scholarly journals IRI performance models for recently constructed low and medium-traffic two lane roads of the province of Biscay

2016 ◽  
Author(s):  
Heriberto Pérez Acebo ◽  
Hernán Gonzalo-Orden
Keyword(s):  
Prediction Models ◽  
Ride Comfort ◽  
Heavy Traffic ◽  
Performance Model ◽  
Pavement Management ◽  
Regional Government ◽  
Performance Models ◽  
Passenger Cars ◽  
Climate Conditions ◽  
On The Road

Reliable pavement prediction models are needed for pavement management systems (PMS), as they are a key component to forecast future conditions of the pavement and to prioritize maintenance, rehabilitation and reconstruction strategies. The International Roughness Index (IRI) is the most used parameter worldwide for calibrating pavement roughness and measures reasonably the ride comfort perceived by occupants of passenger cars. The Regional Government of Biscay also collects this value on the road network under its control These surveys are carried out regularly in the XXI century. Several IRI performance models have been proposed by different authors and administrations, varying greatly in their comprehensiveness, the ability to predict performance with accurancy and input data requirements. The aim of this paper is to develop a roughness performance model for Biscay's roads, based on availablbe IRI data, taking into account heavy traffic volume and the age of pavement. Local characteristics as climate conditions and average rainfall are not considered. IRI performance models have been suggested for regional two lane highways with low and medium heavy traffic constructed in the last 20 years in the province of Biscay, with no treatments during their life. They can be applied for flexible pavements, but no logical coherent results have been concluded for semi-rigid pavements.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4108 

scholarly journals Comparison of Pavement Performance Models for Urban Road Management System

2020 ◽  
Vol 15 (3) ◽  
pp. 111-129
Author(s):  
Igoris Kravcovas ◽  
Audrius Vaitkus ◽  
Rita Kleizienė
Keyword(s):  
Bearing Capacity ◽  
Management System ◽  
Condition Index ◽  
Performance Model ◽  
Pavement Management ◽  
Pavement Performance ◽  
Performance Models ◽  
Climate Conditions ◽  
Pavement Condition ◽  
Pavement Performance Model

The key factors for effective pavement management system (PMS) are timely preservation and rehabilitation activities, which provide benefit in terms of drivers’ safety, comfort, budget and impact on the environment. In order to reasonably plan the preservation and rehabilitation activities, the pavement performance models are used. The pavement performance models are usually based on damage and distress observations of rural roads, and can be applied to forecast the performance of urban roads. However, the adjustment of the parameters related to traffic volume, speed and load, climate conditions, and maintenance has to be made before adding them to PMS for urban roads. The main objective of this study is to identify the performance indicators and to suggest pavement condition establishment methodology of urban roads in Vilnius. To achieve the objective, the distresses (rut depth and cracks), bearing capacity, and international roughness index (IRI) were measured for fifteen urban roads in service within a two-year period. The distresses, rut depth and IRI were collected with the Road Surface Tester (RST) and bearing capacity of pavement structures were measured with a Falling Weight Deflectometer (FWD). The measured distresses were compared to the threshold values identified in the research. According to the measured data, the combined pavement condition indices using two methodologies were determined, as well as a global condition index for each road. The analysed roads were prioritized for maintenance and rehabilitation in respect to these criteria. Based on the research findings, the recommendations for further pavement condition monitoring and pavement performance model implementation to PMS were highlighted.

Local Calibration of Pavement Mechanistic-Empirical Faulting Reliability using Pavement Management Data

2021 ◽  
pp. 036119812110013
Author(s):  
Lucio Salles de Salles ◽  
Lev Khazanovich
Keyword(s):  
Model Calibration ◽  
Prediction Models ◽  
Plain Concrete ◽  
Performance Model ◽  
Pavement Management ◽  
Pavement Design ◽  
Reliability Model ◽  
Transverse Joint ◽  
Design Characteristics ◽  
And Performance

