A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning
Abstract
:1. Introduction
2. Literature Review
2.1. Research on Predictive Model Development Using Pavement Performance Database
2.2. Infrastructure Management Related Database Based on Machine/Deep Learning Research
3. Methodology
4. Prediction of Concrete Pavement Performance Based on Particle Filtering
4.1. Data Preparation
4.2. Data Pre-Processing
- The start and end points were matched using the GPS values for each age of concrete pavement and each 10 m section.
- Time-series sections collected over three consecutive points simultaneously and the endpoint section in the PMS DB are extracted.
- Data were sorted and filtered based on four items: earthworks, pavement type, first pavement history, and design lane (38,208 sections = 38.208 km).
- Data clusters that do not have a maintenance history after initial packaging were extracted (576 sections = 5.76 km).
- Redundantly written historical data were removed to improve data reliability (prediction accuracy) (469 sections = 4.69 km)
- Data sets were classified into two clusters based on consecutively collected time-series data points within the same time and endpoint interval (7 points = 208 sections; 5 points = 261 sections; a total of 469 intervals).
4.3. Particle Filter Model for CPCI Prediction
5. Results
5.1. Application of Particle Filtering
5.2. Verification of the CPCI Prediction Model
6. Conclusions
- This study applied particle filtering on time-series data of the concrete pavement condition of a specific section, which were collected for research, to predict pavement conditions. As a result of reviewing 22 different cases, the particle filtering technique showed a prediction accuracy between 86.09 and 99.33%, indicating its applicability in predicting the road pavement condition index. As national investigations are conducted for both express highways and national highways every year, unlike in the past, the utilization of PMS can be further increased for project level management, where it was difficult to use in the past, through more accurate predictions.
- The results of this study can be more easily utilized by the road pavement maintenance management team for making decisions in the future by presenting the deterioration model in the form of a category according to the performance index obtained at the time of the actual completion of the new section. In particular, modeling through future research is expected to allow practitioners to easily use it without further concerns by presenting the y-intercept value, CPCI at the time of completion, in a graph format for a more subdivided performance index, in addition to 4.2, 4.4, and 4.6.
- The function of the particle filtering technique itself increases the prediction accuracy for the analysis target section as the number of particles increases; however, the accuracy for other sections (routes) can be lowered by specifying the corresponding section. Therefore, as a result of analyzing the accuracy of cases using 1000, 5000, 10,000, 15,000, and 20,000 particles, the use of 15,000 particles was found to be the most efficient.
- We found that the number of time series points in one section had a higher impact on the improvement of prediction accuracy than the number of sections analyzed. The prediction accuracy was 92.3% when using the section with 5 points (261), whereas it was 98.1% when using the section with 7 points (208) (when 15,000 particles were used).
- As a result of analyzing the prediction accuracy by increasing the number of points to 3, 4, 5, and 6 in the section with 7 points, the prediction accuracy was 88.5%, 92.9%, 93.2%, and 99.3%, respectively. Accordingly, we determined that a section with at least six consecutive data points should be selected to secure a prediction accuracy of 95% or more in a study related to the prediction of the road pavement condition index, thereby obtaining the reliability of data and results.
- The relative error by each predicted age for the same section decreased as the number of time series points increased. Meanwhile, prediction accuracy decreased when farther future was predicted in the section. Accordingly, the particle filtering technique is deemed to be effective to predict up to two years of future road pavement conditions.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Latitude | Longitude | Route | Start | End | Bound | Lane | Type | Age 25 | Age 27 | … | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
IRI | SD | CPCI | IRI | SD | CPCI | … | ||||||||
37.2084 | 127.4418 | A | 0 | 0.01 | South bound | 1 | JPCP | 0.81 | 0 | 4.31 | 0.73 | 0.51 | 3.39 | … |
37.2088 | 127.4415 | A | 0.01 | 0.02 | South bound | 1 | JPCP | 0.71 | 0 | 4.37 | 1.29 | 0.24 | 3.33 | … |
37.2092 | 127.4413 | A | 0.02 | 0.03 | South bound | 1 | JPCP | 0.68 | 0 | 4.39 | 0.69 | 0.08 | 3.97 | … |
Division | 206 Sections (in 30 Years) | 261 Sections (in 27 Years) | ||||
---|---|---|---|---|---|---|
Detected CPCI | Predicted CPCI | Accuracy (%) | Detected CPCI | Predicted CPCI | Accuracy (%) | |
4.2 (a),(b) | 3.07 | 2.938 | 95.71 | 3.02 | 3.195 | 94.21 |
4.4 (c),(d) | 3.154 | 97.27 | 3.187 | 94.48 | ||
4.6 (e),(f) | 3.091 | 99.33 | 3.121 | 96.66 |
No. of Particles | 206 Sections (in 30 Years) | 261 Sections (in 27 Years) | ||||
---|---|---|---|---|---|---|
95% Confidential Interval | ||||||
Included | Not Included | Validation Accuracy (%) | Included | Not Included | Validation Accuracy (%) | |
1000 | 200 | 8 | 96.15 | 236 | 25 | 90.42 |
5000 | 199 | 9 | 95.67 | 241 | 20 | 92.34 |
10,000 | 202 | 6 | 97.12 | 241 | 20 | 92.34 |
15,000 | 204 | 4 | 98.08 | 241 | 20 | 92.34 |
20,000 | 201 | 7 | 96.63 | 241 | 20 | 92.34 |
Age (Year) | Actual CPCI | 3 Data | 4 Data | 5 Data | 6 Data | ||||
---|---|---|---|---|---|---|---|---|---|
PF * CPCI | Prediction Accuracy (%) | PF * CPCI | Prediction Accuracy (%) | PF * CPCI | Prediction Accuracy (%) | PF * CPCI | Prediction Accuracy (%) | ||
19 | 4.00 | 3.98 | 99.45 | 4.01 | 99.73 | 4.00 | 99.98 | 3.98 | 99.61 |
21 | 3.83 | 3.83 | 99.98 | 3.83 | 99.94 | 3.82 | 99.62 | 3.81 | 99.43 |
23 | 3.72 | 3.72 | 99.88 | 3.72 | 99.92 | 3.72 | 99.95 | 3.72 | 99.95 |
25 | 3.53 | 3.63 | 97.12 | 3.51 | 99.50 | 3.52 | 99.80 | 3.55 | 99.42 |
27 | 3.41 | 3.55 | 95.97 | 3.42 | 99.63 | 3.40 | 99.65 | 3.39 | 99.43 |
29 | 3.22 | 3.46 | 92.43 | 3.33 | 96.49 | 3.32 | 96.91 | 3.22 | 99.86 |
30 | 3.07 | 3.42 | 88.54 | 3.29 | 92.90 | 3.28 | 93.16 | 3.09 | 99.33 |
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Lee, J.-H.; Jung, D.-H.; Lee, M.-S.; Jeon, S.-I. A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning. Appl. Sci. 2022, 12, 8731. https://doi.org/10.3390/app12178731
Lee J-H, Jung D-H, Lee M-S, Jeon S-I. A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning. Applied Sciences. 2022; 12(17):8731. https://doi.org/10.3390/app12178731
Chicago/Turabian StyleLee, Jin-Hyuk, Dong-Hyuk Jung, Moon-Sub Lee, and Sung-Il Jeon. 2022. "A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning" Applied Sciences 12, no. 17: 8731. https://doi.org/10.3390/app12178731
APA StyleLee, J.-H., Jung, D.-H., Lee, M.-S., & Jeon, S.-I. (2022). A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning. Applied Sciences, 12(17), 8731. https://doi.org/10.3390/app12178731