology proposed in this study, we performed a small-scale field test. We used a one-wheel linear model and two-wheel model to fit the variation of theZ-axis acceleration. The test results demonstrated that the low-cost measurement system has good accuracy and could enhance the efficiency of IRI measurement.
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Abd El-Hakim, Ragaa, and Sherif El-Badawy. "International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application." Advanced Materials Research 723 (August 2013): 854–60. http://dx.doi.org/10.4028/www.scientific.net/amr.723.854.
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nternational Roughness Index (IRI) is an important parameter that indicates the ride quality and pavement condition. In this study, an Artificial Neural Network (ANN) model was developed to predict the IRI for Jointed Plain Concrete Pavement (JPCP) sections. The inputs for this model are: initial IRI value, pavement age, transverse cracking, percent joints spalled, flexible and rigid patching areas, total joint faulting, freezing index, and percent subgrade passing No. 200 U.S. sieve. This data was obtained from the Long Term Pavement Performance (LTPP) Program. It is the same data and inputs used for the development of the Mechanistic-Empirical pavement Design Guide (MEPDG) IRI model for JPCP. The data includes a total of 184 IRI measurements. The results of this study shows that using the same input variables, the ANN model yielded a higher prediction accuracy (coeficint of determination: R2= 0.828, and ratio of standard error of estimate (predicted) to standard deviation of the measured IRI values: Se/Sy=0.414) compared to the MEPDG model (R2= 0.584, Se/Sy=0.643). In addition, the bias in the predicted IRI values using the ANN model was significantly lower compared to the MEPDG regression model.
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Abudinen, Daniel, Luis G. Fuentes, and Juan S. Carvajal Muñoz. "Travel Quality Assessment of Urban Roads Based on International Roughness Index: Case Study in Colombia." Transportation Research Record: Journal of the Transportation Research Board 2612, no. 1 (January 2017): 1–10. http://dx.doi.org/10.3141/2612-01.
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Roughness is the main feature of the pavement surface that defines user comfort. Pavement roughness is generally defined as irregularities in the pavement surface that adversely affect ride quality, specifically user perception of the road condition. This paper highlights the limitations associated with the evaluation and implementation of the international roughness index (IRI) on urban roads. The paper focuses on ( a) roughness evaluation with full-scale profilers and ( b) conditions particular to urban roads—namely, traffic, intersections, and operating speeds. Given that the speed of urban networks is typically less than the 80 km/h used in the IRI quarter-car model, the implementation of the IRI model on urban roads was evaluated. Even though a given pavement surface reported a unique IRI value, user experience of the profile depended on the travel speed. This result was evidence that user perceptions of road condition are highly influenced by travel speed. The results suggested the need to develop a roughness index that captures the unique characteristics of urban roads and can estimate the road condition as perceived by users. For that reason, this research study focused on establishing thresholds for IRI on the basis of the weighted vertical acceleration parameter as an aid to assessing user perception. The proposed method allows the maximum allowable IRI value for a given road to be established on the basis of the road’s operational speed. The results indicated that IRI thresholds agreed with international and local Colombian standards.
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Adeli, Sasan, Vahid Najafi moghaddam Gilani, Mohammad Kashani Novin, Ehsan Motesharei, and Reza Salehfard. "Development of a Relationship between Pavement Condition Index and International Roughness Index in Rural Road Network." Advances in Civil Engineering 2021 (August 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/6635820.
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The main objective of this paper was to investigate the relationship between PCI and IRI values of the rural road network. To this end, 6000 pavement sections of the rural road network in Iran were selected. Road surface images and roughness linear profiles were collected using an automated car to calculate PCI and IRI, respectively. Three exponential regression models were developed and validated in three different IRI intervals. Analysis of the results indicated that exponential regression was the best model to relate IRI and PCI. In these models, R2 values were found to be acceptable, equal to 0.75, 0.76, and 0.59 for roads with good, fair, and very poor qualities, respectively, indicating a good relationship between IRI and PCI. Moreover, validation results showed that the model had a high accuracy. Also, the relation between IRI and PCI became weaker as a result of increasing the level of road surface roughness, which can be caused by the increase in the number and severity of failures. Furthermore, two failures of rail R.C. and rutting were rarely observed in the studied roads. Therefore, the proposed model is more applicable for roads without the mentioned failures and asphalt-pavement rural road network.
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Park, Young Suk, Dong Ku Shin, and Tae Ju Chung. "Influence of road surface roughness on dynamic impact factor of bridge by full-scale dynamic testing." Canadian Journal of Civil Engineering 32, no. 5 (October 1, 2005): 825–29. http://dx.doi.org/10.1139/l05-040.
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Effects of road surface roughness on the dynamic impact factor of bridge are investigated through full-scale field loading tests under controlled traffic conditions. The dynamic time histories of displacements are obtained for twenty-five bridges on Korean highways. The impact factors of the bridges are evaluated by using the measured displacements. The road surface profiles of the twenty-five bridges are also measured at every 10 to 30 cm interval in the span direction. By using the measured road surface profiles, the international roughness index (IRI) and the roughness coefficients of the bridges are evaluated. The linear regression and correlation analyses are performed to obtain the coherences between the IRI and the roughness coefficient and between the IRI and the impact factor. The sample correlation coefficients between the impact factor and the IRI and between the impact factor and the roughness coefficient are calculated to be 0.61 and 0.62, respectively, showing a strong coherence between the road surface roughness and the impact factor.Key words: bridge, impact factor, road surface roughness, international roughness index, roughness coefficient.
