Next-generation coupled structure-human sensing technology: Enhanced pedestrian-bridge interaction analysis using data fusion and machine learning

Abstract

The consequences of crowd behavior in high-density pedestrian flows, especially in response to exacerbating incidents, can result in tragic outcomes such as trampling and crushing, making the active monitoring of crowd motion crucial, to provide timely danger warnings and implement preventive measures. This paper proposes a novel approach for crowd behavior monitoring and prediction of bridge loads based on the following innovative solutions: (a) advanced optimized signal processing is leveraged for noise reduction; (b) novel data fusion approaches are proposed to extract the most informative measurement features; (c) fine-tuned machine-learning techniques are implemented for classification and regression tasks. Data from structure-based sensors and wearable devices were utilized to capture movement- and load-sensitive data on a pedestrian bridge, which facilitated the determination of crowd flow, density, and bridge loading information. The proposed monitoring approach explores signal preprocessing methodologies, including variational mode decomposition (VMD), downsampling, principal component analysis, and novel data fusion, to effectively minimize noise and errors in the input data. Data fusion strategies were introduced to significantly enhance the learning models and improve the overall efficiency and resilience of the system. For further analysis, a 2D-convolutional neural network (CNN) approach was initially applied independently to the sensing sources and subsequently extended to fuse multimodal raw, decomposed, and denoised data. The proposed monitoring method was validated using experimental data obtained from crowd simulations conducted on a scaled-down bridge panel, utilizing next-generation coupled structure-human sensing, fiber-optic sensing, and smartphone technology. The results demonstrated a high level of accuracy for crowd monitoring predictions, with the peak testing accuracy reaching 99.62% for single-class crowd flow classification, 98.69% for multiclass crowd flow and density classification, and 98.42% in R² score for load estimation when fusing denoised signals using VMD. The proposed 2D-CNN model was compared with an existing adaptive Kalman filter (AKF) fusion technique and various machine learning techniques, including random forest, k-nearest neighbor, support vector machine, XGBoost, and ensemble methods. This comparison unequivocally confirmed the robustness and superiority of the proposed monitoring approach.