Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation
Abstract
:1. Introduction
2. 3D Laser Scanning Data
2.1. Data Description
2.2. 3D Data Processing
- (1)
- Pavement region extraction (Figure 2).
- (2)
- Coordinates transformation.
Algorithm 1. Numerical Algorithm for Coordinates Rotation |
STEP 1. Input the 3D surface data, and extract the plane coordinates x, y. |
STEP 2. Estimate the slope of the plane coordinates s using linear fitting. |
STEP 3. Verify the slope condition: |
(i) if s ≤ 0.05, proceed to Step 6. |
(ii) if s > 0.05, proceed to Step 4. |
STEP 4. Calculate θ based on s using the equation: |
STEP 5. Calculate the rotated matrix using Equation (3). Return to Step 2 and continue the slope calculation until the slope condition is satisfied. |
STEP 6. Output the final coordinates matrix as the rotated result. |
- (3)
- Point cloud data denoising and filtering.
2.3. Validation for 3D Road Surface Measurement
3. Water-Film Prediction Based on 3D Surface Data
3.1. Governing Equations
3.2. Numerical Algorithms
Algorithm 2. Numerical Algorithm for 2DDA-SWE |
STEP 1. Input 3D surface data, rainfall intensity data, and initial parameters. The values of z, qr, g, and nc are known. Set the calculation period T and initial the model time t = 0. |
STEP 2. According to solution time and accuracy requirements, set time step Δt and spatial step Δx and Δy. |
STEP 3. Calculate the intercell flux by Equations (7)–(9). |
STEP 4. Update the model time to t = (n + 1) Δt by Equation (6). |
STEP 5. Verify the CFL condition: |
(i) if CFL ≤ 1, proceed to Step 6. |
(ii) if CFL > 1, increase Δx and Δy or decrease Δt. Then return to Step 3. |
STEP 6. Return to Step 3 and continue until the calculation period is completed. |
3.3. Model Parameter Acquisition
3.4. Model Validation
4. Water-Film Thickness Estimation on Road Surfaces with Different Profiles
4.1. Surface with Slope
4.2. Surface with Rutting
4.3. Rough Surface
5. Hydroplaning Risk Evaluation
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Techniques | Rationale | Road Destructive | Measurement Range | Precision |
---|---|---|---|---|
In-pavement monitoring [11] | Directly measuring water-film thickness by the embedded sensor | Road destructive | Point measurement width < 10 cm | Resolution < 0.1 mm |
Roadside detection [19] | Measuring water-film thickness by infrared remote sensing technology | Non-destructive | Point measurement width < 50 cm | Resolution < 0.1 mm |
3D laser scanning [20] | Measuring the 3D profile of pavement and estimating the water-film thickness | Non-destructive | Continuous measurement width > 10 cm | Resolution < 0.3 mm |
Sample Interval | 0.5 mm | 50 mm | 0.1 m | 0.25 m | 0.5 m | 1 m |
Time Consumption | 83,134.02 s | 11.02 s | 3.26 s | 1.01 s | 0.78 s | 0.67 s |
Scenarios | Typical Geometry |
---|---|
Surface with slope: four samples Cao’an Highway Boyuan Highway | |
Surface with rutting: four samples Jiasong Highway | |
Rough surface: three samples Lianqun Highway | |
Gallaway model | |
USF model |
Variable | Value |
---|---|
MTD | 1.0 mm |
Tire pressure (Pt) | 250 Kpa |
Wheel load (W) | 5000 N |
SD | 1.0 |
Tire tread depth | 1.0 mm |
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Yang, W.; Tian, B.; Fang, Y.; Wu, D.; Zhou, L.; Cai, J. Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation. Int. J. Environ. Res. Public Health 2022, 19, 7699. https://doi.org/10.3390/ijerph19137699
Yang W, Tian B, Fang Y, Wu D, Zhou L, Cai J. Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation. International Journal of Environmental Research and Public Health. 2022; 19(13):7699. https://doi.org/10.3390/ijerph19137699
Chicago/Turabian StyleYang, Wenchen, Bijiang Tian, Yuwei Fang, Difei Wu, Linyi Zhou, and Juewei Cai. 2022. "Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation" International Journal of Environmental Research and Public Health 19, no. 13: 7699. https://doi.org/10.3390/ijerph19137699