A novel procedure for cable damage identification of cable-stayed bridge using particle swarm optimization and machine learning
Problem
Cables of cable-stayed bridges can become damaged, and identifying and quantifying that damage early is needed to prevent disasters.
Why it matters
Undetected cable damage can compromise the safety of stayed-cable bridges and lead to catastrophic failures.
What the AI did
A CatBoost machine learning model captured nonlinear correlations between vibration characteristics and cable damage, combined with PSO-based finite element model updating to establish a baseline model.
Results
Cable damage identified in a benchmark bridge case study; no numeric accuracy metrics stated in provided abstract
Impact
The procedure was demonstrated on a benchmark bridge case study, suggesting feasibility of early cable damage identification.
Record
| Venue | Structural Health Monitoring |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Van-Thanh Pham, Duc‐Kien Thai, Seung-Eock Kim |
| Citations | 18 (OpenAlex, at import time) |
| References | 89 |
| Model or method | CatBoost (with particle swarm optimization) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1177/14759217241246501 |
| Open access | bronze |
| Keywords | cable-stayed bridge, cable damage detection, CatBoost, particle swarm optimization, structural health monitoring |
Importance rating
real but narrower advance
Cable damage detection for stayed-cable bridges is safety-critical infrastructure monitoring, but abstract only demonstrates a benchmark case study with no scale stated.
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