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Joni, Hasan H., Miami M. Hilal, and Muataz S. Abed. "Developing International Roughness Index (IRI) Model from visible pavement distresses." IOP Conference Series: Materials Science and Engineering 737 (March 6, 2020): 012119. http://dx.doi.org/10.1088/1757-899x/737/1/012119.
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Mamlouk, Michael, Mounica Vinayakamurthy, B. Shane Underwood, and Kamil E. Kaloush. "Effects of the International Roughness Index and Rut Depth on Crash Rates." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 40 (June 21, 2018): 418–29. http://dx.doi.org/10.1177/0361198118781137.
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Pavement distresses directly affect ride quality, and indirectly contribute to driver distraction, vehicle operation, and accidents. In this study, analysis was performed on highways in the states of Arizona, North Carolina, and Maryland to investigate the relationship between accident rate and pavement ride quality (roughness) and rut depth. Two main types of data were collected: crash data from the accident records and International Roughness Index (IRI) and rut depth data from the pavement management system database in each state. Crash rates were calculated using the U.S. Department of Transportation method, which is the number of accidents per 100 million vehicle-miles of travel. Sigmoidal function regression analysis was performed to study the relationship between crash rate and both IRI and rut depth. In all cases, the crash rate did not show substantial increases until an IRI value of 210 inches/mile or a critical rut depth of 0.4 inches. When the IRI or rut depth increased above these values the crash rate increased. This is a key conclusion that provides empirically derived thresholds for IRI and rut depth to reducing the accident rate.
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Psalmen Hasibuan, Rijal, and Medis Sejahtera Surbakti. "Study of Pavement Condition Index (PCI) relationship with International Roughness Index (IRI) on Flexible Pavement." MATEC Web of Conferences 258 (2019): 03019. http://dx.doi.org/10.1051/matecconf/201925803019.
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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.
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Capuruço, Renato A. C., Tarek Hegazy, Susan L. Tighe, and Sameh Zaghloul. "Full-Car Roughness Index as Summary Roughness Statistic." Transportation Research Record: Journal of the Transportation Research Board 1905, no. 1 (January 2005): 148–56. http://dx.doi.org/10.1177/0361198105190500116.
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The international roughness index (IRI) and the half-car roughness index (HRI) are the two commonly used roughness indices for pavement management, decision making, prioritization, budgeting, and planning. This work presents a new statistic, termed the full-car roughness index (FRI), for calculation of roughness from longitudinal pavement profiles. FRI is calculated from a single, equivalent profile that is a composite of four corner profiles based on both civil and mechanical engineering principles. More specifically, the full-car (four-wheel) model combines the rear and front suspension systems through an interdependent relation of motion with the longitudinal axle. To validate this model, the FRI values for different pavement sections are determined for sampling roughness measurements from several states and provinces. Then, the behavior of FRI is compared with that of IRI and HRI. The methodology of assessment uses a Monte Carlo simulation for calibration and validation of the index. Correlations derived from this sensitivity analysis on the basis of regression analysis arrive at a conversion chart to propose conversion values from these indices to FRIs. Overall, this paper suggests that the mechanical response of the proposed full-car model is more representative of the characteristics of a real vehicle than the response of a quarter- or half-car model. The results also indicate that FRI is less sensitive to the governing factors that account for the quarter-car simulation and thus provides an index that is unique, insightful, and more effective in the characterization of ride quality.
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Mucka, Peter, George Juraj Stein, and Peter Tobolka. "Passsenger Ride Comfort and International Roughness Index Specifications in the Slovak Republic." Communications - Scientific letters of the University of Zilina 21, no. 1 (February 20, 2019): 14–21. http://dx.doi.org/10.26552/com.c.2019.1.14-21.
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New original results are presented on relation between passenger’s whole-body vibration (WBV) and longitudinal road unevenness characterised by the International Roughness Index (IRI) in 100-m segments. Measurements were provided in nine different cars of six vehicle categories operated on about 1860 km of road network. Vibration total value based on the root mean square (RMS) of the frequency-weighted acceleration was used to quantify the ride comfort at seat surface and seat base (i.e. vehicle floor) in three orthogonal axes. The relations between passenger’s acceleration response, comfort reaction levels according to the ISO 2631-1: 1997 and the IRI road unevenness classes, used by the Slovak Road Administration, were estimated. Results indicated higher WBV by ~ 20 % on the motorways than on the 1st and 2nd class roads in the same IRI road class. Using the same IRI road classes for motorways and the 1st and 2nd class roads seems not to be appropriate from the point of view of the whole-body vibrations.
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Uechi, Schun T., and Hiroshi Uechi. "The Profiling of International Roughness Index (IRI) Based on Lagrangian Method." World Journal of Engineering and Technology 06, no. 04 (2018): 885–902. http://dx.doi.org/10.4236/wjet.2018.64059.
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Liu, Shao Wen, and Xiao Zhang. "Relationship between International Roughness Index and Power Spectral Density of Asphalt Pavements in China." Advanced Materials Research 742 (August 2013): 104–8. http://dx.doi.org/10.4028/www.scientific.net/amr.742.104.
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In this paper, pavement roughness is assumed as random stationary variable and used as the exciting force of theoretical analyses of the quarter car model of International Roughness Index (IRI). From the frequency response function of the quarter car, the response function of the displacement difference between sprung and unsprung mass is obtained based on random process theory. Then the relationship between IRI and power spectral density (PSD) is established from statement characteristic of the response function. Finally, the longitudinal road profiles of typical asphalt roads in China are used to validate the proposed model.
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Qian, Jinsong, Chen Jin, Jiake Zhang, Jianming Ling, and Chao Sun. "International Roughness Index Prediction Model for Thin Hot Mix Asphalt Overlay Treatment of Flexible Pavements." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 40 (May 4, 2018): 7–13. http://dx.doi.org/10.1177/0361198118768522.
