Author: Eddie Chou
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 222
Book Description
The primary objectives of this study were to develop models to forecast future pavement conditions and to determine remaining service life of pavements based on the forecasted conditions. Based on available data in the ODOT pavement database, which contains the condition history of each pavement section, along with its location, year of construction, thickness, materials used, climate, and rehabilitation records, individual regression, family regression, and Markov probabilistic models were developed . For the latter two models, pavements were first grouped into "families" with similar characteristics, based on pavement type, priority, District location, and past performance. Forecasting models were then developed for each such "family." The developed models were evaluated by comparing the predicted conditions with the actual observed conditions for the five year period between 2001 and 2005. The Markov model was found to have the highest overall prediction accuracy among all the models evaluated, and it can also predict future distresses in addition to the PCR values. As a result of this study, ODOT can forecast future pavement conditions and estimate the remaining service life of pavements. Future rehabilitation needs can also be determined. Such capabilities will significantly benefit planning and management decision-makings at both project and network levels
Pavement Forecasting Models
Author: Eddie Chou
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 222
Book Description
The primary objectives of this study were to develop models to forecast future pavement conditions and to determine remaining service life of pavements based on the forecasted conditions. Based on available data in the ODOT pavement database, which contains the condition history of each pavement section, along with its location, year of construction, thickness, materials used, climate, and rehabilitation records, individual regression, family regression, and Markov probabilistic models were developed . For the latter two models, pavements were first grouped into "families" with similar characteristics, based on pavement type, priority, District location, and past performance. Forecasting models were then developed for each such "family." The developed models were evaluated by comparing the predicted conditions with the actual observed conditions for the five year period between 2001 and 2005. The Markov model was found to have the highest overall prediction accuracy among all the models evaluated, and it can also predict future distresses in addition to the PCR values. As a result of this study, ODOT can forecast future pavement conditions and estimate the remaining service life of pavements. Future rehabilitation needs can also be determined. Such capabilities will significantly benefit planning and management decision-makings at both project and network levels
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 222
Book Description
The primary objectives of this study were to develop models to forecast future pavement conditions and to determine remaining service life of pavements based on the forecasted conditions. Based on available data in the ODOT pavement database, which contains the condition history of each pavement section, along with its location, year of construction, thickness, materials used, climate, and rehabilitation records, individual regression, family regression, and Markov probabilistic models were developed . For the latter two models, pavements were first grouped into "families" with similar characteristics, based on pavement type, priority, District location, and past performance. Forecasting models were then developed for each such "family." The developed models were evaluated by comparing the predicted conditions with the actual observed conditions for the five year period between 2001 and 2005. The Markov model was found to have the highest overall prediction accuracy among all the models evaluated, and it can also predict future distresses in addition to the PCR values. As a result of this study, ODOT can forecast future pavement conditions and estimate the remaining service life of pavements. Future rehabilitation needs can also be determined. Such capabilities will significantly benefit planning and management decision-makings at both project and network levels
Development of Pavement Condition Forecasting Models
Author: Haricharan Pulugurta
Publisher:
ISBN:
Category : Forecasting
Languages : en
Pages : 364
Book Description
Publisher:
ISBN:
Category : Forecasting
Languages : en
Pages : 364
Book Description
Application of Neural Network Models for Forecasting of Pavement Crack Index and Pavement Condition Rating
Author: Jidong Yang
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 284
Book Description
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 284
Book Description
Developing Pavement Performance Prediction Models and Decision Trees for the City of Cincinnati
Author: Arudi Rajagopal
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 48
Book Description
This report presents the details of a study conducted to develop pavement performance prediction models and decision trees for various families of pavements, using the data available with the City of Cincinnati. Required data was acquired from city's pavement inventory database. The road network was divided into two classifications namely, major roads and minor roads. These roads were further grouped based on their structural makeup. Statistical regression models were developed for each group. A decision tree was developed to suggest appropriate maintenance and rehabilitation activities based on the condition of the pavement. The city engineers can use these models in conjunction with their pavement management system to predict the future condition of the highway network in Cincinnati and to implement cost effective pavement management solutions. Using the methodology developed in this study, the engineers can also further improve the accuracy of the models in the future.
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 48
Book Description
This report presents the details of a study conducted to develop pavement performance prediction models and decision trees for various families of pavements, using the data available with the City of Cincinnati. Required data was acquired from city's pavement inventory database. The road network was divided into two classifications namely, major roads and minor roads. These roads were further grouped based on their structural makeup. Statistical regression models were developed for each group. A decision tree was developed to suggest appropriate maintenance and rehabilitation activities based on the condition of the pavement. The city engineers can use these models in conjunction with their pavement management system to predict the future condition of the highway network in Cincinnati and to implement cost effective pavement management solutions. Using the methodology developed in this study, the engineers can also further improve the accuracy of the models in the future.
