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Building a deterioration model for pavement management using artificial neural networks Application to rut depth prediction
a PAVEMENT Y ,.a .-_
ABRIDGED
MAINTENANCE DECISION SUPPORT ,. “- _~,. -_ , ‘..,j ,.. ,_~.~^_I‘. ,_ .,. _..____ .-
VERSION
Building
a deterioration model for pavement using artificial neural networks Application
Periodic monitoring of road pavements for condition assessment is an important part of road maintenance. In Sweden, the National Road Administration (SNRA) has made great efforts to periodically monitor and collect pavement condition data on its road network. This data is part of its Pavement Management System. In the late 1960s and early 1970s the term Pavement Management System (PMS)began to be used to refer to a wide spectrum of activities including the planning and scheduling of investments, design, construction, maintenance and periodic evaluation of performance. Management at all levels involves comparing alternatives, coordinating activities, making decisions and seeing that they are implemented in an efficient and economical manner. A key to effective pavement management lies in the evaluation of actual and future pavement condition.
to rut depth
management
prediction
Specific analytical models are built to estimate the deterioration of pavement condition over time. Fed with information collected on-site, such models provide pavement engineers with an insight into current pavement distress. Used as prediction tools, they enable highway engineers to plan future maintenance strategies, and adjust future plans to constraints. Deterioration models are regarded as decision-support tools that help decision-makers to answer the following questions: tvhich sections need to be maintained and when is the right time to schedule particular maintenance actions? A need for actions exists whenever an estimation/prediction of pavement condition falls below a specified threshold which threshold is fifed by road agencies to provide consistency between different road sections. Accuracy in estimating pavement condition affects future planning; an over-estimation (or underestimation) of the distress level can
lead to an early (or late) start of the selected action. It has been recently stressed that one of the greatest challenges facing pavement engineers since the formulation of the serviceability-performance concept by Carey and Irick, has been the development of accurate deterioration prediction models. While major advances have been made, it is still an area where there is a need for much further improvement. The major difficulty in developing deterioration models and attempting to keep modelling errors at a reasonable level lies in capturing the large array of factors and their interactions. There are four major classes of factors to take into account when building deterioration models: - enuironmental factors such as temperature, freeze-thaw cycles, moisture, radiation.. . - traffic factors such as axle loads, tyre types and pressures, axle spacing, speed, traffic volumes..
RECIIERCHE TRANSPORT5 SiCUKlTi
N” 68
IUILLET-SEPTEMBRE 2000
- pavement structure factors such as layer thickness, layer types and properties, subgrade type and properties, variations in thickness and properties..
non-linearities into it. Both models were estimated based on exactly the same input variables. The input vector consists of the following information:
construction ictors.
- pavement information - type of pavement for the upper layer; - pauement condition information - previous value of the rut depth, previous change in rut depth; - climatic conditions - Sweden is split up into six different regions according to their climatic conditions; - maintenance information -type of last pavement action, number of years since last maintenance action.
and
maintenance
The paper investigates the application of artificial neural networks for building deterioration models, based on experimental data. It focuses on predicting rut depth. Rutting consists of a depression in the transverse profile of the pavement. It is probably one of the most frequently encountered types of deterioration in Sweden, because approximately 90% of motorised vehicles use studded tyres during wintertime. Rutting is also considered to be a major cause of severe water damage to road pavements. Predicting rut depth is a real need expressed by our sponsor, the Swedish National Road Administration. Our work is slightly different from the previously applications of neural networks for building deterioration models reported in the literature; instead of predicting an index that provides a global insight into pavement condition, it focuses on predicting one specific type of distress. Two types of model were considered for predicting the one-year-ahead rut depth; the first is a linear regression model of ARX type, and the second one is non linear-m model, consisting of one-single-hidden-layer MLP. The main advantage of using linear regression models over neural networks is that linear models are of limited complexity and they yield visible models. This means that it is straightforward to read from the parameters how much certain input data contributes to the output. This is particularly difficult in the case of neural networks, because the contribution of one input is distributed over all the synaptic weights and strengthened/ lessened by the transfer function. The performance of these two models had to be compared in order to evaluate the adequacy of the linear model, and how much is gained by introducing
RECHERCHE TRANSPORTS SiCURl ii N” 68
