This paper presents a feature extraction method to uncover the temperature effects on bridge responses, which combines mode decomposition, data reduction, and blind separation. For mode decomposition, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) have been executed, followed by principal component analysis (PCA) for data size compression. Independent component analysis (ICA) is then employed for blind separation. The unique feature of the proposed method is the blind separation, which enables temperature-induced response to be extracted from the mixed structural responses without any prior information of the loading conditions and structural physical models. This study further evaluates the effects of extracting a temperature-induced response on damage detectability when using Moving Principal Component Analysis (MPCA). Numerical analysis for a truss bridge model is first conducted to evaluate the proposed method for thermal feature extraction, followed by a real truss bridge test in the structural laboratory at the University of Warwick. Results from the numerical study show that the method enables the separation of a temperature-induced response; and furthermore, the EEMD, utilized in mode decomposition, has a positive influence on the blind separation compared with EMD when combined with PCA and ICA. Finally, the real truss bridge test demonstrates that the feature extraction method can enhance the probability of MPCA to uncover the damage as the MPCA fails to discern damage without the proposed method.

In recent years, efforts have been devoted to utilizing terrestrial laser scanning for bridge spatial performance inspection, but they are still restricted to small or medium-span bridges, like some historical heritages. Due to the large-scale dimensional features of long-span bridges, applications of 3D point cloud techniques still remain challenging, such as the extra-long scan range and extreme-small incidence angle when scanning a bridge with a span over 1000 m. Moreover, rare attempts can be found for the performance evaluation of point cloud registration methods for long-span bridges as well, which is a critical basis for further spatial deformation recognition on the point cloud data. Hence, in this study, a cross-evaluation of three iterative closest point (ICP) registration methods is conducted for long-span suspension bridges, namely, traditional ICP, kd-tree-based ICP, and feature point-based ICP algorithms. We conducted field laser scanning on the Ma’anshan Yangtze Bridge, an 1880 m long suspension bridge located in China. The results show that the feature point-based ICP algorithm outperforms the other two in terms of convergence rate and execution time for a single iteration due to the smaller number of registration points compared to the other two algorithms. Moreover, it also gives more precise values in terms of bridge tower spatial deformation identification. Meanwhile, due to efficient data organization, the kd-tree-based ICP algorithm takes less time for a single iteration than the traditional ICP algorithm. Finally, two suggestions for algorithm improvement in terms of efficiency optimization and accuracy improvement of long-span bridge deformation analysis are proposed based on the assessment results.