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

Seyed Amirhossein Hosseini,Ahmad Alhasan,Omar Smadi

doi:10.3390/infrastructures5110095

Seyed Amirhossein Hosseini, Ahmad Alhasan + Show 1 more

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https://doi.org/10.3390/infrastructures5110095

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Abstract

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.

Highlights

Public agencies use pavement management systems (PMSs) to make objective decisions and conduct activities for maintaining pavements in acceptable conditions at minimal cost [1]
The Pavement Condition Index (PCI) provides important information to pavement engineers by describing overall pavement condition based on different types of distress, roughness, and deflection [7]
The comparison between the developed model and the individual regression models used by the Iowa Department of Transportation (DOT) from the three different pavement types indicates that the prediction accuracy in the long/short-term memory (LSTM) model is higher than individual regression models

Summary

Introduction

Public agencies use pavement management systems (PMSs) to make objective decisions and conduct activities for maintaining pavements in acceptable conditions at minimal cost [1]. The Colorado Department of Transportation (CDOT) uses PMS to efficiently spend its $740 million annual budget for maintaining and preserving more than 9100 center-line miles (about 23,000 total lane miles) [4]. It appears that there is potential for all such expenses to be more effective if PMS improvements can be developed and implemented. Based on monitored and modelled PCI values and other important condition indices, decision-makers can evaluate the functionality of pavement networks, predict the best time for maintenance and rehabilitation activities, and estimate future funding needs [8]

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