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Pavement performance prediction after maintenance and rehabilitation is important to pavement management. A two-parameter exponential international roughness index (IRI) regression model for thin hot mix asphalt overlay was developed based on information from the U.S. Long Term Pavement Performance (LTPP) database. The model influence parameters α and β, which represent the initial IRI as the thin overlay completion and shape factor of IRI deterioration curve, were statistically analyzed. The results suggested that the IRI deterioration trends in high-temperature and low-temperature regions are different. This is because β was mainly affected by the structural strength and equivalent single axle loads in the high and medium temperature region and mainly affected by the average annual precipitation in low temperature region. In-situ data from LTPP database was used to verify the IRI prediction model, and it was found that the predicted IRI and measured IRI exhibited similar trends.
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Chen, Shong-Loong, Chih-Hsien Lin, Chao-Wei Tang, Liang-Pin Chu, and Chiu-Kuei Cheng. "Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City." Sustainability 12, no. 24 (December 16, 2020): 10536. http://dx.doi.org/10.3390/su122410536.
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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.
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Samsuri, Saleh, Medis Surbakti, Ahmad Perwira Tarigan, and Ridwan Anas. "A Study on the Road Conditions Assessment Obtained from International Roughness Index (IRI): Roughometer Vs Hawkeye." Simetrikal: Journal of Engineering and Technology 1, no. 2 (September 28, 2019): 103–13. http://dx.doi.org/10.32734/jet.v1i2.756.
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Study is aimed to find out what is the representation of IRI (International Roughness Index) from the Roughometer results if it was used as substitute of IRI from the Hawkeye results on the road conditions assessment, which is the hawkeye device is included in the Class I category of roughness measurement devices, while the Roughometer is in the Class III. The Student’s t statistical operation is used to find the representation of IRI from the Roughometer results as substitute of IRI from the Hawkeye results. It is determined by analyzing the comparison of the mean values of both measurement results. The study was conducted on three national road sections in North Sumatra Province, namely: Bts. Kota Binjai – Bts. Kota Medan road with a length of 7,300 meters, Bts. Kota Tebing Tinggi – Bts. Kabupaten Simalungun road with a length of 18,800 meters, and Bts. Kabupaten Simalungun/Bts. Kabupaten Sergai road with a length of 15,000 meters. The IRI values were measured by using Roughometer and Hawkeye devices. The measurements were carried out with the survey team from the Center for Implementation of the National Road II Medan, which was also the facilitators in providing the survey equipment, Roughometer and Hawkeye. The statistical test results that the IRI values from the Roughometer measurement results were significantly different from the IRI values from the Hawkeye measurement results (Ho was rejected) because the Student’s t-test results for the three road sections showed that tcount > tcritical and p-value < 0.05. And the assessment of the road functional conditions using Roughometer showed the same results on one road section but worse results on the other two road sections compared to assessment of the functional conditions with Hawkeye. Based on the analysis results, it can be concluded that the IRI values from Roughometer were more conservative in representing the functional conditions of the road when used as a substitute for the IRI values from Hawkeye.
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Wang, Zheng, Wei Zhang, Jia Jia Cheng, Meng Chen, and Wen Jing Liu. "Control Research on Asphalt Pavement Roughness in Anchorage Zone." Advanced Materials Research 1030-1032 (September 2014): 754–57. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.754.
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The effect of pavement roughness on the roadbed and retaining structure underground was studied. Three different groups of International Roughness Index (IRI) were analyzed based on 6221 data collection instrument in this paper. Results show that different pavement roughness has different effect on retaining structure. Additionally, the vibration RMS increases with IRI when it is in the range of normal driving for the car, but amplitude of the IRI is larger. Finally, the main factors which influence the stability of structure and some corresponding improving measures are presented.
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OBrien, Eugene J., Abdolrahim Taheri, and Abdollah Malekjafarian. "An alternative roughness index to IRI for flexible pavements." Canadian Journal of Civil Engineering 45, no. 8 (August 2018): 659–66. http://dx.doi.org/10.1139/cjce-2017-0443.
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The International Roughness Index (IRI) is widely accepted as a measure of pavement condition. However it was developed as an indicator of passenger comfort as they travel in a vehicle on the pavement. In this paper, for the assumed failure model adopted, IRI is not found to be a good indicator of remaining pavement service life. The 3D continuous wavelet transform is proposed and shown to be a more effective indicator. Specific scales related to natural frequencies of the vehicle fleet, particularly the body mass frequency, are more significant than others. A weighted mean of the wavelet coefficients for these scales is used as an indicator of remaining life. One hundred randomly generated class A profiles are generated and their histories of damage progression throughout their lives are simulated. The new indicator is applied to the initial profiles to determine which ones are more vulnerable to damage than others. In numerical simulations using the assumed damage model, the wavelet based indicator is shown to be well correlated with service life.
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Nguyen, Hoang-Long, Binh Thai Pham, Le Hoang Son, Nguyen Trung Thang, Hai-Bang Ly, Tien-Thinh Le, Lanh Si Ho, Thanh-Hai Le, and Dieu Tien Bui. "Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction." Applied Sciences 9, no. 21 (November 5, 2019): 4715. http://dx.doi.org/10.3390/app9214715.
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The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.
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Louhghalam, Arghavan, Mehdi Akbarian, and Franz-Joseph Ulm. "Roughness-Induced Pavement–Vehicle Interactions." Transportation Research Record: Journal of the Transportation Research Board 2525, no. 1 (January 2015): 62–70. http://dx.doi.org/10.3141/2525-07.
