Use of Deep Learning to Study Modelling Deterioration of Pavements a Case Study in Iowa

2020 ◽  
Author(s):  
Seyed Amirhossein Hosseini ◽  
Ahmad Alhasan ◽  
Omar Smadi
Keyword(s):  
Prediction Accuracy ◽  
Regression Models ◽  
Short Term Memory ◽  
Weighted Average ◽  
Condition Index ◽  
Pavement Management ◽  
Asphalt Pavements ◽  
Pavement Condition ◽  
Pavement Condition Index ◽  
The Individual

This paper describes the process and outcome of deterioration modeling for three different pavement types in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). Typically, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the Long/Short Term Memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that while the individual regression models achieved higher prediction accuracy with respect to asphalt pavements, the LSTM model achieved a higher prediction accuracy over time for concrete and composite pavement types.

scholarly journals Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

2020 ◽  
Vol 5 (11) ◽  
pp. 95
Author(s):  
Seyed Amirhossein Hosseini ◽  
Ahmad Alhasan ◽  
Omar Smadi
Keyword(s):  
Short Term Memory ◽  
Weighted Average ◽  
Condition Index ◽  
The State ◽  
Pavement Management ◽  
Department Of Transportation ◽  
Pavement Condition ◽  
Process And Outcome ◽  
Pavement Condition Index

This paper describes the process and outcome of deterioration modeling for three different pavement types (asphalt, concrete, and composite) in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). In the state of Iowa, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the long/short-term memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that the LSTM model achieved a higher prediction accuracy over time for all different pavement types.

scholarly journals Proposal and Implementation of a Heliport Pavement Management System: Technical and Economic Comparison of Maintenance Strategies

2021 ◽  
Vol 13 (16) ◽  
pp. 9201 ◽  
Author(s):  
Paola Di Mascio ◽  
Alessio Antonini ◽  
Piero Narciso ◽  
Antonio Greto ◽  
Marco Cipriani ◽  
...  
Keyword(s):  
Management System ◽  
Condition Index ◽  
Pavement Management ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Airport Pavement ◽  
Maintenance And Rehabilitation ◽  
Pavement Condition Index ◽  
Rotary Wing ◽  
Economic Comparison

Maintenance and rehabilitation (M&R) scheduling for airport pavement is supported by the scientific literature, while a specific tool for heliport pavements lacks. A heliport pavement management system (HPMS) allows the infrastructure manager to obtain benefits in technical and economic terms, as well as safety and efficiency, during the analyzed period. Structure and rationale of the APSM could be replicated and simplified to implement a HPMS because movements of rotary-wing aircrafts have less complexity than fixed-wing ones and have lower mechanical effects on the pavement. In this study, an innovative pavement condition index-based HPMS has been proposed and implemented to rigid and flexible surfaces of the airport of Vergiate (province of Varese, Italy), and two twenty-year M&R plans have been developed, where the results from reactive and proactive approaches have been compared to identify the best strategy in terms of costs and pavement level of service. The result obtained shows that although the loads and traffic of rotary-wing aircrafts are limited, the adoption of PMS is also necessary in the heliport environment.

Estimation of Pavement Serviceability Index Through Android-Based Smartphone Application for Local Roads

2017 ◽  
Vol 2639 (1) ◽  
pp. 129-135 ◽  
Author(s):  
Waleed Aleadelat ◽  
Khaled Ksaibati
Keyword(s):  
Smartphone Application ◽  
Condition Index ◽  
Pavement Management ◽  
Pavement Condition ◽  
Explanatory Variables ◽  
Pavement Condition Index ◽  
Local Roads ◽  
Series Acceleration ◽  
The Difference ◽  
The Cost

The Wyoming Technology Transfer Center is in the process of developing a pavement management system (PMS) for county paved roads in Wyoming. This PMS uses the present serviceability index (PSI) as a main pavement performance parameter. This PMS depends on pavement condition index, international roughness index, and pavement rutting as explanatory variables to estimate PSI. This study researched new explanatory variables measured by using smartphones’ sensors to estimate PSI. It was found that the variance of the signals (time series acceleration data) acquired by smartphones’ accelerometers could work as a very good explanatory variable to estimate PSI. Two models were developed with high significance ( R2 higher than .9) to predict PSI using the variance of smartphone signals. The initial validation results suggested that using these models could predict, with high certainty, the actual PSI values. The difference between the predicted and the actual PSI values was not statistically different. The study was performed on 20 roadway segments extracted from the Wyoming county roads’ PMS database. In addition, the selected segments had various lengths and geometric features reflecting various roadway segments under any PMS. The proposed methodology is intended to lower the cost of measuring county roads’ pavement conditions by estimating PSI directly without the reliance on the direct measurement of pavement condition parameters.

