An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database
Abstract
:1. Introduction
- (1)
- A statistical model (for each asset type) that predicts the impact level of each treatment type as a function of initial asset condition, treatment intensity, and other variables;
- (2)
- Average (mean) impact level of each treatment type under consideration;
- (3)
- The other statistical parameters of the impact level of each treatment type for each asset type (pavements and bridges): minimum impact, maximum impact, range, and standard deviation;
- (4)
- Sensitivity charts;
- (5)
- Cost-effectiveness values.
2. Literature Review
2.1. Methodological Research to Predict Bridge Deck Pavement Condition
2.2. Predictive Research on Bridge Deck Pavement Condition Using Various Variables
3. Methodology
4. Forecasting the Status of Bridge Pavement through Particle Filtering
4.1. Preparation of Data
4.2. Pre-processing of Data
- Initiate the matching of start and end points by leveraging GPS values for every 10 m segment, according to the age of the concrete bridge deck pavement;
- Extract time series sections where more than three consecutive points have been collected for the same start and end points in PMS DB;
- Organize the data considering four factors—bridge component, type of pavement, initial paving history, and if it pertains to a design lane—then execute the filtering process (229 sections equivalent to 2.29 km);
- Extract data clusters that lack a maintenance history post-initial paving (comprising 224 sections, which corresponds to 2.24 km);
- Remove duplicate history data to improve the reliability (prediction accuracy) of data (188 sections = 1.88 km);
- Categorize the dataset into two clusters according to time series data points consistently gathered within the identical start and end point segments (5 points accounting for 146 sections and 4 points for 42 sections, amounting to a total of 188 sections).
4.3. CPCI Prognosis Using Particle Filtering
5. Results of Predicting Future Pavement Condition with Particle Filtering
5.1. Application of Particle Filtering
5.2. Verification of the CPCI Prediction Model
6. Discussion on the Utilization of Predicted CPCI
7. Conclusions
- In this study, a predictive model for the future condition of bridge deck pavement using particle filtering was proposed, considering initial CPCI, an appropriate number of particles, and a minimum number of time series data points.
- By presenting the deterioration model in a categorical form based on performance indicators acquired during the future construction of actual new sections, bridge maintenance practitioners made decisions more easily. Particularly, in future research, it is anticipated that the model will present a more specific performance measure, such as initial CPCI values of 4.3, 4.4, 4.5, 4.6, and 4.7, to facilitate practical use.
- The operation principle of particle filtering suggests that, as the number of particles increases, the predicted accuracy for the analyzed section improves, but the accuracy of identifying that specific section may decrease. Therefore, the analysis of prediction accuracy for all cases, using 1000, 5000, 10,000, 15,000, and 20,000 particles, revealed that using 5000 particles was the most efficient approach.
- It was observed that the number of time series data points within the analyzed section had a significant impact on prediction accuracy, rather than the number of sections. Specifically, when utilizing sections with four data points (146 sections), the prediction accuracy was 99.69%, while it was 97.37% for sections with three data points (42 sections) (using 5000 particles).
- To predict the initial CPCI at the age of 27 years, for sections with four data points, the accuracy values were 34.19%, 97.20%, and 99.69% when sequentially adding two, three, and four data points. Therefore, it is crucial to select sections with a minimum of three consecutive data points to ensure the reliability of the data and results when predicting the pavement condition index.
