Application of Artificial neural network technique for prediction of pavement roughness as a performance indicator

Ali, Abdualmtalab; Heneash, Usama; Hussein, Amgad; Khan, Shahbaz

doi:10.1016/j.jksues.2023.01.001

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…It showed that IRI could be accurately predicted from the PCI collected in the LTPP database. Ali et al [28] used multiple linear regression and artificial neural network methods to develop a model for predicting IRI from pavement age and nine types of pavement damage. The results showed that the neural network model has higher accuracy.…”

Section: Pavement Damage and Roughness Indicatorsmentioning

confidence: 99%

Development of a Relationship between Pavement Condition Index and Riding Quality Index on Rural Roads: A Case Study in China

Li,

Liu,

Teng

et al. 2024

Mathematics

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The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model’s applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications.

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Section: Pavement Damage and Roughness Indicatorsmentioning

confidence: 99%

Development of a Relationship between Pavement Condition Index and Riding Quality Index on Rural Roads: A Case Study in China

Li,

Liu,

Teng

et al. 2024

Mathematics

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“…Studies have demonstrated the ML algorithms' effectiveness in predicting pavement performances [9,30,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67]. Likewise, commonly used algorithms include ANN, SVM and support vector regression (SVR) [40], adaptive boosting (AdaBoost) [68], random forest (RF) [69], gradient boosting decision trees (GBDT) [70], categorical boost (CatBoost) [71], and ensemble models [50].…”

Section: Machine Learning For Iri Predictionmentioning

confidence: 99%

“…These features consolidate information about pavement, including records of maintenance and rehabilitation activities, traffic conditions, structural capacity, and exposure to various environmental conditions. [9] 2019 AdaBoost LTPP X X X Wang et al [53] 2021 Adaboost LTPP X X X Hossain et al [54] 2019 ANN LTPP X X Abdelaziz et al [30] 2020 ANN LTPP X X Zeiada et al [55] 2020 DT, SVM, EBT, GPR, ANN LTPP X X X Damirchilo et al [56] 2020 XGBoost LTPP X X X X Zhang et al [67] 2020 GBDT LTPP X X X Guo et al [57] 2021 LightGBM LTPP X X X Gharieb et al [58] 2021 ANN NRN X Marcelino et al [59] 2021 RF LTPP X X X X Naseri et al [60] 2022 RF LTPP X X X X Luo et al [61] 2022 GBDT, XGBoost, SVM LTPP X X X Song et al [62] 2022 ThunderGBM LTPP X X X X Sandamal et al [63] 2023 kNN, SVM, DT, RF, XGBoost Proprietary 1 X Abdualaziz et al [64] 2023 ANN LTPP Naseri et al [65] 2023 DT, SVM, RF, ANN LTPP X X X Sharma et al [66] 2023 GBDT, ANN, XRT, GLM, RF LTPP X X X In the models analyzed, a majority utilize data on traffic, with 89% of the studies incorporating this variable, while climatic factors and pavement structures are considered in 78%. This wide usage reflects a holistic approach to integrating diverse yet influential factors, underscoring the collective recognition of their importance in accurately predicting pavement conditions.…”

Section: Sharma Et Al [66]mentioning

confidence: 99%

Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions

Tamagusko,

Ferreira

2023

Infrastructures

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Timely maintenance of road pavements is crucial to ensure optimal performance. The accurate prediction of trends in pavement defects enables more efficient allocation of funds, leading to a safer, higher-quality road network. This article systematically reviews machine learning (ML) models for predicting the international roughness index (IRI), specifically focusing on flexible pavements, offering a comprehensive synthesis of the state-of-the-art. The study’s objective was to assess the effectiveness of various ML techniques in predicting IRI for flexible pavements. Among the evaluated ML models, tree ensembles and boosted trees are identified as the most effective, particularly in managing data related to traffic, pavement structure, and climatic conditions, which are essential for training these models. Our analysis reveals that traffic data are present in 89% of the studies, while pavement structure and climatic factors are featured in 78%. However, maintenance and rehabilitation history appears less frequently, included in 33% of the studies. This research underscores the need for high-quality, standardized datasets, and highlights the importance of model interpretability and computational efficiency. Addressing data consistency, model interpretability, and replicability across studies are crucial for leveraging ML’s full potential in fine-tuning IRI predictions. Future research directions include developing more interpretable, computationally efficient, and less complex models to maximize the impact of this research field in road infrastructure management.

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“…Table 1 provides a summary of IRI prediction models developed by incorporating several predictor variables such as pavement age, traffic factors, environmental factors, distress, etc. Ali et al [24] Age, Distress (X1,. .…”

Section: Iri Prediction Modelsmentioning

confidence: 99%

Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance

Sandamal,

Shashiprabha,

Muttil

et al. 2023

Sustainability

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Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.

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Application of Artificial neural network technique for prediction of pavement roughness as a performance indicator

Cited by 5 publications

References 18 publications

Development of a Relationship between Pavement Condition Index and Riding Quality Index on Rural Roads: A Case Study in China

Development of a Relationship between Pavement Condition Index and Riding Quality Index on Rural Roads: A Case Study in China

Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions

Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance

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