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Pavement roughness affects rolling resistance and thus vehicle fuel consumption. When a vehicle travels at constant speed on an uneven road surface, the mechanical work dissipated in the vehicle's suspension system is compensated by vehicle engine power and results in excess fuel consumption. This dissipation depends on both road roughness and vehicle dynamic characteristics. This paper proposes, calibrates, and implements a mechanistic model for roughness-induced dissipation. The distinguishing feature of the model is its combination of a thermodynamic quantity (energy dissipation) with results from random vibration theory to identify the governing parameters that drive the excess fuel consumption caused by pavement roughness, namely, the international roughness index (IRI) and the waviness number, w (a power spectral density parameter). It is shown through sensitivity analysis that the sensitivity of model output, that is, excess fuel consumption, to the waviness number is significant and comparable to that of IRI. Thus, introducing the waviness number as a second roughness index, in addition to IRI, allows a more accurate quantification of the impact of surface characteristics on vehicle fuel consumption and the corresponding greenhouse gas emissions. This aspect is illustrated by application of the roughness–fuel consumption model to two road profiles extracted from FHWA's Long-Term Pavement Performance database.
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Vaillancourt, Michel, Daniel Perraton, Pierre Dorchies, and Guy Doré. "Décomposition du pseudo-profil et analyse de l'indice de rugosité international (IRI)." Canadian Journal of Civil Engineering 30, no. 5 (October 1, 2003): 923–33. http://dx.doi.org/10.1139/l03-070.
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In order to get the most out data from a pseudo-profile and to further develop an international roughness index (IRI) concept, a new interpretation approach of the IRI is presented. It is based on the breakdown of the initial pseudo-profile of a pavement into elementary pseudo-profiles based on various well-defined wavelengths. The underlying hypothesis to this approach is that the IRI calculated on these elementary pseudo-profiles is proportional to the contribution of each one to pavement condition. From this hypothesis, various pseudo-profile analytical techniques are presented. A few definitions and context are presented first. The following approach is then described: (i) filtering technique, (ii) calculation of the IRI for elementary pseudo-profiles, (iii) additional analytical tools. Finally, an application example describes the approach.Key words: IRI, filtering, pseudo-profile, pavement, defect, wavelength, bandwidth, profilometre, evenness.[Journal Translation]
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Fernando, Emmanuel G. "Evaluation of Relationship Between Profilograph and Profile-Based Roughness Indexes." Transportation Research Record: Journal of the Transportation Research Board 1699, no. 1 (January 2000): 121–26. http://dx.doi.org/10.3141/1699-17.
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The relationship between the profilograph profile index (PI) and the international roughness index (IRI) is evaluated. To accomplish this evaluation, profile data taken on 48 overlaid test sections were used in profilograph simulations to predict the profilograph response to the measured profiles. The PIs determined were then correlated with IRIs computed from the profile data to evaluate relationships between these roughness statistics. The results show that the PI based on the null blanking band is more strongly related to the IRI than the corresponding index determined using the 5-mm blanking band. In view of the general acceptance of the IRI as a statistic for establishing surface smoothness based on profiles, the results suggest that a profilograph specification based on the null blanking band is preferable to a similar specification based on the 5-mm blanking band, which may mask certain components of roughness that are otherwise picked up if no blanking band is used.
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Mactutis, Joseph A., Sirous H. Alavi, and Weston C. Ott. "Investigation of Relationship Between Roughness and Pavement Surface Distress Based on WesTrack Project." Transportation Research Record: Journal of the Transportation Research Board 1699, no. 1 (January 2000): 107–13. http://dx.doi.org/10.3141/1699-15.
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Modern pavement rehabilitation and design methodologies require an adequate evaluation of the functional capacity of pavements. A key component of this functional capacity is the roughness of the pavement. The current standard for characterization of a pavement’s roughness is the international roughness index (IRI). Pavement roughness measurements were conducted at regular intervals during the application of approximately 5 million equivalent single-axle loads at the WesTrack Project, a full-scale flexible pavement accelerated loading facility located near Reno, Nevada. The results are presented of an investigation into the relationship between pavement roughness and pavement surface distress using WesTrack data. With a sample population of 317 observations, a relationship was found among the roughness (IRI) and the initial IRI, percentage of fatigue cracking, and average rut depth. A test of the relationship with data collected as a part of the Long-Term Pavement Performance Program indicates favorable results.
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Hanson, Trevor, Coady Cameron, and Eric Hildebrand. "Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values." Canadian Journal of Civil Engineering 41, no. 9 (September 2014): 819–27. http://dx.doi.org/10.1139/cjce-2014-0183.
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International roughness index (IRI) values were calculated from multi-step processing of accelerometer data collected using three smartphone devices in three consumer vehicles under 11 test scenarios on a 1000 m stretch of secondary highway in New Brunswick. These data were compared to IRI data from a Class 1 inertial profiler averaged over 1000 m (2.60 m/km, std. dev. = 0.029). The combinations of factors producing average IRI values closest to Class 1 inertial profiler were the compact car, Galaxy SIII, windshield mount, at 80 km/h (2.58 m/km, std. dev. = 0.075) and the SUV, iPhone 5, windshield mount, at 50 km/h (2.63 m/km, std. dev. = 0.054). Changes in device type, vehicle type, and mounting arrangement significantly impacted IRI variance, while vehicle speed (50 km/h and 80 km/h) did not. The development of correction factors and analysis automation could make these devices a low-cost option for real-time network-level pavement management.
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Shafizadeh, Kevan, and Fred Mannering. "Acceptability of Pavement Roughness on Urban Highways by Driving Public." Transportation Research Record: Journal of the Transportation Research Board 1860, no. 1 (January 2003): 187–93. http://dx.doi.org/10.3141/1860-21.