scholarly journals Assessment of Al-Amarah street within the Al-kut city using pavement condition index (PCI) and GIS technique

2018 ◽  
Vol 162 ◽  
pp. 01033
Author(s):  
Mohammed Al-Neami ◽  
Rasha Al-Rubaee ◽  
Zainab Kareem
Keyword(s):  
Condition Index ◽  
Pavement Management ◽  
Pavement Condition ◽  
The Road ◽  
Gis Technique ◽  
Gis Data ◽  
Pavement Condition Index ◽  
Specific Sign ◽  
Maintenance Process

The capabilities of geographical system and their spatial analysis is considered the most appropriate tools to enhance pavement management operations, with features such as graphical display of pavement condition. In Iraq, most of transportation agencies do not have a tool that is used as a database for road deteriorations, so there is a need for road surveying and storing the collected information in GIS to know the condition of every road with details. Furthermore, these data can be used for maintenance process and estimation of prior cost. This research has been carried out to estimate of flexible pavement condition through visual surveys using the Pavement Condition Index (PCI) method; so it can provide an easy way to calculate the PCI based on GIS data with Micro PAVER software 5.2. Al-Amarah Street, which is internal road in Al-Kut city in the eastern part of Iraq, is used as a case study. The average pavement condition index of the selected case study is found to be “64” using Micro PAVER 5.2 software which mean “Fair” pavement condition. Arc Map 9.3 has been applied in this study to make an integrated maintenance system for each road in the region demonstrating the annual road deteriorations and the resulting change in the PCI values which occurs every year. The study provides an easy and simplified way of presentation the details of deteriorations on the satellite or the geographical map of the road in which each type of distress has been symbolized with specific sign and each PCI value has been represented with specific color.

scholarly journals Evaluation of Asphalt Concrete Airport Pavement Conditions Based on the Airfield Pavement Condition Index (APCI) in Scope of Flight Safety

Aerospace ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 78
Author(s):  
Mariusz Wesołowski ◽  
Paweł Iwanowski
Keyword(s):  
Quality Management System ◽  
Condition Index ◽  
Assessment Method ◽  
Technical Condition ◽  
Pavement Management ◽  
Infrastructure Development ◽  
Pavement Condition ◽  
Airport Pavement ◽  
Pavement Condition Index ◽  
High Level

Airoport infrastructure development requires care to maintain it in proper technical condition. Due to this, airport pavements should be constantly monitored, and, above all, correctly managed. High-level airport pavement management requires access to reliable information about their current technical condition as well as proper forecasting of this condition in the future. Obtaining good quality information about the technical condition of airport pavement should be based on a proven methodology, taking into account the introduced quality management system. The authors propose a method of technical pavement condition assessment based on the Airfield Pavement Condition Index (APCI), taking into account not only the results of the surface deterioration inventory, but also repair overviews, load bearing capacity, evenness and roughness of the surface, as well as the surface tensile bond strength. The method was developed during long-term work financed by the Ministry of Science and Higher Education. At the beginning of the article, the authors focus on reviewing the currently available methods of assessing the technical condition of the pavement. Then they briefly present the most popular surface assessment method based on the PCI indicator. Afterwards, a proprietary asphalt pavement assessment method based on the APCI indicator is proposed and an example of how to use the method is presented. Finally, they discuss the results and summarize the work done, and present further directions of work.

scholarly journals Automated Pavement Distress Detection Using Image Processing Techniques

2021 ◽  
Vol 11 (5) ◽  
pp. 7702-7708
Author(s):  
I. H. Abbas ◽  
M. Q. Ismael
Keyword(s):  
Image Processing ◽  
Surface Damage ◽  
Condition Index ◽  
Asphalt Pavements ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Automated Method ◽  
Pavement Condition Index ◽  
Processing Techniques

Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels with a medium validity of 76%. There are two kinds of methods, manual and automated, for distress evaluation that are used to assess pavement condition. A committee of three expert engineers in the maintenance department of the Mayoralty of Baghdad did the manual assessment of a highway in Baghdad city by using a Pavement Condition Index (PCI). The automated method was assessed by processing the videos of the road. By comparing the automated with the manual method, the accuracy percentage for this case study was 88.44%. The suggested method proved to be an encouraging solution for identifying cracks and potholes in asphalt pavements and sorting their severity. This technique can replace manual road damage assessment.

scholarly journals Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index

2020 ◽  
Vol 26 (12) ◽  
pp. 81-94
Author(s):  
Muataz Safaa Abed
Keyword(s):  
Regression Models ◽  
Essential Element ◽  
Condition Index ◽  
Flexible Pavement ◽  
Transportation Infrastructure ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Roughness Index ◽  
Pavement Condition Index ◽  
Quadratic Models

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.