The second-order Gauss-Newton algorithm was applied to estimate the parameters of both the linear model and the neural networks. The earlystop procedure was applied to prevent over-fitting. To evaluate the performance of the prediction models, three indicators were considered: the average absolute error of the rut depth prediction, its standard deviation, and the correlation factor between predicted rut depth and actual measurements. As far as the linear model is concerred, scatter plots of actual rut depth versus predicted rut depth on the recall of both the estimation and validation sets, show that points do not cluster on the diagonal, indicating that the model does not predict accurately. The correlation coefficient was 0.79 for the estimation set, and 0.78 for the validation set. The average absolute prediction error was 0.78 mm f 0.7 for the estimation set and 0.78 mm f. 0.81 for the validation set, which represents a 10% error. Introducing non-linearities into the model greatly increases accuracy, leading to a correlation coefficient of 0.88 for the recall of the estimation set, and 0.84 for the validation set. The average absolute prediction error of was 0.64 mm f 0.62 on the estimation set and 0.70 mm 1 0.63. The results illustrate the aforementioned challenge faced by pavement engineers when building deterioration models. This challenge originates from
JUILLET-SEPTEMKRE2000
the mediocre quality and incompleteness of the data sets provided by road administrations. Even though most factors influencing rut depth are measured in some way and included in the data sets, the choice of the variables for describing certain factors is questionable. At this point, no matter how sophisticated the method for modelling the correlation between factors affecting the studied deterioration and the actual deterioration, the incompleteness and poor quality of the data sets severely limit the accuracy of the predictions. Integration of our model into the Swedish PMS must depend on the accuracy of its predictions. The Swedish PMShas a three level structure. The network level is used for network planning, without regard to individual road segments. It includes evaluation of optimum condition standards, assessment of financial needs for different standard levels and allocation of funds to sub-networks. Budget restrictions can either be taken into consideration or not. The programme level provides a prioritised list of projects, i.e. stretches of road sections that need some maintenance actions. A rough socio-economic evaluation of the proposed projects is used as a criterion for their priority. Finally, the project level provides details for designing individual projects. Discussion with pavement engineers from SNRAreveals that our predictive model meets the requirements for integration into the network and programme levels. The constellation of often competing and contradictory factors, which the decision-makers face both at the network and programme levels, diminished the importance of a refined description of road condition. At the project level, decisions are based on accurate and precise estimations of the actual and future physical condition of the road network. This means that the level of detail and accuracy expected at the project level are higher than at the network and programme levels. At this stage of the research, incompleteness in the data set resulting in prediction errors constitutes a major obstacle to integrating the model into the project level.
ABRIDGED
MAINTENANCE DECISION SUPPORT ,. “- _~,. -_ , ‘..,j ,.. ,_~.~^_I‘. ,_ .,. _..____ .-
VERSION
Building
a deterioration model for pavement using artificial neural networks Application
Periodic monitoring of road pavements for condition assessment is an important part of road maintenance. In Sweden, the National Road Administration (SNRA) has made great efforts to periodically monitor and collect pavement condition data on its road network. This data is part of its Pavement Management System. In the late 1960s and early 1970s the term Pavement Management System (PMS)began to be used to refer to a wide spectrum of activities including the planning and scheduling of investments, design, construction, maintenance and periodic evaluation of performance. Management at all levels involves comparing alternatives, coordinating activities, making decisions and seeing that they are implemented in an efficient and economical manner. A key to effective pavement management lies in the evaluation of actual and future pavement condition.
to rut depth
management
prediction
Specific analytical models are built to estimate the deterioration of pavement condition over time. Fed with information collected on-site, such models provide pavement engineers with an insight into current pavement distress. Used as prediction tools, they enable highway engineers to plan future maintenance strategies, and adjust future plans to constraints. Deterioration models are regarded as decision-support tools that help decision-makers to answer the following questions: tvhich sections need to be maintained and when is the right time to schedule particular maintenance actions? A need for actions exists whenever an estimation/prediction of pavement condition falls below a specified threshold which threshold is fifed by road agencies to provide consistency between different road sections. Accuracy in estimating pavement condition affects future planning; an over-estimation (or underestimation) of the distress level can
lead to an early (or late) start of the selected action. It has been recently stressed that one of the greatest challenges facing pavement engineers since the formulation of the serviceability-performance concept by Carey and Irick, has been the development of accurate deterioration prediction models. While major advances have been made, it is still an area where there is a need for much further improvement. The major difficulty in developing deterioration models and attempting to keep modelling errors at a reasonable level lies in capturing the large array of factors and their interactions. There are four major classes of factors to take into account when building deterioration models: - enuironmental factors such as temperature, freeze-thaw cycles, moisture, radiation.. . - traffic factors such as axle loads, tyre types and pressures, axle spacing, speed, traffic volumes..