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The driving public’s attitude toward acceptable levels of road roughness is explored using empirical data collected on urban highways. Individual driver acceptability levels are matched with international roughness index (IRI) levels to examine the existence of potential user acceptability thresholds. In particular, the observed trends are compared with the federal IRI guideline of 170 in./mi (2.7 m/km) for acceptable ride quality recommended by FHWA in its 1998 strategic plan for the National Highway System. The research reported on appears to provide empirical support for the federal IRI guidelines that are already in existence. This study also found that IRI levels provided a very good indication of driver acceptability, which agrees with past research based on antiquated present serviceability ratings.
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Aleksikov, S. V., A. I. Leskin, D. I. Gofman, and M. I. Alshanova. "Mathematical model for optimizing the repair plan for the international roughness index IRI." IOP Conference Series: Materials Science and Engineering 913 (September 12, 2020): 042067. http://dx.doi.org/10.1088/1757-899x/913/4/042067.
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Uechi, Schun T., and Hiroshi Uechi. "A Mechanical Vibration-induced Electric Energy Generation (MVEG) and Applications to Ride Quality of Vehicles and International Roughness Index (IRI)." Studies in Engineering and Technology 6, no. 1 (May 28, 2019): 59. http://dx.doi.org/10.11114/set.v6i1.4301.
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A mechanical vibration-induced, electric energy harvesting method is discussed with applications to vibration analyses of systems of vehicles, motorboats, trains, machines and bridges, etc.. The research has evolved from the analysis of International Roughness Index (IRI), which studies roughness of road-surface as longitudinal vibrational motions in a vehicle measured with a quarter-car simulation (QCS) or Global Positioning System (GPS) with sensors such as gyro sensor and magnetometer sensor. The electric energy-convertible vibrations with information of roughness of road surface are extracted by way of an mechanoelectric energy conversion, and an energy harvesting technology suitable for the system of vehicles is discussed. The mechanical vibration-induced electric current is also suitable for IRI information measurement as well as a measure for ride quality of vehicles.
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Popoola, M. O., O. A. Apampa, and O. Adekitan. "Impact of Pavement Roughness on Traffic Safety under Heterogeneous Traffic Conditions." Nigerian Journal of Technological Development 17, no. 1 (April 22, 2020): 13–19. http://dx.doi.org/10.4314/njtd.v17i1.2.
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H ighway safety is a major priority for public use and for transportation agencies. Pavement roughness indirectly influence drivers' concentration, vehicle operation, and road traffic accidents, and it directly affect ride quality. This study focuses on analyzing the influence of pavement roughness on traffic safety using traffic, pavement and accident data on dual and single carriageway operated under heterogeneous traffic conditions in South-west, Nigeria. Traffic crash data between 2012 and 2015 was obtained from the Federal Road Safety Commission (FRSC) and International Roughness Index (IRI) data from the Pavement Evaluation Unit of the Federal Ministry of Works, Kaduna. Crash road segments represented 63 percent of the total length of roads. IRI values for crash and non-crash segments was a close difference of 0.3,This indicates that roughness is not the only factors affecting occurrence of traffic crashes but a combination with other factors such as human error, geometric characteristics and vehicle conditions. Crash severity was categorized into Fatal, serious and minor injury crashes. In all cases, the total crash rate increases with increase in IRI value up to a critical IRI value of 4.4 and 6.15 for Sagamu-Ore road and Ilesha-Akure-Owo road respectively, wherein the crash rate dropped. The conclusion is key in improving safety concerns, if transportation agencies keep their road network below these critical pavement conditions, the crash rate would largely decrease. The study concluded that ride quality does not directly affect traffic crash rate.
Keywords: Pavement conditions, traffic safety, International Roughness Index, crash rate, carriageway.
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Karahancer, Sebnem Sargin, Ekinhan Eriskin, Buket Capali, Serdal Terzi, and Mehmet Saltan. "Route 93, Arizona’s IRI estimation using least squares method and fuzzy logic." Global Journal of Information Technology: Emerging Technologies 7, no. 3 (December 24, 2017): 157–62. http://dx.doi.org/10.18844/gjit.v7i3.2836.
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AbstractServiceability was found to be influenced by longitudinal and transverse profiles as well as the extent of cracking and patching. The amount of weight to assign to each element in the determination of the overall serviceability is a matter of subjective opinion. International roughness index of highway pavements has been estimated by least squares and fuzzy logic methods and compared. For these models, Route 93, Arizona experimental data have been used. Annual freeze –thaw occurring days, depending on years, ha ve been used for modelling. The developed model with least squares method has a high regression value. This approach can be easily and realistically performed to solve problems that do not have a formulation or function for the solution.Keywords: International roughness index, least squares method, modelling, estimation, fuzzy logic.
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Sari, Devita, Sri Sukmawati, and Akhmad Hasanuddin. "THE COMPARISON OF ROAD DAMAGE VALUES BASED ON PCI (PAVEMENT CONDITION INDEX) METHOD OBSERVATION AND IRI (INTERNATIONAL ROUGHNESS INDEX) METHOD ON ROAD CLASS II IN LUMAJANG DISTRICT." Jurnal Rekayasa Sipil dan Lingkungan 3, no. 2 (December 2, 2019): 113. http://dx.doi.org/10.19184/jrsl.v3i2.10904.
