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.

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.

Predicted Reference Equations for Spirometry Indices as a Proxy from Anthropometry Measurements of Nigerian Children with Sickle Cell Anaemia Aged 5–12 Years

2020 ◽  
Author(s):  
Olufemi S Akodui ◽  
Abiodun O Faleti ◽  
Folashade A Adekanmbi ◽  
Tinuade A Ogunlesi
Keyword(s):  
Sickle Cell ◽  
Standard Error ◽  
Sickle Cell Anaemia ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Nigerian Children ◽  
Arm Span ◽  
Reference Equations

Abstract Background Significant morbidity and mortality in patients with sickle cell disease are accounted for by lung complications. To facilitate good respiratory care for children with sickle cell anaemia the generation of local predicted values is highly important. Objective To determine the reference equations for spirometry indices estimation in children with sickle cell anaemia, which can be readily used as proxy when there is no easy accessibility to spirometer. Methods A cross-sectional study with linear regression models developed to estimate reference values for spirometric indices in Nigerian children with sickle cell anaemia aged 5–12 years. Results Age as independent variables for estimation of forced expiratory volume in 1 s (FEV1) was associated with lowest coefficient of determination (R2) and highest standard error. The coefficient of determination (R2) and standard error was highest and lowest, respectively, when arm span was used to determine peak expiratory flow rate (PEFR). Prediction models for PEFR and FEV1 gave the closest mean estimates that were 0.18 and 0.16 lower and higher than the actual mean PEFR and FEV1, respectively, but the differences was significant only in FEV1. On the contrary the prediction models for forced vital capacity (FVC) gave mean estimates that was 1.02 higher than the actual mean FVC, however, the finding was not significant. Conclusion Preferred proxy for spirometry indices in children with sickle cell anaemia may be arm span.

scholarly journals Measurement of International Roughness Index by UsingZ-Axis Accelerometers and GPS

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yuchuan Du ◽  
Chenglong Liu ◽  
Difei Wu ◽  
Shengchuan Jiang
Keyword(s):  
Measurement System ◽  
Low Cost ◽  
Pavement Management ◽  
Small Scale ◽  
International Roughness Index ◽  
The Road ◽  
Quarter Car Model ◽  
Pavement Management Systems ◽  
Roughness Index ◽  
Wheel Model

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.

scholarly journals Impact of Pavement Roughness on Traffic Safety under Heterogeneous Traffic Conditions

2020 ◽  
Vol 17 (1) ◽  
pp. 13-19
Author(s):  
M.O. Popoola ◽  
O.A. Apampa ◽  
O. Adekitan
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Heterogeneous Traffic ◽  
Ride Quality ◽  
International Roughness Index ◽  
Traffic Crash ◽  
Traffic Conditions ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Transportation Agencies

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.

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:  

Safety effects of maintenance treatments to improve pavement condition on two-lane rural roads – Insights for pavement management

2020 ◽  
Author(s):  
Alireza Jafari Anarkooli ◽  
Iliya Nemtsov ◽  
Bhagwant Persaud
Keyword(s):  
Empirical Bayes ◽  
State Of The Art ◽  
Pavement Management ◽  
Property Damage ◽  
International Roughness Index ◽  
Rural Roads ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Roughness Index ◽  
A Site

The research used data from two-lane rural roads in Ontario, Canada to evaluate the change in safety following maintenance treatments to improve pavement condition as measured by International Roughness Index (IRI). The state-of-the-art empirical Bayes (EB) before-after methodology was applied to estimate the effects on crashes, separately for arterial and collector roads. The results indicate statistically significant reductions (P<0.05) in all crashes and property damage only (PDO) crashes of about 5% and 7%, respectively, for arterial roads and about 11% and 13% for collector roads. For fatal plus injury (FI) crashes, there were small, statistically insignificant changes for the two road types. The results provide interesting, and sometimes counterintuitive insights for those planning maintenance treatments to improve IRI. In sum, the results suggest that consideration should be given to designing and planning pavement maintenance treatments on a site-by-site basis, and, in so doing, to optimize the IRI levels and safety effects that may be accomplished with specific treatments.

scholarly journals Roughness Modeling for Composite Pavements using Machine Learning

2021 ◽  
Vol 1203 (3) ◽  
pp. 032035
Author(s):  
Rulian Barros ◽  
Hakan Yasarer ◽  
Waheed Uddin ◽  
Salma Sultana
Keyword(s):  
Prediction Models ◽  
Concrete Pavement ◽  
Ride Quality ◽  
Performance Models ◽  
Asphalt Overlay ◽  
Road Users ◽  
Maintenance And Rehabilitation ◽  
Roughness Index ◽  
Roughness Prediction ◽  
Composite Pavements

