Clustering-Based Framework for Multi-Sensor Data Fusion in Bridge Deck Condition Assessment

Bridge deck deterioration poses a significant challenge to transportation infrastructure, resulting in costly maintenance and potential safety hazards. Traditional bridge deck assessments primarily rely on visual inspections, which can be subjective and fail to capture subsurface defects, such as delamination, rebar corrosion, and concrete degradation. To enhance the accuracy of condition assessment, this study explores multi-sensor data fusion and clustering techniques for defect identification using Ground Penetrating Radar (GPR) and Impact Echo (IE). By integrating multiple Non-Destructive Evaluation (NDE) datasets, a clustering-based framework was developed to automatically categorize bridge deck conditions. K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), and Fuzzy C-Means (FCM) clustering algorithms were evaluated to determine their effectiveness in grouping similar defect patterns. The optimal number of clusters is determined using the Elbow Method, Silhouette Score, and Davies-Bouldin Index. Results indicate that DBSCAN outperforms other clustering techniques in detecting defect hotspots while effectively handling noise and spatial inconsistencies. The clustered defects are mapped spatially to visualize regions of deterioration, enabling bridge engineers to identify high-risk areas and prioritize maintenance efficiently.