- By validating the correlation and significance between the CPCI prediction results based on particle filtering and the cumulative ESAL traffic volume by vehicle type over 27 years, it was concluded that the cumulative ESAL traffic volume of vehicle types 5, 6, 7, and 8 directly affected pavement deterioration. Therefore, for sections where these vehicle types are prevalent, various traffic management strategies, including direct maintenance (e.g., partial depth repair, overlay, etc.) and alternative route planning considering the specific vehicle types, can be considered effective alternatives to ensure appropriate road performance.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
coefficient of the regression line for two parameters CPCI and t (regression model slope) | |
coefficient of the regression line for two parameters CPCI and t (initial CPCI when t is zero) | |
CPCI | concrete pavement condition index |
measurement function of CPCI | |
measured CPCI | |
random variable of the predicted CPCI | |
random variable of the detected CPCI | |
the rate of relative error between the predicted data and the actual data | |
zero-mean Gaussian white noise sequences about | |
zero-mean Gaussian white noise sequences about | |
variance for zero-mean Gaussian measurement function | |
measurement function | |
SD | surface distress |
IRI | international roughness index |
K | time index |
N | number of particles |
q | relative likelihood |
r | random number |
t | time |
References
- Oyeyi, A.G.; Achebe, J.; Ni, F.M.W.; Tighe, S. Life cycle assessment of lightweight cellular concrete subbase pavements in Canada. Int. J. Pavement Eng. 2023, 24, 2168662. [Google Scholar] [CrossRef]
- Chen, L.; Zhao, X.; Qian, Z.; Li, J. A systematic review of steel bridge deck pavement in China. J. Road Eng. 2023, 3, 1–15. [Google Scholar] [CrossRef]
- Lee, K.R.; Han, S.Y.; Kim, S.K.; Yun, K.K. Evaluation of Physical Properties and Economic Feasibility with Cellular Re-mixed Concrete through Test Construction. Int. J. Highw. Eng. 2021, 23, 27–35. [Google Scholar] [CrossRef]
- Jung, H.K.; Topendra, O.; Nam, J.H.; Yun, K.K.; Kim, S.W.; Park, C.W. Life-Cycle Cost Analysis on Application of Asphalt and Concrete Pavement Overlay. Appl. Sci. 2022, 12, 5098. [Google Scholar] [CrossRef]
- Han, D.S.; Lee, J.H.; Park, K.T. Deterioration Models for Bridge Pavement Materials for a Life Cycle Cost Analysis. Sustainability 2022, 14, 11435. [Google Scholar] [CrossRef]
- Liu, C.; Qian, Z.; Liao, Y.; Ren, H. A Comprehensive Life-Cycle Cost Analysis Approach Developed for Steel Bridge Deck Pavement Schemes. Coatings 2021, 11, 565. [Google Scholar] [CrossRef]
- Liu, Y.; Shen, Z.; Liu, J.; Chen, S.; Wang, J.; Wang, X. Advances in the application and research of steel bridge deck pavement. Structures 2022, 45, 1156–1174. [Google Scholar] [CrossRef]
- Su, N.; Lou, L.; Amirkhanian, A.; Amirkhanian, S.N.; Xiao, F. Assessment of effective patching material for concrete bridge deck—A review. Constr. Build. Mater. 2021, 293, 123520. [Google Scholar] [CrossRef]
- Kim, H.B.; Lee, K.H. An Innovative Rehabilitation Approach for the Bridge Deck Pavement; Geotechnical Special Publication No. 196; American Society of Civil Engineering: Reston, VA, USA, 2009; pp. 19–27. [Google Scholar]
- Gao, F.; Gao, X.; Li, Y.; Gao, Z.; Wang, C. Materials and Performance of Asphalt-Based Waterproof Bonding Layers for Cement Concrete Bridge Decks: A systematic Review. Sustainability 2022, 14, 15500. [Google Scholar] [CrossRef]
- Gucunski, N.; Romero, F.; Kruschwitz, S.; Feldmann, R.; Abu-Hawash, A.; Dunn, M. Multiple Complementary Nodestructive Evaluation Technologies for Condition Assessment of Concrete Bridge Decks. Transp. Res. Rec. J. Transp. Res. Board 2010, 2201, 34–44. [Google Scholar] [CrossRef]
- Hossain, A.; Chang, C.M. Life-Cycle Cost Analysis of Ultra High-Performance Conrete(UHPC) in Retrofitting Applications. In Proceedings of the Third International Interactive Symposium on Ultra-High Performance Concrete, Wilmington, DC, USA, 4–7 June 2023; p. 82. [Google Scholar]
- Saeed, T.U.; Qiao, Y.; Chen, S.; Al-Qadhi, S.; Zhang, Z.; Labi, S.; Sinha, K.C. Effects of Bridge Surface & Pavement Maintenance Activites on Asset Rating; Final Report 2017, FHWA/IN/JTRP-2018/19; Indiana Department of Transportation: Indianapolis, IN, USA, 2017.