Abstract A large number of paved highway surfaces comprises composite pavements as a result of concrete pavement rehabilitation that uses an asphalt overlay on top of the concrete surface. Annually, billions of dollars are spent on the maintenance and rehabilitation of road networks. Roughness is one of the several indicators of road conditions used to make objective decisions related to road network management. The irregularities in the pavement surface affecting the ride quality of road users can be described by a standard roughness index defined as the International Roughness Index (IRI). Roughness prediction models can identify rehabilitation needs, analyze rehabilitation effects, and estimate future pavement conditions to implement different Maintenance and Rehabilitation (M&R) activities to extend the pavement life cycle and provide a smooth surface for road users. This study intended to develop pavement performance models to predict roughness for asphalt overlay on concrete pavement sections using the Long-Term Performance Pavement (LTPP) program database. Artificial Neural Networks (ANNs) approach was used to develop roughness prediction models. A total of 52 pavement sections with 592 data points were analyzed. Five models were developed, and the best performing model, Model 5 was found with an average square error (ASE) of 0.0023, mean absolute relative error (MARE) of 12.936, and coefficient of determination (R2) of 0.88. Model 5 utilized one output variable (IRIMean) and 14 input variables (i.e., Initial IRIMean, Age, Wet-Freeze, Wet Non-Freeze, Dry-Freeze, Dry Non-Freeze, Asphalt Thickness, Concrete Thickness, CN Code, ESAL, Annual Air Temperature, Freeze Index, Freeze-Thaw, and Precipitation). The ANN model structure utilized for Model 5 was 14-9-1 (14 inputs, 9 hidden nodes, and 1 output). Environmental impacts and traffic repetitions can cause severe damage to the pavement if timely maintenance and rehabilitation are not performed. By considering the effects of the M&R history of the pavement, it is possible to obtain realistic prediction models for future planning. Therefore, the developed ANN roughness performance models in this paper can be used as a prediction tool for IRI values and guide decision-makers to develop a better M&R plan. Local and state agencies can use available historical traffic and climatological data in the developed models to estimate the change in IRI values. Utilizing these prediction models eliminates time-consuming data collection and post-processing, and consequently, a cost reduction. This low-cost tool will improve the condition assessment and effective M&R scheduling.

scholarly journals Factors Influencing Creatine Kinase Response in Youth National Team Soccer Players

2021 ◽  
pp. 194173812199938
Author(s):  
Gabor Schuth ◽  
Gyorgy Szigeti ◽  
Gergely Dobreff ◽  
Peter Revisnyei ◽  
Alija Pasic ◽  
...  
Keyword(s):  
Creatine Kinase ◽  
Prediction Models ◽  
Soccer Players ◽  
Linear Regression Models ◽  
Level Of Evidence ◽  
Internal Load ◽  
Youth Soccer ◽  
National Team ◽  
Weekly Training ◽  
The Relationship

Background: Previous studies have examined the relationship between external training load and creatine kinase (CK) response after soccer matches in adults. This study aimed to build training- and match-specific CK prediction models for elite youth national team soccer players. Hypothesis: Training and match load will have different effects on the CK response of elite youth soccer players, and there will be position-specific differences in the most influential external and internal load parameters on the CK response. Study Design: Prospective cohort study. Level of Evidence: Level 4. Methods: Forty-one U16-U17 youth national team soccer players were measured over an 18-month period. Training and match load were monitored with global positioning system devices. Individual CK values were measured from whole blood every morning in training camps. The dataset consisted of 1563 data points. Clustered prediction models were used to examine the relationship between external/internal load and consecutive CK changes. Clusters were built based on the playing position and activity type. The performance of the linear regression models was described by the R2 and the root-mean-square error (RMSE, U/L for CK values). Results: The prediction models fitted similarly during games and training sessions ( R2 = 0.38-0.88 vs 0.6-0.77), but there were large differences based on playing positions. In contrast, the accuracy of the models was better during training sessions (RMSE = 81-135 vs 79-209 U/L). Position-specific differences were also found in the external and internal load parameters, which best explained the CK changes. Conclusion: The relationship between external/internal load parameters and CK changes are position specific and might depend on the type of session (training or match). Morning CK values also contributed to the next day’s CK values. Clinical Relevance: The relationship between position-specific external/internal load and CK changes can be used to individualize postmatch recovery strategies and weekly training periodization with a view to optimize match performance.

scholarly journals Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions

2021 ◽  
Vol 11 (6) ◽  
pp. 2458
Author(s):  
Ronald Roberts ◽  
Laura Inzerillo ◽  
Gaetano Di Mino
Keyword(s):  
Deep Learning ◽  
Management Practices ◽  
Prediction Models ◽  
Road Networks ◽  
Pavement Management ◽  
Efficient Operation ◽  
Pavement Maintenance ◽  
Goods And Services ◽  
Maintenance Decisions ◽  
Computational Systems

Road networks are critical infrastructures within any region and it is imperative to maintain their conditions for safe and effective movement of goods and services. Road Management, therefore, plays a key role to ensure consistent efficient operation. However, significant resources are required to perform necessary maintenance activities to achieve and maintain high levels of service. Pavement maintenance can typically be very expensive and decisions are needed concerning planning and prioritizing interventions. Data are key towards enabling adequate maintenance planning but in many instances, there is limited available information especially in small or under-resourced urban road authorities. This study develops a roadmap to help these authorities by using flexible data analysis and deep learning computational systems to highlight important factors within road networks, which are used to construct models that can help predict future intervention timelines. A case study in Palermo, Italy was successfully developed to demonstrate how the techniques could be applied to perform appropriate feature selection and prediction models based on limited data sources. The workflow provides a pathway towards more effective pavement maintenance management practices using techniques that can be readily adapted based on different environments. This takes another step towards automating these practices within the pavement management system.

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