Prediction of International Roughness Index Based on Stacking Fusion Model
Abstract
:1. Introduction
2. Data Preparation
3. Methodology
3.1. Feature Selection Based on RF Algorithm
3.2. Accuracy Evaluation of MLR, GBDT, XGBoost, and SVM Models
3.3. The Stacking Fusion Method
4. Results and Discussion
4.1. Feature Selection Results
4.2. Accuracy Evaluation Results of MLR, GBDT, XGBoost, and SVM Models
4.3. Evaluation of the Stacking Fusion Model
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. | Models | Whether Pavement Distress or Rutting Indicators Are Used | Other Parameters | R2 (Testing) | RMSE | MAE (%) | Segments | Observations |
---|---|---|---|---|---|---|---|---|
1 [18] | MLR | N | Pavement age, previous IRI value (initial IRI), pavement thickness, subgrade stiffness, average rainfall, average temperature | 0.57 | 0.205 | / | 464 | 6222 |
ANN | 0.92 | 0.133 | / | |||||
2 [23] | ANN | N | Age, vehicle per direction heavy vehicle per direction, ESAL per direction | 0.86 | 0.369 | 9.8 | 204 | / |
3 [2] | RNN | N | AADT, ESAL, climate, equipment | 0.87 | 0.14 | / | 1880 | / |
4 [3] | XGBoost | N | Age (years), four specific climate and weather indicators, two specific traffic indicators, modified thickness | 0.7 | / | 12.6 | 1390 | 12,637 |
RF | 0.66 | / | 13.51 | |||||
SVM | 0.44 | / | 17.64 | |||||
5 [17] | ANN | Y | Initial IRI, age | 0.75 | / | / | 506 | 2439 |
6 [13] | RF | Y | Structure, total pavement thickness, initial IRI | 0.974 | 0.078 | / | / | 19,900 |
7 [24] | AdaBoost | Y | Pavement total thickness, initial IRI, AADT, ESAL, freeze precipitation | 0.9751 | 0.094 | / | / | 4265 |
8 [22] | GBM | Y | Structural number, KESAL, unbound granular base thickness, asphalt concrete thickness, temperature, precipitation, initial IRI, age | 0.86572 | 0.176003 | 12.6345 | 211 | / |
DL (deep learning) | 0.829877 | 0.198105 | 12.9814 | |||||
DRF (distributed random forest) | 0.795589 | 0.217154 | 14.5215 | |||||
GLM (Generalized linear model) | 0.824244 | 0.201358 | 15.1275 | |||||
9 [21] | LightGBM | Y | Total thickness, AC ratio, temperature, precipitation, KESAL, freeze index, wind speed, initial IRI time | 0.9 | 0.19 | 11 | 1781 | 100,000 |
ANN | 0.84 | 0.25 | 15 | |||||
RFR | 0.88 | 0.21 | 12 |
No | FIELD_NAME | FIELD_ALIAS |
---|---|---|
1 | GATOR_CRACK_A_L | Low-Severity Alligator Cracking Area |
2 | GATOR_CRACK_A_M | Medium-Severity Alligator Cracking Area |
3 | GATOR_CRACK_A_H | High-Severity Alligator Cracking Area |
4 | BLK_CRACK_A_L | Low-Severity Block Cracking Area |
5 | BLK_CRACK_A_M | Medium-Severity Block Cracking Area |
6 | BLK_CRACK_A_H | High-Severity Block Cracking Area |
7 | EDGE_CRACK_L_L | Low-Severity Edge Crack Length |
8 | EDGE_CRACK_L_M | Medium-Severity Edge Crack Length |
9 | EDGE_CRACK_L_H | High-Severity Edge Crack Length |
10 | LONG_CRACK_WP_L_L | Low-Severity Wheel Path Longitudinal Crack Length |
11 | LONG_CRACK_WP_L_M | Medium-Severity Wheel Path Longitudinal Crack Length |
12 | LONG_CRACK_WP_L_H | High-Severity Wheel Path Longitudinal Crack Length |
