An Improved Hybrid XGBoost Model for Culvert Inspection Using Swarm Intelligence Algorithms
Publication: Computing in Civil Engineering 2023
ABSTRACT
Departments of transportation (DOTs) must regularly inspect and maintain culverts to prevent their failure. However, DOTs cannot conduct culvert inspections on a regular basis due to their limited maintenance budgets. In this study, we proposed an intelligent approach for inspecting the condition of culverts. We developed a hybrid model based on the eXtreme Gradient Boosting (XGBoost) algorithm to predict culvert condition ratings using culvert inventory data from the states of Utah, Vermont, Ohio, and Colorado. We applied a swarm intelligence algorithm—Grey Wolf Optimizer (GWO)—to fine-tune the XGBoost model’s hyper-parameters and enhance the model’s prediction accuracy. Based on the results, the GWO-XGBoost algorithm outperformed the previous algorithms employed in the literature for predicting culvert condition ratings. This study showed that DOTs could minimize culvert inspection costs while maximizing their culvert network quality by taking advantage of predictive analytics models trained on their current inventory datasets.
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Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 100 - 108
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Algorithms
- Business management
- Construction engineering
- Construction management
- Culverts
- Engineering fundamentals
- Freight transportation
- Hybrid methods
- Infrastructure
- Inspection
- Inventories
- Logistics
- Management methods
- Mathematics
- Methodology (by type)
- Model accuracy
- Models (by type)
- Optimization models
- Pipeline systems
- Pipes
- Practice and Profession
- Ratings
- Transportation engineering
Authors
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