Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components
Abstract
:1. Introduction
- Develop a hybrid Gaussian process regression–grey wolf optimization model for simulating the deterioration process of a set of highway tunnel components.
- Validate the developed deterioration model against a set of widely used machine learning models using performance evaluation and statistical comparisons.
2. Literature Review
3. Research Framework
- To improve the prediction performance of Gaussian process regression by optimizing its hyper parameters.
- To select the most significant subset of deterioration features.
4. Model Development
4.1. Gaussian Process Regression
- Type of kernel function.
- Kernel function parameters.
- Type of basis function.
- Gaussian noise standard deviation.
4.2. Grey Wolf Optimization
4.3. Training of the Gaussian Process Regression Model
4.4. Copeland
5. Performance Assessment
6. Model Implementation
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Description |
---|---|
Condition state | Condition state can be good (condition state 1), fair (condition state 2), poor (condition state 3), or severe (condition state 4). |
Age | Age in years since the construction of the tunnel was completed. |
Number of lanes | Number of highway traffic lanes that are carried out by the tunnel. |
Annual average daily traffic | Annual average daily traffic for the inventory route. |
Annual average daily truck traffic | Annual average daily truck traffic for the inventory route. |
Detour length | Total additional travel for the vehicle resulting from closing the tunnel. |
Service in tunnel | Service can be highway (1); highway and railroad (2); highway and pedestrian (3); highway, railroad, and pedestrian (4); or other (5). |
NHS designation | NHS stands for the National Highway System. It can be an inventory route on the National Highway System (1) or not on the National Highway System (0). |
STRAHNET designation | STRAHNET stands for the Strategic Highway Network. STRAHNET is a structure of interstate and principal highways connected to United States military installations. STRAHNET designation can be 1 if the inventory route is a STRAHNET route or 0 if it is not. |
Functional classification | Functional classification can be interstate (1), principal arterial (2), freeways and expressways (3), principal arterial (4), major collector (5), minor collector (6), or local (7). |
Tunnel length | It is length of the tunnel measured along the centerline of the roadway. |
Roadway width | It is the most restrictive minimum distance between curbs or rails on the tunnel roadway. |
Hazardous material restriction | It can be 1 if the tunnel has hazardous material or 0 if it does not. |
Number of bores | It denotes number of bores in the tunnel. |
Tunnel shape | The type of tunnel shape can be oval (1), horseshoe (2), rectangular (3), or circular (4). |
Ground conditions | The type of ground conditions can be soil (1), rock (2), or mixed face (3). |
Complexity | A complex tunnel is distinguished by the presence of advanced functional systems or structural components. The complexity can be either 1 if the tunnel is complex or 0 if it is not. |
Age | Annual Average Daily Truck Traffic | Service in Tunnel | Functional Classification | Number of Bores | Tunnel Shape | Ground Conditions | Actual Condition | Predicted Condition |
---|---|---|---|---|---|---|---|---|
47 | 75,320 | 1 | 1 | 2 | 4 | 1 | 1 | 1 |
68 | 8250 | 1 | 3 | 1 | 2 | 2 | 1 | 1 |
62 | 4822 | 1 | 3 | 1 | 2 | 2 | 2 | 2 |
86 | 1500 | 1 | 6 | 1 | 2 | 2 | 3 | 3 |
86 | 1000 | 1 | 6 | 1 | 2 | 2 | 2 | 2 |
110 | 5 | 3 | 7 | 1 | 2 | 2 | 2 | 2 |
110 | 5 | 1 | 7 | 1 | 2 | 2 | 2 | 3 |
61 | 6650 | 1 | 3 | 1 | 2 | 3 | 3 | 3 |
48 | 7100 | 1 | 7 | 1 | 2 | 3 | 4 | 4 |
61 | 1400 | 1 | 6 | 1 | 2 | 2 | 2 | 1 |
Deterioration Model | |||||
---|---|---|---|---|---|
