There is a need for reliable structural health monitoring (SHM) systems that can detect local and global structural damage in existing steel bridges. In this paper, a data-based SHM approach for damage detection in steel bridges is presented. An extensive experimental study is performed to obtain data from a real bridge under different structural state conditions, where damage is introduced based on a comprehensive investigation of common types of steel bridge damage reported in the literature. An analysis approach that includes a setup with two sensor groups for capturing both the local and global responses of the bridge is considered. From this, an unsupervised machine learning algorithm is applied and compared with four supervised machine learning algorithms. An evaluation of the damage types that can best be detected is performed by utilizing the supervised machine learning algorithms. It is demonstrated that relevant structural damage in steel bridges can be found and that unsupervised machine learning can perform almost as well as supervised machine learning. As such, the results obtained from this study provide a major contribution towards establishing a methodology for damage detection that can be employed in SHM systems on existing steel bridges.

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This paper proposes a new method for extracting static influence lines from measurements on bridges. The response of a structure is the convolution of the load and the influence line. Previous research has not embraced the fact that convolution is very efficiently handled in the frequency domain. The new method is based on the Fourier transform, which reduces the computational complexity of influence line extraction by several orders of magnitude compared to the conventional matrix method. The method can therefore be used to extract influence lines in near real time when implemented in low-powered devices, high-sensor-count systems, under high sampling rates and/or long signal sizes. It is shown that the inverse approach is ill-posed for certain vehicle configurations. A regularization technique for the ill-posed inverse problem is provided by a stabilizing filter. A numerical example is used to validate the regularization technique. The feasibility of the proposed method on real-world applications is demonstrated by a case study. The method is relevant to applications of and research on B-WIM algorithms, damage detection in structural health monitoring applications as well as model validation and model updating in the model-based evaluation of bridges.

This paper presents a methodology for classifying train passages into different types with a weigh-in-motion (WIM) system to allow the calibration of railway fatigue load models and identify individual vehicles from the measurements for the continuous calibration of railway WIM stations from in-service trains. The quality assurance of the measured responses is demonstrated using statistical methods. This paper discusses the measurement station, the method used for processing the raw data, the algorithm used to identify the train types and vehicles automatically, and the limits of the obtained load spectra. The measurement errors are demonstrated to be satisfying for use in fatigue load model calibration. Furthermore, this paper proposes actions for accurately obtaining the actual traffic conditions and describes the future work required in this area.