The Pavement ME transverse joint faulting model incorporates mechanistic theories that predict development of joint faulting in jointed plain concrete pavements (JPCP). The model is calibrated using the Long-Term Pavement Performance database. However, the Mechanistic-Empirical Pavement Design Guide (MEPDG) encourages transportation agencies, such as state departments of transportation, to perform local calibrations of the faulting model included in Pavement ME. Model calibration is a complicated and effort-intensive process that requires high-quality pavement design and performance data. Pavement management data—which is collected regularly and in large amounts—may present higher variability than is desired for faulting performance model calibration. The MEPDG performance prediction models predict pavement distresses with 50% reliability. JPCP are usually designed for high levels of faulting reliability to reduce likelihood of excessive faulting. For design, improving the faulting reliability model is as important as improving the faulting prediction model. This paper proposes a calibration of the Pavement ME reliability model using pavement management system (PMS) data. It illustrates the proposed approach using PMS data from Pennsylvania Department of Transportation. Results show an increase in accuracy for faulting predictions using the new reliability model with various design characteristics. Moreover, the new reliability model allows design of JPCP considering higher levels of traffic because of the less conservative predictions.

scholarly journals Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems

2020 ◽  
Vol 2674 (10) ◽  
pp. 448-460
Author(s):  
Orhan Kaya ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Danny Waid ◽  
Brian P. Moore
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Decision ◽  
Performance Models ◽  
Remaining Service Life ◽  
Management Decision Making ◽  
Decision Making Processes

In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

scholarly journals Analysis of Vehicle Vibrations – New Approach to Rating Pavement Condition of Urban Roads

2012 ◽  
Vol 23 (6) ◽  
pp. 485-494 ◽  
Author(s):  
Stjepan Lakušić ◽  
Davor Brčić ◽  
Višnja Tkalčević Lakušić
Keyword(s):  
Surface Roughness ◽  
Ride Comfort ◽  
Road Traffic ◽  
Heavy Traffic ◽  
Pavement Management ◽  
International Roughness Index ◽  
New Approach ◽  
Pavement Condition ◽  
Roughness Index ◽  
Road Surface Roughness

Urban road infrastructure is daily burdened by heavy traffic volume. Pavement structure roughness observations are significantly more difficult in urban agglomerations than on roads in unpopulated areas. Roughness, expressed by IRI (International Roughness Index), directly affects the quality and safety of road traffic. Within the framework of the pavement management in relation to safety and the achievement of the best possible ride comfort, it is very important to foresee when a road should be reconstructed. The method for quality evaluations of safety and ride comfort on urban roads presented in this paper is based on vehicle vibrations measurements. In the article, measuring of vehicle vibrations was performed on the main urban roads in Zagreb (Croatia). Measurements covered roads with different pavement surface roughness. This method can be simply and very easily used in pavement management aimed at achieving road safety and better ride comfort. The results of measurements according to this method could be used by traffic and civil engineering experts as an indication for the roads that require reconstruction or maintenance. KEY WORDS: urban roads, traffic flow, safety, vehicle vibrations, road surface roughness (IRI)

scholarly journals IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work

Coatings ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 97 ◽  
Author(s):  
Heriberto Pérez-Acebo ◽  
Alaitz Linares-Unamunzaga ◽  
Eduardo Rojí ◽  
Hernán Gonzalo-Orden
Keyword(s):  
Surface Layer ◽  
Deterministic Model ◽  
Heavy Traffic ◽  
Multiple Linear Regression Model ◽  
Performance Model ◽  
Coefficient Of Determination ◽  
Performance Models ◽  
Deterministic Models ◽  
Qualitative Variable ◽  
Traffic Volumes

Pavement performance models play a vital role in any pavement management system. The Regional Government of Biscay (RGB) (Spain) manages a 1200 km road network and conducts pavement data collections, including the International Roughness Index (IRI) values. The aim of the paper is to develop an IRI performance model for two-lane roads with flexible pavement until the first maintenance and/or rehabilitation activity is performed. Due to the huge amount of available information, a deterministic model was selected. A literature review of deterministic models showed that, apart from age and traffic volumes, the pavement structure is a key factor. Therefore, it was decided to analyze the only road stretches whose entire pavement section was known (surface layer + base + subbase). Various variables related to age, traffic volumes and employed materials were introduced as possible factors. The multiple linear regression model with the highest coefficient of determination and all the variables significant included the real pavement age, the cumulated heavy traffic and the total thickness of bituminous layers. As the material employed in the surface layer could affect roughness progression, a qualitative variable was introduced to consider various surface materials. The model improved its accuracy, indicating that the surface layer material is also an influencing factor on IRI evolution.

scholarly journals Comparing of Data Collection for Network Level Pavement Management of Urban Roads and Highways

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Zhoucong Xu ◽  
Lei Yue
Keyword(s):  
Data Collection ◽  
Prediction Models ◽  
Condition Index ◽  
Current Data ◽  
Performance Model ◽  
Pavement Management ◽  
Pavement Performance ◽  
Pavement Condition ◽  
Monitoring Frequency ◽  
Maintenance Decisions

