LSTM+MA: A Time-Series Model for Predicting Pavement IRI

The accurate prediction of pavement performance is essential for transportation administration or management to appropriately allocate resources road maintenance and upkeep. The international roughness index (IRI) is one of the most commonly used pavement performance indicators to reflect the surfac...

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Main Authors: Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Infrastructures
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Online Access:https://www.mdpi.com/2412-3811/10/1/10
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author Tianjie Zhang
Alex Smith
Huachun Zhai
Yang Lu
author_facet Tianjie Zhang
Alex Smith
Huachun Zhai
Yang Lu
author_sort Tianjie Zhang
collection DOAJ
description The accurate prediction of pavement performance is essential for transportation administration or management to appropriately allocate resources road maintenance and upkeep. The international roughness index (IRI) is one of the most commonly used pavement performance indicators to reflect the surface roughness. However, the existing research on IRI prediction mainly focuses on using linear regression or traditional machine learning, which cannot take into account the historical effects of IRI caused by climate, traffic, pavement construction and intermittent maintenance. In this work, a long short-term memory (LSTM)-based model, LSTM+MA, is proposed to predict the IRI of pavements using the time-series data extracted from the long-term pavement performance (LTPP) dataset. Effective preprocessing methods and hyperparameter fine-tuning are selected to improve the accuracy of the model. The performance of the LSTM+MA is compared with other state-of-the-art models, including logistic regressor (LR), support vector regressor (SVR), random forest (RF), K-nearest-neighbor regressor (KNR), fully connected neural network (FNN), XGBoost (XGB), recurrent neural network (RNN) and LSTM. The results show that selected preprocessing methods can help the model learn quickly from the data and reach high accuracy in small epochs. Also, it shows that the proposed LSTM+MA model significantly outperforms other models, with an <i>R</i><sup>2</sup> of 0.965 and a mean square error (MSE) of 0.030 in the test datasets. Moreover, an overfitting score is proposed in this work to represent the severity degree of the overfitting problem, and it shows that the proposed model does not suffer severely from overfitting.
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spelling doaj-art-dfe6bccfa5824607a8bf891fb76038522025-01-24T13:35:23ZengMDPI AGInfrastructures2412-38112025-01-011011010.3390/infrastructures10010010LSTM+MA: A Time-Series Model for Predicting Pavement IRITianjie Zhang0Alex Smith1Huachun Zhai2Yang Lu3Computing PhD Program, College of Engineering, Boise State University, Boise, ID 83725, USADepartment of Computer Science, College of Engineering, Boise State University, Boise, ID 83725, USAIdaho Asphalt Supply Inc., Nampa, ID 83687, USADepartment of Civil Engineering, College of Engineering, Boise State University, Boise, ID 83725, USAThe accurate prediction of pavement performance is essential for transportation administration or management to appropriately allocate resources road maintenance and upkeep. The international roughness index (IRI) is one of the most commonly used pavement performance indicators to reflect the surface roughness. However, the existing research on IRI prediction mainly focuses on using linear regression or traditional machine learning, which cannot take into account the historical effects of IRI caused by climate, traffic, pavement construction and intermittent maintenance. In this work, a long short-term memory (LSTM)-based model, LSTM+MA, is proposed to predict the IRI of pavements using the time-series data extracted from the long-term pavement performance (LTPP) dataset. Effective preprocessing methods and hyperparameter fine-tuning are selected to improve the accuracy of the model. The performance of the LSTM+MA is compared with other state-of-the-art models, including logistic regressor (LR), support vector regressor (SVR), random forest (RF), K-nearest-neighbor regressor (KNR), fully connected neural network (FNN), XGBoost (XGB), recurrent neural network (RNN) and LSTM. The results show that selected preprocessing methods can help the model learn quickly from the data and reach high accuracy in small epochs. Also, it shows that the proposed LSTM+MA model significantly outperforms other models, with an <i>R</i><sup>2</sup> of 0.965 and a mean square error (MSE) of 0.030 in the test datasets. Moreover, an overfitting score is proposed in this work to represent the severity degree of the overfitting problem, and it shows that the proposed model does not suffer severely from overfitting.https://www.mdpi.com/2412-3811/10/1/10deep learninginternational roughness indexLSTMLTPP
spellingShingle Tianjie Zhang
Alex Smith
Huachun Zhai
Yang Lu
LSTM+MA: A Time-Series Model for Predicting Pavement IRI
Infrastructures
deep learning
international roughness index
LSTM
LTPP
title LSTM+MA: A Time-Series Model for Predicting Pavement IRI
title_full LSTM+MA: A Time-Series Model for Predicting Pavement IRI
title_fullStr LSTM+MA: A Time-Series Model for Predicting Pavement IRI
title_full_unstemmed LSTM+MA: A Time-Series Model for Predicting Pavement IRI
title_short LSTM+MA: A Time-Series Model for Predicting Pavement IRI
title_sort lstm ma a time series model for predicting pavement iri
topic deep learning
international roughness index
LSTM
LTPP
url https://www.mdpi.com/2412-3811/10/1/10
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AT alexsmith lstmmaatimeseriesmodelforpredictingpavementiri
AT huachunzhai lstmmaatimeseriesmodelforpredictingpavementiri
AT yanglu lstmmaatimeseriesmodelforpredictingpavementiri