Prediction models for international roughness index and rut depth

2019 ◽  
pp. 225-230
Author(s):  
O. Eriksson ◽  
T. Lundberg
Keyword(s):  
Prediction Models ◽  
International Roughness Index ◽  
Roughness Index

Predicting International Roughness Index Based on Surface Distresses in Various Climate and Traffic Conditions Using Laser Crack Measurement System

2021 ◽  
pp. 036119812110179
Author(s):  
Mansour Fakhri ◽  
Seyed Masoud Karimi ◽  
Jalal Barzegaran
Keyword(s):  
Measurement System ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
International Roughness Index ◽  
Pavement Maintenance ◽  
Traffic Conditions ◽  
Roughness Index ◽  
Roughness Prediction ◽  
Performance Surface

Roughness is one of the most significant parameters in the evaluation of pavement performance. Surface distresses are among the main factors leading to roughness. The collection and evaluation of roughness data require the application of modern equipment such as road surface profilers. In the absence of such equipment, roughness prediction models that are based on surface distresses might provide a desirable assessment of pavement conditions. This research employs the laser crack measurement system (LCMS) to detect and measure surface distresses and roughness along 268 km of primary roads in Iran. Compared with manual survey, LCMS provides maximum detection and measurement accuracy. Based on the LCMS output, distresses with a higher correlation with the International Roughness Index (IRI) were selected as predictors in linear regression models and artificial neural networks (ANN). The models were developed for 10 m and 100 m length sections of the roads under different climate and traffic conditions. The results indicate that the performance of ANN for the 100 m sections with coefficient of determination ( R2) of 0.82 is superior to other models. The best case was that of using ANN in 100 m sections for regions with moderate climate and medium traffic levels, with a 0.94 correlation. Satisfactory results in field validation of the models demonstrated that agencies can use other methods of data collection (e.g., manual, right of way [ROW]) to assess the surface distresses and roughness condition of their roads from the developed models with minimum spending and without expensive equipment. Such estimates can be employed to make informed decisions in pavement maintenance programs at the network level.

scholarly journals COMPARISON OF ROAD TRAFFIC ACCIDENT PREDICTION MODELS FOR TWO-LANE HIGHWAY INTEGRATING TRAFFIC AND PAVEMENT CONDITION PARAMETERS

2019 ◽  
Vol 16 (2) ◽  
pp. 1-10
Author(s):  
O. M. POPOOLA ◽  
O. S. ABIOLA ◽  
S. O. ODUNFA ◽  
S. O. ISMAILA
Keyword(s):  
Road Traffic Accident ◽  
Traffic Accident ◽  
Negative Binomial ◽  
Prediction Models ◽  
Road Traffic ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Accident Prediction ◽  
Explanatory Variables ◽  
Roughness Index

In Nigeria, literature on the integration of traffic of pavement condition and traffic characteristics in predicting road traffic accident frequency on 2-lane highways are scanty, hence this article to fill the gap. A comparison of road traffic accident frequency prediction models on IIesha-Akure-Owo road based on the data observed between 2012 and 2014 is presented. Negative Binomial (NB), Ordered Logistic (OL) and Zero Inflated Negative Binomial (ZINB) models were used to model the frequency of road traffic accident occurrence using road traffic accident data from the Federal Road Safety Commission (FRSC) and pavement conditions parameters from pavement evaluation unit of the Federal Ministry of Works, Kaduna. The explanatory variables were: annual average daily traffic (aadt), shoulder factor (sf), rut depth (rd), pavement condition index (pci), and international roughness index (iri). The explanatory variables that were statistically significant for the three models are aadt, sf and iri with the estimated coefficients having the expected signs. The number of road traffic accident on the road increases with the traffic volume and the international roughness index while it decreases with shoulder factor. The systematic variation explained by the models amounts to 87.7, 78.1 and 74.4% for NB, ZINB and OL respectively. The research findings suggest the accident prediction models that should be integrated into pavement rehabilitation.   Keywords:  

Roughness prediction models using pavement surface distresses in different Canadian climatic regions

2019 ◽  
Vol 46 (10) ◽  
pp. 934-940 ◽  
Author(s):  
Graeme Patrick ◽  
Haithem Soliman
Keyword(s):  
Prediction Models ◽  
Climatic Conditions ◽  
International Roughness Index ◽  
Climatic Regions ◽  
Pavement Surface ◽  
Roughness Index ◽  
Roughness Prediction ◽  
Surface Distress ◽  
The Relationship

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.

Effects of international roughness index on vehicle emissions

2021 ◽  
Vol 15 (1) ◽  
pp. 41
Author(s):  
Ernesto Ferreira Nobre Junior ◽  
Arielle Elias Arantes ◽  
Priscilla Oliveira Azevedo
Keyword(s):  
Vehicle Emissions ◽  
International Roughness Index ◽  
Roughness Index

scholarly journals Avaliação de perfil longitudinal de pavimento de concreto de cimento Portland com perfilômetro inercial a laser

TRANSPORTES ◽  
2017 ◽  
Vol 25 (1) ◽  
pp. 82
Author(s):  
Fernando Silva Albuquerque ◽  
Rodrigo Fábio Silva de Oliveira
Keyword(s):  
International Roughness Index ◽  
Roughness Index

A avaliação do perfil longitudinal em pavimentos de Concreto de Cimento Portland é de suma importância, tanto para a o controle da qualidade de serviços executados durante períodos de obra, quanto para monitorar a evolução da irregularidade ao longo de sua vida de serviço. O Perfilômetro Inercial a Laser apresenta grande potencial de uso para este fim, pois proporciona elevada produtividade e resolução menor que 5 mm (a depender da velocidade de uso). Nesta pesquisa foram tomados como caso de estudo 09 segmentos da rodovia BR-101/SE, com níveis diferentes de qualidade de perfil. Os perfilogramas e resultados de IP (índices de perfis) e IRI (international roughness index) obtidos com o Perfilômetro Inercial a Laser e Perfilógrafo Califórnia (Equipamento de referência) foram comparados para todos os segmentos estudados. Para o pavimento em estudo, concluiu-se que os perfilogramas obtidos representaram de forma graficamente análoga os defeitos que interferem na irregularidade longitudinal. Na comparação para os índices IP e IRI resultantes dos dois equipamentos, os resultados apresentaram-se estatisticamente semelhantes. Portanto, observa-se que o equipamento Perfilômetro Inercial a Laser representa bem as características dos defeitos que influenciam na irregularidade longitudinal dos pavimentos de Concreto de Cimento Portland. Ele pode ser utilizado para medições do Perfil Longitudinal e obtenção dos índices de irregularidade para aceitação de obras e monitoramento da irregularidade longitudinal nas rodovias em operação.

scholarly journals PEMANFAATAN APLIKASI SMARTPHONE UNTUK MENGUKUR KEMANTAPAN PERMUKAAN JALAN BERDASARKAN INTERNATIONAL ROUGHNESS INDEX

2020 ◽  
Vol 19 (3) ◽  
pp. 205-214
Author(s):  
Arief Setiawan ◽  
Novita Pradani ◽  
Ferra Claudia Masoso
Keyword(s):  
Segment Length ◽  
Android Application ◽  
Smartphone Applications ◽  
International Roughness Index ◽  
Surface Conditions ◽  
Software Applications ◽  
Measurement Results ◽  
Roughness Index ◽  
Technological Developments ◽  
The Relationship

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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