13 | LONG_CRACK_WP_SEAL_L_L | Low-Severity Well-Sealed Wheel Path Longitudinal Crack Length |
14 | LONG_CRACK_WP_SEAL_L_M | Medium-Severity Well-Sealed Wheel Path Longitudinal Crack Length |
15 | LONG_CRACK_WP_SEAL_L_H | High-Severity Well-Sealed Wheel Path Longitudinal Crack Length |
16 | LONG_CRACK_NWP_L_L | Low-Severity Non-Wheel Path Longitudinal Crack Length |
17 | LONG_CRACK_NWP_L_M | Medium-Severity Non-Wheel Path Longitudinal Crack Length |
18 | LONG_CRACK_NWP_L_H | High-Severity Non-Wheel Path Longitudinal Crack Length |
19 | LONG_CRACK_NWP_SEAL_L_L | Low-Severity Non-Wheel Path Well-Sealed Longitudinal Crack Length |
20 | LONG_CRACK_NWP_SEAL_L_M | Medium-Severity Non-Wheel Path Well-Sealed Longitudinal Crack Length |
21 | LONG_CRACK_NWP_SEAL_L_H | High-Severity Non-Wheel Path Well-Sealed Longitudinal Crack Length |
22 | TRANS_CRACK_NO_L | Low-Severity Transverse Cracks Number |
23 | TRANS_CRACK_NO_M | Medium-Severity Transverse Cracks Number |
24 | TRANS_CRACK_NO_H | High-Severity Transverse Cracks Number |
25 | TRANS_CRACK_L_L | Low-Severity Transverse Crack Length |
26 | TRANS_CRACK_L_M | Medium-Severity Transverse Crack Length |
27 | TRANS_CRACK_L_H | High-Severity Transverse Crack Length |
28 | TRANS_CRACK_SEAL_L_L | Low-Severity Well-Sealed Transverse Crack Length |
29 | TRANS_CRACK_SEAL_L_M | Medium-Severity Well-Sealed Transverse Crack Length |
30 | TRANS_CRACK_SEAL_L_H | High-Severity Well-Sealed Transverse Crack Length |
31 | PATCH_NO_L | Low-Severity Patches Number |
32 | PATCH_NO_M | Medium-Severity Patches Number |
33 | PATCH_NO_H | High-Severity Patches Number |
34 | MRI | Mean Roughness Index |
35 | LLH_DEPTH_1_8_MEAN | Average Left Lane Half Depth From 1.8m Straight Edge |
36 | RLH_DEPTH_1_8_MEAN | Average Right Lane Half Depth From 1.8m Straight Edge |
37 | MAX_MEAN_DEPTH_1_8 | Maximum Average Depth From 1.8m Straight Edge |
38 | RLH_DEPTH_WIRE_REF_MEAN | Average Right Lane Half Depth From Wire Reference |
39 | MAX_MEAN_DEPTH_WIRE_REF | Maximum Average Depth From Wire Reference |
40 | AADTT_ALL_TRUCKS_TREND | Trend LTPP Lane Annual Average Daily Truck Traffic |
41 | ANNUAL_TRUCK_VOLUME_TREND | LTPP Lane Annual Truck Trend Estimate |
Algorithms | RMSE | MAE | R2 |
---|---|---|---|
GBDT | 0.096 | 0.062 | 0.974 |
XGBoost | 0.162 | 0.084 | 0.925 |
SVM | 0.541 | 0.350 | 0.161 |
MLR | 0.504 | 0.344 | 0.271 |
The stacking fusion model | 0.040 | 0.013 | 0.996 |
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Luo, Z.; Wang, H.; Li, S. Prediction of International Roughness Index Based on Stacking Fusion Model. Sustainability 2022, 14, 6949. https://doi.org/10.3390/su14126949
Luo Z, Wang H, Li S. Prediction of International Roughness Index Based on Stacking Fusion Model. Sustainability. 2022; 14(12):6949. https://doi.org/10.3390/su14126949
Chicago/Turabian StyleLuo, Zhiyuan, Hui Wang, and Shenglin Li. 2022. "Prediction of International Roughness Index Based on Stacking Fusion Model" Sustainability 14, no. 12: 6949. https://doi.org/10.3390/su14126949