1.65% | 0.018 | 0.21% | 0.018 | 0.147 | |
17.74% | 0.204 | 1.68% | 0.864 | 1.702 | |
11.98% | 0.149 | 0.79% | 0.425 | 1.24 | |
17.11% | 0.199 | 1.28% | 0.64 | 166 | |
2.69% | 0.028 | 1.02% | 0.24 | 0.23 | |
9.71% | 0.132 | 0.65% | 0.5 | 1.09 | |
7.25% | 0.083 | 1.35% | 0.49 | 0.69 |
Deterioration Model | |||||
---|---|---|---|---|---|
1.24% | 0.018 | 0.55% | 0.445 | 0.17 | |
13.95% | 0.157 | 2.1% | 1.051 | 1.511 | |
6.71% | 0.073 | 2.07% | 0.949 | 0.701 | |
14.37% | 0.148 | 2.62% | 1.134 | 1.427 | |
3.84% | 0.044 | 1.98% | 0.92 | 0.423 | |
2.31% | 0.029 | 0.92% | 0.553 | 0.276 | |
7.14% | 0.084 | 2.17% | 1.029 | 0.811 |
Deterioration Model | |||||
---|---|---|---|---|---|
1.73% | 0.017 | 0.21% | 0.125 | 0.132 | |
12.03% | 0.146 | 0.94% | 0.283 | 1.117 | |
10.78% | 0.128 | 0.92% | 0.773 | 0.974 | |
12.63% | 0.154 | 0.95% | 0.251 | 1.177 | |
5.01% | 0.064 | 0.85% | 0.365 | 0.49 | |
11.29% | 0.125 | 0.63% | 0.235 | 0.957 | |
8.5% | 0.113 | 0.95% | 0.278 | 0.863 |
Deterioration Model | |||||
---|---|---|---|---|---|
2.81% | 0.038 | 0.88% | 0.362 | 0.22 | |
25.81% | 0.283 | 4.02% | 1.024 | 1.615 | |
9.19% | 0.108 | 1.82% | 0.56 | 0.619 | |
17.49% | 0.191 | 4.62% | 1.157 | 1.092 | |
7.05% | 0.082 | 3.99% | 0.991 | 0.471 | |
11.73% | 0.17 | 1.74% | 1.01 | 0.971 | |
8.38% | 0.125 | 2.09% | 0.904 | 0.717 |
Deterioration Model | |||||
---|---|---|---|---|---|
1.21% | 0.012 | 0.17% | 0.114 | 0.138 | |
16.22% | 0.177 | 1.63% | 1.207 | 2.016 | |
9.19% | 0.11 | 0.82% | 0.868 | 1.257 | |
13.22% | 0.151 | 1.23% | 1.066 | 1.726 | |
7.74% | 0.101 | 0.71% | 0.99 | 1.149 | |
10.77% | 0.114 | 0.66% | 0.61 | 1.304 | |
6.84% | 0.089 | 0.81% | 0.958 | 1.02 |
Model | Description | |
---|---|---|
Mean absolute percentage error | 1.83 × 10−1 () | |
Mean absolute percentage error | 1.41 × 10−1 () | |
Mean absolute percentage error | 1.72 × 10−1 () | |
Mean absolute percentage error | 8.08 × 10−1 () | |
Mean absolute percentage error | 2.95 × 10−1 () | |
Mean absolute percentage error | 7.08 × 10−1 () | |
Mean absolute percentage error | 9.67 × 10−1 () | |
Root-mean square percentage error | 1.85 × 10−1 () | |
Root-mean square percentage error | 6.89 × 10−1 () | |
Root-mean square percentage error | 2.41 × 10−1 () | |
Root-mean square percentage error | 3.96 × 10−1 () | |
Root-mean square percentage error | 6.76 × 10−1 () | |
Root-mean square percentage error | 1.47 × 10−1 () | |
Root-mean square percentage error | 5.52 × 10−1 () |
Pair of Deterioration Models | |
---|---|
, ) | ( = 8.7 × 10−4) |
, ) | ( = 1.97 × 10−3) |
, ) | ( = 9.35 × 10−5) |
, ) | ( = 1.79 × 10−2) |
, ) | ( = 1.01 × 10−2) |
, ) | ( = 1.31 × 10−5) |
Pair of Deterioration Models | |
---|---|
, ) | ( = 1.21 × 10−2) |
, ) | ( = 6.53 × 10−3) |
, ) | ( = 1.03 × 10−2) |
, ) | ( = 1.64 × 10−2) |
, ) | ( = 2.79 × 10−2) |
, ) | ( = 9.52 × 10−3) |
Deterioration Model | Loss | Win | Difference | Final Rank |
---|---|---|---|---|
0 | 30 | 30 | 1 | |
28 | 2 | −26 | 7 | |
17 | 13 | −4 | 5 | |
27 | 3 | −24 | 6 | |
8 | 22 | 14 | 2 | |
13 | 17 | 4 | 4 | |
12 | 18 | 6 | 3 |
Deterioration Model | Total Size of Dataset | Training Dataset | Testing Dataset | |
---|---|---|---|---|
model | 404 | 366 | 38 | 7.69% |
Yamany and Elwakil [8] | 404 | 366 | 38 | 29.91% |
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Abdelkader, E.M.; Al-Sakkaf, A.; Elshaboury, N.; Alfalah, G. Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components. Processes 2022, 10, 36. https://doi.org/10.3390/pr10010036
Abdelkader EM, Al-Sakkaf A, Elshaboury N, Alfalah G. Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components. Processes. 2022; 10(1):36. https://doi.org/10.3390/pr10010036
Chicago/Turabian StyleAbdelkader, Eslam Mohammed, Abobakr Al-Sakkaf, Nehal Elshaboury, and Ghasan Alfalah. 2022. "Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components" Processes 10, no. 1: 36. https://doi.org/10.3390/pr10010036