- Srikanth, I.; Arockiasamy, M. Deterioration models for prediction of remaining useful life of timber and concrete bridges: A review. J. Traffic Transp. Eng. 2020, 7, 152–173. [Google Scholar] [CrossRef]
- Jing, D.; Sang, L. A Sixteen-Year Review of Condition Survey and Analysis of Steel Deck Pavement on Jiangyin Yangtze River Bridge. Mater. Sci. Forum 2016, 873, 91–95. [Google Scholar]
- Moomen, M.; Siddiqui, C. Probabilistic deterioration modeling of bridge component condition with random effects. J. Struct. Integr. Maint. 2022, 7, 151–160. [Google Scholar] [CrossRef]
- SIXENSE Engineering (Vinci Group). Effective Network Level Optimization for Bridges Maintenance. Int. J. Eng. Comput. Sci. 2020, 9, 25161–25174. [Google Scholar]
- Lee, K.B.; Kwon, S.M.; Lee, J.H.; Sohn, D.S. Influence on Predicted Performance of Jointed Concrete Pavement with Variations in Axle Load Spectra. Int. J. Highw. Eng. 2014, 16, 11–19. [Google Scholar] [CrossRef]
- Ministry of Land, Infrastructure and Transport. The Guidebook of Structural Design of Road Pavement; Ministry of Land, Infrastructure and Transport: Sejong, Republic of Korea, 2011.
- Amorim, S.I.R.; Pais, J.C.; Vale, A.C.; Minhoto, M.J.C. A model for equivalent axle load factors. Int. J. Pavement Eng. 2015, 16, 881–893. [Google Scholar] [CrossRef]
- Rys, D.; Judycki, J.; Jaskula, P. Determination of vehicles load equivalency factors for polish catalogue of typical flexible and semi-rigid pavement structures. Transp. Res. Procedia 2016, 14, 2382–2391. [Google Scholar] [CrossRef]
- Kim, S.S.; Yang, J.J.; Durham, S.A.; Kim, I.K.; Yaghoubi, N.T. Determination of Equivalent Single Axle Load (ESAL) Factor for Georgia Pavement Design, Final Report; GHWA-GA-21-1804; Georgia Department of Transportation: Atlanta, GA, USA, 2021.
- Simon, D. Optimal State Estimation: Kalman, H-Infinity, and Nonlinear Approaches; John Willey & Sons: Hoboken, NJ, USA, 2006; pp. 461–483. [Google Scholar]
- Ristic, B.; Arulampalam, S.; Gordon, N. Beyond the Kalman Filter: Particle Filters for Tracking Applications; Artech House Publishers: Boston, MA, USA; London, UK, 2004. [Google Scholar]
- Lee, J.H.; Jung, D.H.; Lee, M.S.; Jung, S.I. A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning. Appl. Sci. 2022, 12, 8731. [Google Scholar] [CrossRef]
Vehicle Type | Axle Configuration | ESALf | 8 Types | 12 Types | |
---|---|---|---|---|---|
Before | After | Before | After | ||
Passenger cars | 2Axle-4Tire | 0.0001 | 0.0001 | 1 | 1 |
Buses | 2Axle-4Tire | 0.0007 | 0.0004 | 2 | - |
2Axle-6Tire | 1.043 | 1.043 | 3 | 2 | |
Trucks | 2Axle-4Tire | 0.015 | 0.015 | 4 | 3 |
2Axle-6Tire | 0.796 | 0.795 | 5 | 4 | |
3Axle-10Tire | 2.52 | 2.516 | 6 | 5~7 | |
Special purpose trucks | 2.948 | 2.482 | 7~8 | 8~12 |
Division | Section A (in 27 Years) | Section B (in 27 Years) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Detected CPCI | Particle Filtering | Multinomial Regression | Detected CPCI | Particle Filtering | Multinomial Regression | |||||