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Roads are a means of transportation that is often used by Indonesians to travel. Some class II road segments in Lumajang Regency include Tempeh-Sumberjati, Sumberjati-Karangrejo, Karangrejo-Yosowilangun, and Jalan Lintas Timur. The four sections are collector roads outside the city so that large vehicles often pass. Before handling the road, it must first be assessed on the condition of the pavement. The road surface condition assessment system uses the PCI method and the IRI method. The purpose of this study was to determine the condition of road damage and handling damage to the road so that the maintenance category was obtained. The IRI method is obtained from the Bina Marga Lumajang PU, while the PCI method is done visually. The average results of the four PCI methods are 76.54 with very good conditions and the IRI method is obtained 3.94 with good conditions so that both of these methods produce the same conclusions Maintenance for the PCI method and the IRI method uses routine maintenance, seen from both values.
Jalan merupakan sarana transportasi yang sering digunakan bagi warga Indonesia untuk berpergian. Beberapa ruas jalan kelas II di Kabupaten Lumajang antara lain Tempeh-Sumberjati, Sumberjati-Karangrejo, Karangrejo-Yosowilangun dan Jalan Lintas Timur. Keempat ruas tersebut merupakan jalan kolektor luar kota, sehingga sering dilewati kendaraan bermuataan besar. Sebelum dilakukan penanganan jalan, terlebih dahulu harus dilakukan penilaian kondisi perkerasan jalan. Sistem penilaian kondisi permukaan jalan menggunakan metode PCI dan metode IRI. Tujuan dari penelitian ini untuk mengetahui kondisi kerusakan jalan dan penanganan kerusakan jalan, sehingga didapatkan kategori pemeliharaan. Untuk metode IRI didapatkan dari PU Bina Marga Lumajang, sedangkan metode PCI dilakukan secara visual. Hasil rata-rata keempat ruas metode PCI sebesar 76,54 dengan kondisi sangat baik dan metode IRI didapatkan 3,94 dengan kondisi baik, sehingga kedua metode ini menghasilkan kesimpulan yang sama Pemeliharaan untuk metode PCI dan metode IRI menggunakan pemeliharaan rutin, dilihat dari kedua nilai.
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Gharieb, Mohamed, and Takafumi Nishikawa. "Development of Roughness Prediction Models for Laos National Road Network." CivilEng 2, no. 1 (February 11, 2021): 158–73. http://dx.doi.org/10.3390/civileng2010009.
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The International Roughness Index (IRI) has been accepted globally as an essential indicator for assessing pavement condition. The Laos Road Management System (RMS) utilizes a default Highway Development and Management (HDM-4) IRI prediction model. However, developed IRI values have shown the need to calibrate the IRI prediction model. Data records are not fully available for Laos yet, making it difficult to calibrate IRI for the local conditions. This paper aims to develop an IRI prediction model for the National Road Network (NRN) based on the available Laos RMS database. The Multiple Linear Regression (MLR) analysis technique was applied to develop two new IRI prediction models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections. The final database consisted of 83 sections with 269 observations over a 1850 km length of DBST NRN and 29 sections with 122 observations over a 718 km length of AC NRN. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The model’s parameter analysis confirmed their significance, and R2 values were 0.89 and 0.84 for DBST and AC models, respectively. It can be concluded that the developed models can serve as a useful tool for engineers maintaining paved NRN.
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Zhang, Zong Tao, Quan Man Zhao, and Wan Qiao Yang. "Pavement Roughness Indices Related to Riding Comfort." Applied Mechanics and Materials 505-506 (January 2014): 180–83. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.180.
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The most widely used pavement roughness index is the international roughness index (IRI), but it is a poor predictor of ride comfort. In addition, the rider has not yet been included in the vehicle model used to evaluate pavement roughness. In this paper, in order to evaluate the comfort of the rider directly and consider the effects on ride comfort of pitch movement, a five-degree-freedom vibration model was built when a rider was added to a pitch-plane vehicle model. The vertical weighted root-mean-square (RMS) acceleration of the rider was suggested to be pavement roughness indices, which were related to ride comfort, respectively. The new roughness indices were calculated and a new pavement roughness evaluation method was developed.
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Goenaga, Boris Jesús, Luis Guillermo Fuentes Pumarejo, and Otto Andrés Mora Lerma. "Evaluation of the methodologies used to generate random pavement profiles based on the power spectral density: An approach based on the International Roughness Index." Ingeniería e Investigación 37, no. 1 (January 1, 2017): 49. http://dx.doi.org/10.15446/ing.investig.v37n1.57277.
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The pavement roughness is the main variable that produces the vertical excitation in vehicles. Pavement profiles are the main determinant of (i) discomfort perception on users and (ii) dynamic loads generated at the tire-pavement interface, hence its evaluation constitutes an essential step on a Pavement Management System. The present document evaluates two specific techniques used to simulate pavement profiles; these are the shaping filter and the sinusoidal approach, both based on the Power Spectral Density. Pavement roughness was evaluated using the International Roughness Index (IRI), which represents the most used index to characterize longitudinal road profiles. Appropriate parameters were defined in the simulation process to obtain pavement profiles with specific ranges of IRI values using both simulation techniques. The results suggest that using a sinusoidal approach one can generate random profiles with IRI values that are representative of different road types, therefore, one could generate a profile for a paved or an unpaved road, representing all the proposed categories defined by ISO 8608 standard. On the other hand, to obtain similar results using the shaping filter approximation a modification in the simulation parameters is necessary. The new proposed values allow one to generate pavement profiles with high levels of roughness, covering a wider range of surface types. Finally, the results of the current investigation could be used to further improve our understanding on the effect of pavement roughness on tire pavement interaction. The evaluated methodologies could be used to generate random profiles with specific levels of roughness to assess its effect on dynamic loads generated at the tire-pavement interface and user’s perception of road condition.
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Mahmoudzadeh, Ahmadreza, Amir Golroo, Mohammad Jahanshahi, and Sayna Firoozi Yeganeh. "Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor." Sensors 19, no. 7 (April 6, 2019): 1655. http://dx.doi.org/10.3390/s19071655.