Pavement condition data are collected by agencies to support pavement management system (PMS) for decision-making purpose as well as to construct performance model. The cost of pavement data collection increases with the increase of survey frequencies. However, a lower monitoring frequency could lead to unreliable maintenance decisions. It is necessary to understand the influence of monitoring frequencies on maintenance decision by considering the reliability of performance prediction models. Because of different maintenance conditions of urban roads and highways, their performance show different trends. In this paper, the influence of pavement monitoring frequency on the pavement performance models was investigated. The results indicate that low collection frequencies may result in delay in maintenance action by overestimating pavement performance. The collection frequency for Pavement Condition Index (PCI) can be reduced without compromising the accuracy of performance model, more work should be done to ensure the PCI data quality, thus to guarantee the rationality of maintenance decisions. Effect of frequency reduction on pavement performance (IRI) models of urban roads seems greater than on pavement performance (IRI) models of highways, which may lead to heavier monitoring work for urban roads management. This paper provided an example which demonstrated how a comparative analysis can be performed to determine whether the current data collection plan can provide sufficient data for time series analysis.

A Study on Job Satisfaction among Tamil Nadu State Transport Corporation (TNSTC) Employees at Tiruvarur Branch

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 230-242
Author(s):  
M. Ganesan, K ◽  
K. Veerakumar ◽  
N. R Vembu ◽  
Dr. M. K Durgamani ◽  
Dr. Renuka
Keyword(s):  
Job Satisfaction ◽  
Job Stress ◽  
Tamil Nadu ◽  
Heavy Traffic ◽  
Road Transportation ◽  
The Road ◽  
Tamil Nadu State ◽  
On The Road ◽  
Day By Day ◽  
Labour Turnover

Job satisfaction is an important factor for employees working in formal and informal sector. The job is small or big, permanent or temporary, risky or non-risky, job satisfaction is important. It is the mental feeling which drives the employees to excel. Job satisfaction is a combination of psychological, physiological and environmental circumstances. A satisfied employee is a contented and happy human being. The labour turnover depends upon job satisfaction. Even highly paid employees quit the job when they are not satisfied with the job. Road transportation in Tamilnadu is growing day by day. Job stress in the road transportation is very high due to increase in number of vehicle playing on the road and heavy traffic. The drivers and conductors working in public transport corporation are suffering from high job stress. If drivers and conductors are not satisfied with their job which leads to mental stresses and affects the productivity and also creates accidents. In this present study the researchers made an attempt to study the level of job satisfaction among the drivers and conductors who are working in the Tamilnadu State Transport Corporation (TNSTC). The study reveals the expectations of drivers and conductors working in TNSTC with regards to the attributes like salary, promotion and fringe benefits etc., are satisfactory and not detrimental. 

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.

Validating Human Performance Models: Building a House without Bricks

2020 ◽  
Vol 64 (1) ◽  
pp. 1511-1514
Author(s):  
Richard Steinberg ◽  
Raytheon Company ◽  
Alice Diggs ◽  
Raytheon Company ◽  
Jade Driggs
Keyword(s):  
Air Force ◽  
Human Performance ◽  
Control Program ◽  
Real Data ◽  
Performance Model ◽  
Force Model ◽  
Performance Models ◽  
Actual System ◽  
Support Design ◽  
Trade Offs

Verification and validation (V&V) for human performance models (HPMs) can be likened to building a house with no bricks, since it is difficult to obtain metrics to validate a model when the system is still in development. HPMs are effective for performing trade-offs between the human system designs factors including number of operators needed, the role of automated tasks versus operator tasks, and member task responsibilities required to operate a system. On a recent government contract, our team used a human performance model to provide additional analysis beyond traditional trade studies. Our team verified the contractually mandated staff size for using the system. This task demanded that the model have sufficient fidelity to provide information for high confidence staffing decisions. It required a method for verifying and validating the model and its results to ensure that it accurately reflected the real world. The situation caused a dilemma because there was no actual system to gather real data to use to validate the model. It is a challenge to validate human performance models, since they support design decisions prior to system. For example, crew models are typically inform the design, staffing needs, and the requirements for each operator’s user interface prior to development. This paper discusses a successful case study for how our team met the V&V challenges with the US Air Force model accreditation authority and successfully accredited our human performance model with enough fidelity for requirements testing on an Air Force Command and Control program.

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