Statistical Analysis of Automated versus Manual Pavement Condition Surveys

2005 ◽  
Vol 1940 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Jason M. McQueen ◽  
David H. Timm
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Analysis ◽  
Condition Index ◽  
Pavement Management ◽  
Severity Level ◽  
Pavement Condition ◽  
Condition Rating ◽  
Pavement Condition Index ◽  
Level 3

The Alabama Department of Transportation (ALDOT) has used a vendor to perform automated pavement condition surveys for the Alabama pavement network since 1997. In 2002, ALDOT established a quality assurance (QA) program to check the accuracy of the automated pavement condition data. The QA program revealed significant discrepancies between manual and automatically collected data. ALDOT uses a composite pavement condition index called pavement condition rating (PCR) in its pavement management system. The equation for PCR was developed in 1985 for use with manual pavement condition surveys; however, ALDOT continues to use it with data from automated condition surveys. Since the PCR equation was developed for manual surveys, the discrepancies between the manual and automated data led ALDOT to question the continuity between its manual and automated pavement condition survey programs. A regression analysis was completed to look for any systematic error or general trends in the error between automated and manual data. Also, Monte Carlo simulation was used to determine which distress parameters most influence the PCR and whether they require more accuracy. The regression analysis showed the following general trends: automated data overreport outside wheelpath rut depth, under-report alligator severity Level 1 cracking, and overreport alligator severity Level 3 cracking. Through Monte Carlo simulation, it was determined that all severity levels of transverse cracking, block cracking, and alligator cracking data require greater accuracy.

Investigating Pavement Maintenance and Rehabilitation Requirement Based on Material Types and Traffic Levels through an Application of Probabilistic Approach for Ontario Highways

2019 ◽  
Vol 2673 (11) ◽  
pp. 561-572
Author(s):  
Gulfam Jannat ◽  
Susan L. Tighe
Keyword(s):  
Probabilistic Approach ◽  
Condition Index ◽  
Probability Of Failure ◽  
Pavement Management ◽  
Deterministic Approach ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Maintenance And Rehabilitation ◽  
Pavement Condition Index

In a pavement management system (PMS), time to maintenance is generally estimated based on the predicted condition of the pavement. Usually a deterministic approach is applied in the PMS to estimate the time to maintenance by following the deterioration equation of the performance index. However, it is necessary to be aware of the probability of failure to investigate whether the estimated time to maintenance by the deterministic approach is reasonably probable. For this reason, a probabilistic approach is applied in this study to estimate the probability of failure over the estimated time to maintenance. In this approach, the probability of failure is estimated from the distribution of the mean time to maintenance by considering both the overall condition of the pavement and individual instances of distress. These mean times to failure or maintenance are calculated from the overall condition of pavement in relation to the pavement condition index (PCI) when the trigger value becomes 65 or less. A pavement may be expected to fail, however, because of any specific distress before it reaches the PCI trigger value for maintenance. For this reason, the probability of failure of each specific distress is also investigated by using a Monte Carlo simulation. It is found that the survival probability up to the fifth year is approximately 80% to 90% for each category of traffic and material type based on the overall condition, and the probability of failure for individual distress is very low over the performance cycle.

scholarly journals Condition Survey for Evaluation of Pavement Condition Index of a Highway

2019 ◽  
Vol 5 (6) ◽  
pp. 1367-1383
Author(s):  
Muhammad Saleem Zafar ◽  
Syed Naveed Raza Shah ◽  
Muhammad Jaffar Memon ◽  
Touqeer Ali Rind ◽  
Muhammad Afzal Soomro
Keyword(s):  
Management System ◽  
Structural Reliability ◽  
Condition Index ◽  
Pavement Management ◽  
Present Condition ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Pavement Condition Index ◽  
The Common ◽  
Infrastructure Maintenance

Pavements are major means of highway infrastructure. Maintenance and rehabilitation of these pavements for the required serviceability is a routine problem faced by highway engineers and organizations. Improvement in road management system results in reduction of time and cost, the pavement condition survey plays a big role in the pavement management. The initial phase in setting up a pavement management system (PMS) is road network identification. A vital element of a PMS is the capacity to assess the present condition of a pavement network and anticipation of future condition. The pavement condition index (PCI) is a numerical index generally utilized for the assessment of the operational condition & structural reliability of pavements. Estimation of the PCI is dependent on the results of a visual inspection in which the type, severity, and quantity of distresses are distinguished. In this research, a pavement distress condition rating strategy was utilized to accomplish the goals of this study. The main targets of this research were to categorize the common types of distress that exist on “Lakhi Larkana National Highway (N-105)”, and to estimate the pavement condition index. Using these data, Average PCI for the highway section was calculated. PCI to assess the pavement performance, 10 out of 19 defects were recognized in the pavement, as stated by the PCI method. Results indicated that the common pavement distress types were depressions, polished aggregate, rutting, potholes, block cracking, and alligator cracking.

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