Predicting pavement performance using distress deterioration curves

Figures & data

Figure 1. A typical SCANNER system.

Figure 2. A map showing the study area and the road sections used in this study (The shown points represent the scanned road sections; bridges, roundabouts, and intersections were not included in the survey).

Table 1. Sample of the raw SCANNER data used in this study.

Section ID	Type	Start	End	Rutting in left wheel path mm	Rutting in right wheel path mm	Texture depth mm	Longitudinal profile variance over 3m mm^2
3055A6005201	A	0	10	2.5	3	0.85	0
3055A6005201	A	10	20	2.9	5.8	0.19	0
3055A6005201	A	20	30	3.3	7.6	0.54	0.7

Table 2. Data pre-processing methods (Kargah-Ostadi et al., 2019).

Data issue	Pre-processing technique
Spatial Variability	Dynamic Segmentation for Model Development; Probabilistic Model Development; Smaller Segments for Model Development Controlling Standard Deviation in Segmentation
Inaccurate Section Boundaries	Combining Sections and Averaging
Temporal Variability	Smoothing / Moving Average; Benchmarking
Noise	Smoothing / Moving Average
Outliers (Unreasonable Data)	Outlier Removal
Missing Data	Averaging / Interpolation; Bootstrapping; Imputation
Scale Differences	Normalization
Inter-Correlation of Input Data	Principal Components Analysis; Stepwise Regression; Variable Multiplication
Measurement Errors/ Subjectivity	Outlier Removal; Averaging; Benchmarking

Figure 3. Flow chart explaining the methodology of the study.

Figure 4. DDRs for rutting in the left wheel path.

Figure 5. DDRs for rutting in the right wheel path.

Figure 6. DDRs of the crack intensity over the whole carriageway.

Figure 7. DDRs of the texture depth in the case of increasing texture over time.

Table 3. Rutting data in the left and right wheel paths collected between 2014 and 2018.

	Distress	Rutting in the right side			Rutting in the left side
Severity	Year	2014	2016	2018	2014	2016	2018
0–2 mm	Mean	1.03	2.20	3.02	1.18	1.87	2.68
	Std	0.58	1.35	1.76	0.59	0.85	1.19
	CoV %	56.2	61.3	58.1	49.8	45.3	44.3
	10%	0.29	0.47	0.77	1.94	2.96	4.21
	90%	1.78	3.92	5.27	0.43	0.79	1.16
2–4 mm	Mean	2.83	3.96	4.83	2.69	3.57	4.49
	Std	0.58	1.28	1.59	0.54	1.11	1.39
	CoV %	20.4	32.4	32.9	20.0	31.1	31.0
	10%	2.09	2.32	2.80	3.38	4.99	6.27
	90%	3.57	5.61	6.86	2.00	2.15	2.70
4–6 mm	Mean	4.83	6.01	6.91	4.80	5.58	6.46
	Std	0.61	1.23	1.51	0.63	0.92	1.24
	CoV %	12.6	20.5	21.9	13.1	16.5	19.2
	10%	4.05	4.43	4.97	5.61	6.77	8.05
	90%	5.61	7.59	8.85	3.99	4.40	4.87
6–8 mm	Mean	6.86	8.02	8.93	6.90	7.82	8.88
	Std	0.61	1.21	1.48	0.69	0.97	1.79
	CoV %	8.9	15.1	16.5	10.0	12.4	20.2
	10%	6.08	6.47	7.04	7.79	9.06	11.18
	90%	7.64	9.57	10.82	6.01	6.58	6.59
8–10 mm	Mean	8.86	10.04	11.06	8.50	10.15	10.90
	Std	0.63	1.02	1.25	0.57	1.92	2.05
	CoV %	7.1	10.2	11.3	6.7	18.9	18.8
	10%	9.66	11.34	12.66	9.22	12.61	13.53
	90%	8.05	8.73	9.46	7.78	7.69	8.27
>10 mm	Mean	10.83	11.82	13.14	11.34	11.97	13.62
	Std	0.88	0.92	1.28	1.90	1.75	1.70
	CoV %	8.1	7.8	9.8	16.8	14.6	12.5
	10%	9.71	10.63	11.50	13.78	14.21	15.80
	90%	11.96	13.00	14.78	8.90	9.72	11.44

Figure 8. Deterioration curve for rutting in the left wheel path.

Figure 9. Deterioration curve for rutting in the right wheel path.

Figure 10. Deterioration curve for cracking over the whole carriageway.

Figure 11. Deterioration curve for texture depth.

Table 4. Crack intensity and texture depth deterioration models at 10%, average, 90% reliability levels.

$C{I}_{i+1}=2.548\times \exp \left(0.0815\times \left[\ln \left(10\times \frac{25\times C{I}_i+62}{637}\right)/0.0815+\mathrm{PP}\right]\right)-2.48$	10%	Equation 7
$C{I}_{i+1}=3.396\times \exp \left(0.1006\times \left[\ln \left(5\times \frac{100\times C{I}_i}{1698}\right)/0.1006+\mathrm{PP}\right]\right)-2.93$	Average	Equation 8
$C{I}_{i+1}=5.14\times \exp \left(0.1345\times \left[\ln \left(\frac{500\times C{I}_i+2557}{2570}\right)/0.1345+\mathrm{PP}\right]\right)-5.114$	90%	Equation 9
$T{D^{\ast }}_{i+1}=2.4552\times e^{-0.056\times (-\mathrm{Ln}(T{D}_i/2.4552)/0.056+\mathrm{PP})}$	10%	Equation 10
$T{D}_{i+1}=2.0262\times e^{-0.094\times (-\mathrm{Ln}(T{D}_i/2.0262)/0.094+\mathrm{PP})}$	Average	Equation 11
$T{D}_{i+1}=1.6471\times e^{-0.199\times (-\mathrm{Ln}(T{D}_i/1.64711)/0.199+\mathrm{PP})}$	90%	Equation 12

*TD stands for texture depth

Figure 12. Distress predictions for year 2020 based on data collected in year 2018

Table 5. Number of sections used in the validation process with, MSE, and R2 results.

Distress	Number of validation sections	RMSE at the average prediction	R^2
Rutting in left wheel path	28205	1.27	0.856
Rutting in right wheel path	34092	1.72	0.815
Crack intensity	2549	0.88	0.83
Texture depth	21931	0.15	0.916

Table 6. Criteria considered to rank the accuracy of the distress predictions.

Distress	Excellent	Good	Poor	Unsatisfactory
Rutting mm	±1.0	±2.0	±3.0	>±3.0
Crack intensity %	±0.25	±0.5	±0.75	>±0.75
Texture depth mm	±0.35	±0.7	±1.0	>±1.0

Figure 13. Pie chart illustrating the ranking of the predicted distress accuracy.

Kargah-Ostadi, N., Zhou, Y., & Rahman, T. (2019). Developing performance prediction models for pavement management systems in local governments in absence of age data. Transportation Research Record: Journal of the Transportation Research Board, 2673(3), 334–341. https://doi.org/10.1177/0361198119833680

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Data availability

The data underlying this article will be made available upon reasonable request from the corresponding author subject to data owner approval.