Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach
Abstract
:1. Introduction
- Marginal Attention to the Interrelations Between Asset Classes: Due to the mutual impacts of nearby assets and similar environmental conditions in their proximity, there is a potential correlation between the condition of neighboring asset classes. A few research studies have investigated such correlations [9,10,11]. However, the majority of the developed deterioration models in the literature did not take into consideration such interrelations and investigated the condition of each asset independent from its neighbors [12,13,14,15,16,17,18]. For example, Abaza et al. [12] forecasted pavement condition only based on historical condition data of pavements. As another example, Immaneni et al. [16] developed prediction models for traffic signs only based on age and retroreflectivity data of the signs.
- Shortcomings of Predictive Frameworks in Dealing with Limited Inspection Data: Random inspection of roadways is the current practice of most transportation agencies that restrict the number of segments with adequate historical condition data of road assets. This usually results in discontinuous records of historical conditions on most road segments during all years of inspection. To overcome this limitation, most of the previous studies used the idea of grouping segments with similar deterioration characteristics (family groups) and estimating the average degradation of each group by utilizing a family deterioration model. For example, Mills et al. [19] developed family pavement performance models to help the Delaware DOT in managing road pavements. In another study, Saha et al. [20] used the family group idea to come up with pavement distress deterioration models. However, several challenges come with this approach. Firstly, the condition of specific segments in a family might be different from the average condition of the family. This is mainly attributed to the local variation of contributors to the degradation of assets such as traffic, weather, and maintenance [21]. Secondly, since the number of families highly impacts the accuracy of family deterioration models finding the optimal number of families is still challenging [22].
- Subjective Expert-based Selection of Contributing Factors to Assets Degradation: Several factors impact the condition of roadway assets and could be considered as the contributing factors to their deterioration. For example, the role of material, traffic loading, weather condition, and historical maintenance on the degradation patterns of multiple assets was highlighted in several studies [12,23,24,25,26,27,28,29,30]. For example, the study performed by Anyala et al. [23] highlighted the impacts of the thickness of flexible pavements and the binder type as two main factors on the resistance of the pavement layer against degradation. As another example, Bannour et al. [24] addressed the role of different ranges of pavements structural composition, environment, moisture and traffic conditions on the deterioration of pavements. However, most studies developed deterioration models when a selected number of contributing factors were considered based on experts’ judgment. In addition, historical maintenance activities, as a major factor that improves the condition of highway assets, have received marginal attention in building previous prediction models [31].
- Maximizing the potential of available data in building prediction models by combining machine learning and risk score generator, offering transportation agencies a practical predictive maintenance planning
- Incorporating the interrelations of defects in multiple nearby assets into a defect prediction method
- Developing a data-driven approach to identify and quantify the most significant contributors to the degradation of multiple assets among a wide range of potential candidates
- Creating a scalable learning-based algorithm to improve maintenance planning for a combination of assets by forecasting the occurrence probability of various defects on multiple asset types
2. Related Works
2.1. Machine Learning in Transportation Asset Management
2.2. Risk-Based Predictive Modelling
3. Methodology
3.1. Collection of Contributing Factors’ Data
3.2. Data Preparation
3.3. Density Estimation of Defects
3.4. Preprocessing for Machine Learning (ML)
3.5. Predictive Modelling
3.5.1. Linear Regression
3.5.2. Nonlinear Regression
3.6. Validation
3.7. Model Selection and Implementation
4. Case Study
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Index | Parameter | Definition |
---|---|---|
1 | TMAX | Annual maximum daily temperature (°C) |
2 | TMIN | Annual minimum daily temperature (°C) |
3 | TMAXMIN | Annual average of daily max-min temperature difference (°C) |
4 | DWT32 | Number of days with minimum temperature < 0 °C (32 °F) in a year |
5 | DWT80 | Number of days with maximum temperature > 26.7 °C (80 °F) in a year |
6 | DWTMXN30 | Number of days with Tmax-Tmin > 16.7 °C (30 °F) in a year |
7 | DSNW | Number of days with snow depth > 2.54 cm (1 inch) in a year |
8 | EMSD | Maximum annual daily snow depth (cm) |
9 | EMXP | Maximum annual daily precipitation depth (cm) |