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Measuring pavement roughness and detecting pavement surface defects are two of the most important tasks in pavement management. While existing pavement roughness measurement approaches are expensive, the primary aim of this paper is to use a cost-effective and sufficiently accurate RGB-D sensor to estimate the pavement roughness in the outdoor environment. An algorithm is proposed to process the RGB-D data and autonomously quantify the road roughness. To this end, the RGB-D sensor is calibrated and primary data for estimating the pavement roughness are collected. The collected depth frames and RGB images are registered to create the 3D road surfaces. We found that there is a significant correlation between the estimated International Roughness Index (IRI) using the RGB-D sensor and the manual measured IRI using rod and level. By considering the Power Spectral Density (PSD) analysis and the repeatability of measurement, the results show that the proposed solution can accurately estimate the different pavement roughness.
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Fradette, Nicolas, Guy Doré, Pascale Pierre, and Serge Hébert. "Evolution of Pavement Winter Roughness." Transportation Research Record: Journal of the Transportation Research Board 1913, no. 1 (January 2005): 137–47. http://dx.doi.org/10.1177/0361198105191300114.
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The functional service level of roads is quantified in terms of roughness. This parameter considers every road surface defect that causes passenger vehicle discomfort. Roughness is measured by a quality index, the international roughness index (IRI). Roughness gives an overall appreciation of road profile quality without, however, permitting a deeper analysis. The overall value of the IRI does not discriminate between the two main factors responsible for winter deterioration of roughness: the subgrade differential heave and crack heaving (winter tenting). Differential heave is the result of variability in frost susceptibility of subgrade. This phenomenon can be detected by isolating the long wavelengths produced at the road surface from the longitudinal profile. Crack heaving is a superficial phenomenon greatly influenced by the application of deicing salts. By isolating the short wavelengths from the profile, it is possible to highlight the influence of this phenomenon on deterioration. The goal of this research is to establish, with the use of a filtering technique of road profile, the contribution of these two main factors to winter deterioration of roughness on five road sections in the Quebec City, Canada, area. This study will then allow for the development of a tool to determine the dominant factor for longitudinal profile deterioration and therefore the use of the best technique to rehabilitate roads.
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Patrick, Graeme, and Haithem Soliman. "Roughness prediction models using pavement surface distresses in different Canadian climatic regions." Canadian Journal of Civil Engineering 46, no. 10 (October 2019): 934–40. http://dx.doi.org/10.1139/cjce-2018-0697.
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The correlation between the international roughness index (IRI) and distress is inherent, as roughness is a function of both the changes in elevation of the distress-free pavement surface and the changes in elevation due to existing surface distress. In this way, a relationship between existing surface distress and IRI may be developed. However, the susceptibility of pavement to various types of surface distress is affected by many factors, including climatic conditions. A model that relates pavement surface distress to IRI for Canada needs to account for climatic conditions in different locations. This paper investigates the relationship between pavement surface distresses and IRI for different climatic conditions in Canada using historical data collected at numerous pavement test section locations sourced from the Long-Term Pavement Performance program database. Developed models were calibrated then validated and found to be statistically significant.
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Baihaqi, Baihaqi, Sofyan M. Saleh, and Renni Anggraini. "TINJAUAN KONDISI PERKERASAN JALAN DENGAN KOMBINASI NILAI INTERNATIONAL ROUGHNESS INDEX (IRI) DAN SURFACE DISTRESS INDEX (SDI) PADA JALAN TAKENGON – BLANGKEJEREN." Jurnal Teknik Sipil 1, no. 3 (January 15, 2018): 543–52. http://dx.doi.org/10.24815/jts.v1i3.9993.
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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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Tehrani, Saleh Sharif, Lynne Cowe Falls, and Darel Mesher. "Road users’ perception of roughness and the corresponding IRI threshold values." Canadian Journal of Civil Engineering 42, no. 4 (April 2015): 233–40. http://dx.doi.org/10.1139/cjce-2014-0344.
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One of the primary objectives of highway agencies in Canada is providing a safe and reliable road network with a good level of service. In the Province of Alberta specific International Roughness Index (IRI) threshold values classify pavements into good, fair, and poor condition categories to manage and schedule rehabilitation and maintenance programs. This research investigated the significant factors that affect the perception of road roughness and established IRI threshold values for good, fair, and poor road condition based on public perception. A questionnaire was designed to investigate the road users’ perception and included questions covering gender, age, familiarity with the road, type and model of car, and perception of road roughness. In addition, psychometric scaling analysis was used to develop a set of IRI threshold values for classifying road condition based on public perception in the Province of Alberta. According to the results of the survey, Alberta Transportation threshold values of IRI do not agree with the road users’ opinion and an alternate set of threshold values was developed. The analysis of the survey results identified that trip purpose, driving experience, dry surface, and familiarity with the road are the most significant factors that influence the perception of road roughness.
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Shrestha, Satkar, and Rajesh Khadka. "Assessment of Relationship between Road Roughness and Pavement Surface Condition." Journal of Advanced College of Engineering and Management 6 (July 10, 2021): 177–85. http://dx.doi.org/10.3126/jacem.v6i0.38357.