RECIIERCHE TRANSPORT5 SiCUKlTi
N” 68
IUILLET-SEPTEMBRE 2000
- pavement structure factors such as layer thickness, layer types and properties, subgrade type and properties, variations in thickness and properties..
non-linearities into it. Both models were estimated based on exactly the same input variables. The input vector consists of the following information:
construction ictors.
- pavement information - type of pavement for the upper layer; - pauement condition information - previous value of the rut depth, previous change in rut depth; - climatic conditions - Sweden is split up into six different regions according to their climatic conditions; - maintenance information -type of last pavement action, number of years since last maintenance action.
and
maintenance
The paper investigates the application of artificial neural networks for building deterioration models, based on experimental data. It focuses on predicting rut depth. Rutting consists of a depression in the transverse profile of the pavement. It is probably one of the most frequently encountered types of deterioration in Sweden, because approximately 90% of motorised vehicles use studded tyres during wintertime. Rutting is also considered to be a major cause of severe water damage to road pavements. Predicting rut depth is a real need expressed by our sponsor, the Swedish National Road Administration. Our work is slightly different from the previously applications of neural networks for building deterioration models reported in the literature; instead of predicting an index that provides a global insight into pavement condition, it focuses on predicting one specific type of distress. Two types of model were considered for predicting the one-year-ahead rut depth; the first is a linear regression model of ARX type, and the second one is non linear-m model, consisting of one-single-hidden-layer MLP. The main advantage of using linear regression models over neural networks is that linear models are of limited complexity and they yield visible models. This means that it is straightforward to read from the parameters how much certain input data contributes to the output. This is particularly difficult in the case of neural networks, because the contribution of one input is distributed over all the synaptic weights and strengthened/ lessened by the transfer function. The performance of these two models had to be compared in order to evaluate the adequacy of the linear model, and how much is gained by introducing
RECHERCHE TRANSPORTS SiCURl ii N” 68
The second-order Gauss-Newton algorithm was applied to estimate the parameters of both the linear model and the neural networks. The earlystop procedure was applied to prevent over-fitting. To evaluate the performance of the prediction models, three indicators were considered: the average absolute error of the rut depth prediction, its standard deviation, and the correlation factor between predicted rut depth and actual measurements. As far as the linear model is concerred, scatter plots of actual rut depth versus predicted rut depth on the recall of both the estimation and validation sets, show that points do not cluster on the diagonal, indicating that the model does not predict accurately. The correlation coefficient was 0.79 for the estimation set, and 0.78 for the validation set. The average absolute prediction error was 0.78 mm f 0.7 for the estimation set and 0.78 mm f. 0.81 for the validation set, which represents a 10% error. Introducing non-linearities into the model greatly increases accuracy, leading to a correlation coefficient of 0.88 for the recall of the estimation set, and 0.84 for the validation set. The average absolute prediction error of was 0.64 mm f 0.62 on the estimation set and 0.70 mm 1 0.63. The results illustrate the aforementioned challenge faced by pavement engineers when building deterioration models. This challenge originates from
JUILLET-SEPTEMKRE2000
the mediocre quality and incompleteness of the data sets provided by road administrations. Even though most factors influencing rut depth are measured in some way and included in the data sets, the choice of the variables for describing certain factors is questionable. At this point, no matter how sophisticated the method for modelling the correlation between factors affecting the studied deterioration and the actual deterioration, the incompleteness and poor quality of the data sets severely limit the accuracy of the predictions. Integration of our model into the Swedish PMS must depend on the accuracy of its predictions. The Swedish PMShas a three level structure. The network level is used for network planning, without regard to individual road segments. It includes evaluation of optimum condition standards, assessment of financial needs for different standard levels and allocation of funds to sub-networks. Budget restrictions can either be taken into consideration or not. The programme level provides a prioritised list of projects, i.e. stretches of road sections that need some maintenance actions. A rough socio-economic evaluation of the proposed projects is used as a criterion for their priority. Finally, the project level provides details for designing individual projects. Discussion with pavement engineers from SNRAreveals that our predictive model meets the requirements for integration into the network and programme levels. The constellation of often competing and contradictory factors, which the decision-makers face both at the network and programme levels, diminished the importance of a refined description of road condition. At the project level, decisions are based on accurate and precise estimations of the actual and future physical condition of the road network. This means that the level of detail and accuracy expected at the project level are higher than at the network and programme levels. At this stage of the research, incompleteness in the data set resulting in prediction errors constitutes a major obstacle to integrating the model into the project level.