Yogi Oktopianto, Antonius, Abdul Rochim

An Artificial Neural Network Approach for Predicting Pavement Distress: A Case Study Toward Sustainable Road Maintenance

Introduction

An artificial neural network approach for predicting pavement distress: a case study toward sustainable road maintenance. This study develops an Artificial Neural Network (ANN) model to accurately predict pavement distress (SDI) for sustainable road maintenance. Achieves R² 0.87, optimizing road management efficiency.

Abstract

The Surface Distress Index (SDI) is a crucial parameter to consider when determining road conditions as part of an effective maintenance strategy. This study aims to develop an SDI prediction model using road surface distress data to enhance maintenance planning. The developed Artificial Neural Network (ANN) model resulted in an optimal structure with two hidden layers comprising 6 neurons and 4 neurons, respectively. The model was trained using two years of surface distress data collected from 40 road sections managed by the city’s road maintenance division. Variables used included Composition, Condition, Depression, Patches, Damage types, Crack Area, and Crack Width. The results demonstrated high accuracy in predicting SDI, with model performance achieving an R² of 0.87. This model can be applied to optimize the efficiency of road maintenance strategies.

Review

This study presents a timely and relevant application of Artificial Neural Networks (ANNs) for predicting the Surface Distress Index (SDI), a critical metric for effective road maintenance planning. The authors' initiative to develop an SDI prediction model using real-world surface distress data is commendable, addressing a key challenge in infrastructure management. The reported R² value of 0.87 indicates a promising level of accuracy for the developed ANN model, suggesting its potential utility in optimizing maintenance strategies and contributing to more sustainable road networks. The explicit mention of the ANN architecture (two hidden layers with 6 and 4 neurons, respectively) provides an initial level of detail for interested readers. While the study offers a valuable contribution, several aspects warrant further detail and consideration. The dataset, comprising two years of data from 40 road sections, may be somewhat limited for training a robust and generalizable ANN model, especially given the inherent variability in pavement distress. More information on the data collection methodology, the specifics of how variables such as "Composition," "Condition," and "Damage types" were quantified, and the data distribution would significantly enhance the transparency and reproducibility of the work. Furthermore, the abstract lacks specifics on the ANN training process (e.g., activation functions, optimization algorithms, validation strategies) and the methodology used to determine the "optimal structure." A comparative analysis against traditional prediction methods or other machine learning algorithms would also strengthen the assertion of the model's superior performance. In conclusion, this research lays a solid foundation for utilizing advanced machine learning techniques in pavement management. To maximize its impact, future work should consider expanding the dataset to enhance the model's generalizability and robustness across a wider range of road conditions and geographical areas. A more detailed elucidation of the model's training parameters and a comparative analysis would also significantly strengthen the methodology. Emphasizing the direct mechanisms by which this model contributes to the "sustainable" aspect of road maintenance, beyond just efficiency, would also be beneficial. Despite these points, the study represents a significant step towards leveraging data-driven approaches for more intelligent and proactive infrastructure management.

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