10 | PRCP | Total annual precipitation (cm) |
11 | SNOW | Total annual snow depth (cm) |
Index | Parameter | Definition |
---|---|---|
1 | ADT | Average daily traffic (number of vehicles per day) |
2 | AAWDT | Average annual weekday traffic (number of vehicles per day) |
3 | ADT_4 | Average daily traffic of 4-tire vehicles (number of vehicles per day) |
4 | ADT_BU | Average daily traffic of buses (number of vehicles per day) |
5 | ADT_TR | Average daily traffic of trucks with 1 trailer (number of vehicles per day) |
6 | ADT_1 | Average daily traffic of trucks with 2 axles (number of vehicles per day) |
7 | ADT_2 | Average daily traffic of trucks with 2 trailers (number of vehicles per day) |
8 | ADT_3 | Average daily traffic of trucks with 3 axles (number of vehicles per day) |
Index | Code | Maintenance Name | Description |
---|---|---|---|
1 | M_70141 | Hand Cleaning | Hand cleaning of drainage assets, traffic control devices, shoulders, tunnels, ferries, etc. Cleaning with manual tools (shovels, pickaxes, etc.). Cleaning without the use of machinery. |
2 | M_70142 | Machine Cleaning/Mechanical Sweeping | Machine cleaning or sweeping of drainage assets such as pipes, ditches, etc.; tunnels; roadside assets such as sidewalks, truck ramps, pedestrian trails, walls, etc.; traffic assets such as rumble strips; pavement assets including roads, and paved shoulders, etc. Also, to be used for cleaning when using pressurized water such as power washing. |
3 | M_71152 | Seeding, Fertilizing, Mulching (Serv) | Seeding, fertilizing, mulching, sodding, soiling, spreading lime. The cyclical and regular replacement and maintenance of vegetation to combat erosion. |
4 | M_72223 | Concrete Patching/Repair-Drainage (Serv) | Patching holes, blow-ups, and other irregularities on concrete surfaces for drainage assets. This activity includes cutting and removing damaged concrete and patching concrete areas. |
5 | M_72224 | Concrete Joint Repair-Drainage (Serv) | Removing and replacing joint filler, pouring joints, trimming joints, joint patching, and other maintenance of drainage concrete joints. |
Asset Type | Acronym | Defects | ||||
---|---|---|---|---|---|---|
D1 | D2 | D3 | D4 | D5 | ||
Flexible Pavement | FPM | Pothole | Patch | - | - | - |
Paved Ditch | PDC | Erosion | Obstruction | Cracking | - | - |
Unpaved Ditch | UPD | Erosion | Obstruction | - | - | - |
Slope | SLP | Erosion | Erosion Pattern | Lower Slope | Higher Slope | - |
Small Pipes and Box Culverts | SPB | Pipe Obstruction | Pipe Joint | Pipe Erosion | Pipe Vegetation | End Wall |
Under Drains and Edge Drains | UED | Drain Outlet Damage | Drain Obstruction | End Protection | - | - |
M_71152 | M_70141 | M_70142 | M_72223 | M_72224 | |
M_71152 | N/A | 9.08 × 10−219 | 6.60 × 10−147 | 1.04 × 10−5 | 5.22 × 10−2 |
M_70141 | 9.08 × 10−219 | N/A | 0.00 | 9.14 × 10−260 | 8.26 × 10−25 |
M_70142 | 6.60 × 10−147 | 0.00 | N/A | 7.22 × 10−159 | 1.99 × 10−42 |
M_72223 | 1.04 × 10−5 | 9.14 × 10−260 | 7.22 × 10−159 | N/A | 0.00 |
M_72224 | 5.22 × 10−2 | 8.26 × 10−25 | 1.99 × 10−42 | 0.00 | N/A |
Utilized ML Algorithm | Erosion | Obstruction | Cracking | |||
---|---|---|---|---|---|---|
Training | Testing | Training | Testing | Training | Testing | |
Multivariate Linear Regression | 0.642 | 0.652 | 0.515 | 0.516 | 0.317 | 0.330 |
Regularized Linear Regression | Ridge | 0.641 | 0.651 | 0.515 | 0.516 | 0.316 | 0.330 |
Regularized Linear Regression | Lasso | 0.600 | 0.602 | 0.479 | 0.481 | 0.127 | 0.150 |
Support Vector Regression | 0.845 | 0.852 | 0.871 | 0.872 | −2.575 | −2.638 |
Artificial Neural Network | 0.968 | 0.969 | 0.982 | 0.982 | 0.919 | 0.911 |
Decision Tree | 0.918 | 0.918 | 0.886 | 0.881 | 0.951 | 0.942 |
Adaptive Boosting | 0.926 | 0.927 | 0.876 | 0.877 | 0.493 | 0.477 |
Random Forest Regression | 0.999 | 0.997 | 0.999 | 0.997 | 0.999 | 0.996 |
Algorithm | Erosion | Obstruction | Cracking | |||
---|---|---|---|---|---|---|
Min Score | Max Score | Min Score | Max Score | Min Score | Max Score | |
Multivariate Linear Regression | 0.614 | 0.672 | 0.480 | 0.546 | 0.285 | 0.350 |
Regularized Linear Regression | Ridge | 0.615 | 0.67 | 0.481 | 0.546 | 0.286 | 0.349 |
Regularized Linear Regression | Lasso | 0.583 | 0.623 | 0.453 | 0.502 | 0.103 | 0.162 |
Support Vector Regression | 0.834 | 0.865 | 0.873 | 0.877 | −2.997 | −2.224 |
Artificial Neural Network | 0.973 | 0.984 | 0.984 | 0.990 | 0.914 | 0.937 |
Decision Tree | 0.893 | 0.927 | 0.829 | 0.891 | 0.933 | 0.963 |
Adaptive Boosting | 0.919 | 0.939 | 0.872 | 0.910 | 0.390 | 0.624 |
Random Forest Regression | 0.996 | 0.999 | 0.998 | 0.999 | 0.997 | 0.999 |
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Karimzadeh, A.; Shoghli, O.; Sabeti, S.; Tabkhi, H. Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach. Sustainability 2022, 14, 4979. https://doi.org/10.3390/su14094979
Karimzadeh A, Shoghli O, Sabeti S, Tabkhi H. Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach. Sustainability. 2022; 14(9):4979. https://doi.org/10.3390/su14094979
Chicago/Turabian StyleKarimzadeh, Arash, Omidreza Shoghli, Sepehr Sabeti, and Hamed Tabkhi. 2022. "Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach" Sustainability 14, no. 9: 4979. https://doi.org/10.3390/su14094979