A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification

Abstract

Recently, a variety of intelligent structural damage identification algorithms have been developed and have obtained considerable attention worldwide due to the advantages of reliable analysis and high efficiency. However, the performances of existing intelligent damage identification methods are heavily dependent on the extracted signatures from raw signals. This will lead to the intelligent damage identification method becoming the optimal solution for actual application. Furthermore, the feature extraction and neural network training are time-consuming tasks, which affect the real-time performance in identification results directly. To address these problems, this paper proposes a new intelligent data fusion system for damage detection, combining the probabilistic neural network (PNN), data fusion technology with correlation fractal dimension (CFD). The intelligent system consists of three modules (models): the eigen-level fusion model, the decision-level fusion model and a PNN classifier model. The highlight points of this system are these three intelligent models specialized in certain situations. The eigen-level model is specialized in the case of measured data with enormous samples and uncertainties, and for the case of confidence level of each sensor is determined ahead, the decision-level model is the best choice. The single PNN model is considered only when the data collected is somehow limited, or few sensors have been installed. Numerical simulations of a two-span concrete-filled steel tubular arch bridge in service and a seven-storey steel frame in laboratory were used to validate the hybrid system by identifying both single- and multi-damage patterns. The results show that the hybrid data-fusion system has excellent performance of damage identification, and also has superior capability of anti-noise and robustness.

A Review on Damage Monitoring and Identification Methods for Arch Bridges

“…Thus, several common defects of a CFST arch bridge were investigated and updated. Fu and Jiang [187] proposed a new intelligent damage detection data fusion system by combining a probabilistic neural network (PNN), data fusion technology and correlation fractal dimension (CFD). Single and multiple damage modes were identified by the numerical simulation of a two-span CFST arch bridge.…”

A Hybrid Method for Structural Modal Parameter Identification Based on IEMD/ARMA: A Numerical Study and Experimental Model Validation

“…As shown in Figure 9, a seven-story steel-frame structure laboratory scale model [28] was constructed, and the plane size of this model was 0.4 m × 0.2 m, and it was 1.4125 m in height. Using hot-rolled 300 w grade steel (fy = 300 Mpa), the section size of the beam, column, and the characteristics of the cross-section were as shown in Table 1.…”

A Particle Swarm Optimization Algorithm with Sigmoid Increasing Inertia Weight for Structural Damage Identification

“…Ghiasi et al [39] proposed an improved bat algorithm to improve the accuracy of structural damage identification. Fu and Jiang [40] combined the probabilistic neural network and data fusion technology with the correlation fractal dimension to identify structural damage. Standoli et al [41] developed a finite element model calibration based on ambient vibration tests and machine learning processes.…”