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Pavement evaluation is the most significant procedure to minimize the degradation of the pavement both functionally and structurally. Proper evaluation of pavement is hence required to prolong the life year of the pavement, which thus needs to be addressed in the policy level. By this, the development of genuine indices are to be formulated and used for the evaluation. In context of evaluating the pavement indices for measuring the pavement roughness, International Roughness Index (IRI) is used, whereas for calculating the surface distress, indices as such Surface Distress Index (SDI) and Pavement Condition Index (PCI) are used. Past evaluating schemes used by Department of Roads (DOR) were limited to IRI for evaluating the pavement roughness and SDI for measuring the surface distress, which has least variability in categorizing the pavement according to the deformation. Apart from these, PCI which has wide range of categories for evaluating pavement, is not seen in practice in Nepal due to its cumbersome field work and calculations. In this paper the relationship is developed relating PCI with IRI and SDI using regression analysis by using Microsoft excel. In the other words, the pavement roughness index is compared with the surface distress indices. In 2017, 23.6Km of feeder roads in various locations of Kathmandu and Lalitpur districts were taken for this study which comprised of 236 sample data, each segmented to 100m. For this, IRI was sourced as secondary data, obtained from Highway Maintenance and Information System (HMIS) unit, Kathmandu, whereas, PCI and SDI were calculated from the field data obtained from the survey carried out in those sections manually. Then after, among 236 samples, 189 samples were taken for the relationship development which was then validated using 47 remaining samples. Furthermore, in the year, 2019 additional 3 Km of data was taken for validating the obtained relationships. It was done to improve the numerical predictions of data with such variation and thus satisfactory relationships were developed among the indices discussed in this study. The regression relationships between the two indices, IRI-PCI and IRI-SDI were thus significantly obtained. It has been found that the R² value for these relationships developed were statistically significant with 5% level of significance. The R² value for all the relationships showed that these relationships could be used for predicting the indices which would help in evaluating the pavement.
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Hassan, Rayya A., and Kerry McManus. "Assessment of Interaction Between Road Roughness and Heavy Vehicles." Transportation Research Record: Journal of the Transportation Research Board 1819, no. 1 (January 2003): 236–43. http://dx.doi.org/10.3141/1819b-30.
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Road surface roughness excites low- and high-frequency vibration modes of a heavy articulated vehicle body. These vibrations result in motions in all directions that detract from the driver’s perceived ride and comfort and increase pavement damage due to dynamic wheel loads (DWLs). A subjective assessment survey was conducted to identify surface roughness characteristics that mainly influence the perceptions of heavy-vehicle drivers of pavement rideability and their comfort. The latter was achieved by correlating drivers’ ratings to roughness contents in different roughness wavebands. The results indicated that the drivers mainly object to low-frequency body vibrations excited by roughness wavelengths in the range of 4.88 to 19.5 m. Roughness content in this band was used to establish a new profile-based index called the profile index for truck ( PIt). Drivers consider pavement rideability to be poor when PIt exceeds 2.75 m/km. PIt provides better predictions of heavy vehicle ride than the international roughness index (IRI). The methodology for developing the PIt and assessment of its reliability as a measure of heavy vehicle ride are described. The latter was achieved by testing the statistical significance of the effects of factors other than road roughness that influence the perceived ride of truck drivers. They include factors related to the vehicle, the road, and the driver as well as situational factors. In addition, PIt was found to be a better indicator than IRI of the levels of whole body vibrations transmitted to the driver through the seat and a better predictor of the magnitude of DWL to which the test pavements are subject.
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Shalaby, Ahmed, and Alan Reggin. "Optimization of data collection needs for manual and automated network-level pavement condition ratings based on transverse variability and neural networks." Canadian Journal of Civil Engineering 34, no. 2 (February 1, 2007): 139–46. http://dx.doi.org/10.1139/l06-126.
Full textAbstract:
The paper deals with two approaches to optimizing pavement condition surveys for the urban pavement network of the City of Winnipeg, Manitoba. First, a nonparametric statistical test was applied to assess the transverse variability of the data. The test compared the ratings for one lane with those of all lanes of each segment. The test concluded that the medians of the two groups are equal at a 92% confidence interval and that there are observed biases in the data. The bias can be eliminated if the surveyed lane is selected randomly. The second approach was to predict visual survey scores from automated (laser-based) measurement of rut depth and international roughness index (IRI). A resilient back-propagation algorithm was selected, and the Kappa coefficient was used to examine the strength of the agreement. The results showed that only moderate agreement was achieved and that additional data elements are required to improve the predictive ability of the model.Key words: international roughness index (IRI), rutting, cracking, spalling, pavement management system (PMS), Kappa coefficient, distress surveys.
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Karballaeezadeh, Nader, Danial Mohammadzadeh S., Dariush Moazemi, Shahab S. Band, Amir Mosavi, and Uwe Reuter. "Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods." Coatings 10, no. 11 (November 17, 2020): 1100. http://dx.doi.org/10.3390/coatings10111100.
Full textAbstract:
The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.
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Lin, Jyh Dong, Chin Yuan Zheng, and Chien Chih Liu. "The Applicability Compare of Pavement Smoothness Instruments and the Exploration of the Specifications." Advanced Materials Research 723 (August 2013): 909–15. http://dx.doi.org/10.4028/www.scientific.net/amr.723.909.
Full textAbstract:
Pavement roughness has a huge influence on driving comfort. As the progressive growing of motor vehicles, it is an important issue to effectively and quickly collect pavement information and prevent road congestion from lane closure when doing road test. This research compares the applicability of each roughness index and roughness instrument to recommend related organizations. The International Roughness Index, IRI is the index which is suitable for large-scale test, and the wavelength it can detect is close to human bodys sensation of vertical acceleration. The research also improves the self-developed inertial profiler based on previous generation, the instrument can now collect data on two side of the car instead of one side to improve the reliability in the process of road network test. The research finally compare the related regulation of inertial profiler and find out that the accelerometer, displacement sensors and numerical verification of domestic inertial profiler is incomplete and needs to be refined.