Predicted CPCI | Prediction Accuracy (%) | Predicted CPCI | Prediction Accuracy (%) | Predicted CPCI | Prediction Accuracy (%) | Predicted CPCI | Prediction Accuracy (%) | |||
4.3 | 1.609 | 1.663 | 96.69 | 1.698 | 94.47 | 2.266 | 2.036 | 89.85 | 2.282 | 92.61 |
4.4 | 1.614 | 99.69 | 1.696 | 94.59 | 2.066 | 91.16 | 2.281 | 92.66 | ||
4.5 | 1.659 | 96.95 | 1.693 | 94.78 | 2.102 | 92.77 | 2.279 | 92.75 | ||
4.6 | 1.726 | 92.74 | 1.691 | 94.90 | 2.207 | 97.37 | 2.278 | 92.80 | ||
4.7 | 1.710 | 93.77 | 1.689 | 95.03 | 2.125 | 93.75 | 2.277 | 92.85 |
Num. of Particles | Section A (In 27 Years) | Section B (In 27 Years) | ||||
---|---|---|---|---|---|---|
95% Confidential Interval | Prediction Accuracy (%) | 95% Confidential Interval | Prediction Accuracy (%) | |||
Included (Not Included) | Validation Accuracy (%) | Included (Not Included) | Validation Accuracy (%) | |||
1000 | 135 (11) | 92.47 | 87.54 | 41 (1) | 97.62 | 88.42 |
5000 | 137 (9) | 93.84 | 88.07 | 42 (0) | 100.00 | 89.54 |
10,000 | 136 (10) | 93.15 | 87.98 | 41 (1) | 97.62 | 89.47 |
15,000 | 133 (13) | 91.10 | 88.03 | 41 (1) | 97.62 | 89.35 |
20,000 | 134 (12) | 91.78 | 88.04 | 41 (1) | 97.62 | 89.32 |
Age (Year) | Actual CPCI | 2 Data | 3 Data | 4 Data | |||
---|---|---|---|---|---|---|---|
PF * CPCI | Prediction Accuracy (%) | PF * CPCI | Prediction Accuracy (%) | PF * CPCI | Prediction Accuracy (%) | ||
19 | 4.236 | 4.234 | 99.97 | 4.240 | 99.90 | 4.232 | 99.90 |
21 | 3.629 | 3.620 | 99.77 | 3.652 | 99.35 | 3.618 | 99.71 |
23 | 2.491 | 3.303 | 67.40 | 2.500 | 99.63 | 2.462 | 98.83 |
25 | 2.027 | 2.986 | 52.71 | 2.077 | 97.52 | 2.001 | 98.69 |
27 | 1.609 | 2.669 | 34.19 | 1.655 | 97.20 | 1.614 | 99.69 |
Age (Year) | Actual CPCI | 2 Data | 3 Data | ||
---|---|---|---|---|---|
PF * CPCI | Prediction Accuracy (%) | PF * CPCI | Prediction Accuracy (%) | ||
21 | 3.961 | 3.957 | 99.91 | 3.967 | 99.86 |
23 | 3.106 | 3.086 | 99.38 | 3.093 | 99.60 |
25 | 2.483 | 2.781 | 87.98 | 2.478 | 99.82 |
27 | 2.266 | 2.476 | 90.73 | 2.207 | 97.37 |
Type | Total | Section A | Section B |
---|---|---|---|
1 | 12 | 12 | 12 |
2 | 8 | 9 | 8 |
3 | 5 | 6 | 5 |
4 | 6 | 5 | 6 |
5 | 1 | 1 | 1 |
6 | 3 | 3 | 4 |
7 | 4 | 4 | 3 |
8 | 2 | 2 | 2 |
9 | 7 | 7 | 7 |
10 | 9 | 8 | 9 |
11 | 11 | 11 | 11 |
12 | 10 | 10 | 10 |
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Lee, J.; Jung, D.; Baek, C.; An, D. An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database. Sustainability 2023, 15, 16680. https://doi.org/10.3390/su152416680
Lee J, Jung D, Baek C, An D. An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database. Sustainability. 2023; 15(24):16680. https://doi.org/10.3390/su152416680
Chicago/Turabian StyleLee, Jinhyuk, Donghyuk Jung, Cheolmin Baek, and Deoksoon An. 2023. "An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database" Sustainability 15, no. 24: 16680. https://doi.org/10.3390/su152416680