Flexible Pavement Condition Prediction Models for Local Governments
Development of Pavement Condition Forecasting for Web-based Asset Management for County Governments
Author: Bradley Wentz
Publisher:
ISBN:
Category : Geographic information systems
Languages : en
Pages : 18
Book Description
This application was developed to expand a low-cost asset inventory program called Geographic Roadway Inventory Tool (GRIT) to include roadway forecasting based on the American Association of State Highway and Transportation Officials (AASHTO) 93 model with inventory, pavement condition, and traffic forecasting data. Existing input data from GRIT such as pavement thickness, roadway structural information, and construction planning information will be spatially combined with current MnDOT Pathway pavement condition and traffic data to automatically forecast the future condition and age of roadways using the AASHTO 93 model. This forecasting model will allow roadway managers to use this information with comprehensive geographic information system (GIS) web maps to prioritize roadways in their construction schedules or multi-year plans.
Publisher:
ISBN:
Category : Geographic information systems
Languages : en
Pages : 18
Book Description
This application was developed to expand a low-cost asset inventory program called Geographic Roadway Inventory Tool (GRIT) to include roadway forecasting based on the American Association of State Highway and Transportation Officials (AASHTO) 93 model with inventory, pavement condition, and traffic forecasting data. Existing input data from GRIT such as pavement thickness, roadway structural information, and construction planning information will be spatially combined with current MnDOT Pathway pavement condition and traffic data to automatically forecast the future condition and age of roadways using the AASHTO 93 model. This forecasting model will allow roadway managers to use this information with comprehensive geographic information system (GIS) web maps to prioritize roadways in their construction schedules or multi-year plans.
Development of Pavement Prediction Models
Author: Ying-Haur Lee
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 344
Book Description
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 344
Book Description
Pavement Management Forecasting Model
Author: Massachusetts. Metropolitan Area Planning Council
Publisher:
ISBN:
Category : Computer simulation
Languages : en
Pages : 90
Book Description
Publisher:
ISBN:
Category : Computer simulation
Languages : en
Pages : 90
Book Description
Implementation of New Pavement Performance Prediction Models in PMIS
Multiple Random Slope and Fixed Intercept Linear Regression Models for Pavement Condition Forecasting
Author: Xiaojun Lin
Publisher:
ISBN:
Category : Forecasting
Languages : en
Pages : 94
Book Description
Pavement condition forecasting plays an important role in pavement management. Accurate predictions can help pavement managers in making optimal management plans which keeps the pavements in serviceable conditions over a specific period, as well as saves costs that are spent in the pavement maintenance and rehabilitation. In general, there are two types of prediction methods for pavement condition, which are probabilistic approach and deterministic approach. Probabilistic approach has its advantage in large scale pavement network’s overall condition predictions, it focuses on the whole pavement network condition forecasting and provides prediction results in distributions manner. Deterministic approach has its advantage in small pavement section scale predictions; it focuses on specific pavement sections condition forecasting and is able to provide the result of specific pavement section’s condition. This paper attempts to develop a deterministic forecasting approach which not only utilizes the advantage of deterministic approach that is able to provide specific pavement section condition prediction, but also attempts to adopt the advantage of probabilistic approach, which considers the effects that might have universal influence on a pavement network’s overall condition. After model simulations, the approach finally obtained on making pavement condition forecasting in this study is a random slope and fixed intercept linear regression approach. Each pavement section is assigned with a specific slope and specific intercept based on its categorical variable values and numerical variable values. The effectiveness of the obtained models is checked by comparing their predictions with the predictions through the existing prediction program ODOTPMIS.
Publisher:
ISBN:
Category : Forecasting
Languages : en
Pages : 94
Book Description
Pavement condition forecasting plays an important role in pavement management. Accurate predictions can help pavement managers in making optimal management plans which keeps the pavements in serviceable conditions over a specific period, as well as saves costs that are spent in the pavement maintenance and rehabilitation. In general, there are two types of prediction methods for pavement condition, which are probabilistic approach and deterministic approach. Probabilistic approach has its advantage in large scale pavement network’s overall condition predictions, it focuses on the whole pavement network condition forecasting and provides prediction results in distributions manner. Deterministic approach has its advantage in small pavement section scale predictions; it focuses on specific pavement sections condition forecasting and is able to provide the result of specific pavement section’s condition. This paper attempts to develop a deterministic forecasting approach which not only utilizes the advantage of deterministic approach that is able to provide specific pavement section condition prediction, but also attempts to adopt the advantage of probabilistic approach, which considers the effects that might have universal influence on a pavement network’s overall condition. After model simulations, the approach finally obtained on making pavement condition forecasting in this study is a random slope and fixed intercept linear regression approach. Each pavement section is assigned with a specific slope and specific intercept based on its categorical variable values and numerical variable values. The effectiveness of the obtained models is checked by comparing their predictions with the predictions through the existing prediction program ODOTPMIS.