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Abstract: The earliest scientific descriptions of excessive pedestrian-induced lateral vibrations are dated back to the 1970s, but it was not until the beginning of the new millennium that bridge engineers fully comprehended the potential negative effect of pedestrian crowds on long-span footbridges. Following the unexpected serviceability failures of Paris’ Solférino and London’s Millennium footbridges in 1999 and 2000, a new tract of research was initiated, focused on understanding the phenomenon which has become known as Synchronous Lateral Excitation (SLE). In this paper, a comprehensive review of studies related to pedestrian-induced lateral vibrations of footbridges is provided, primarily focusing on studies published within the last decade. Research in this field can generally be split into three categories; (i) full-scale testing of existing bridges subject to crowd loading, (ii) laboratory studies on human-structure interaction between single pedestrians and laterally moving platforms and (iii) mathematical modelling of the pedestrian-induced load. It is shown herein, that a significant amount of research has been carried out within each of the three categories, but there is only limited interconnection, particularly between the mathematical models on one side and the empirical observations on the other. The main purpose of this review is to provide this link, through a detailed and critical review of publications within each of the three categories. [Copyright &y& Elsevier]

Abstract: Structural health monitoring is a problem which can be addressed at many levels. One of the more promising approaches used in damage assessment problems is based on pattern recognition. The idea is to extract features from the data that characterize only the normal condition and to use them as a template or reference. During structural monitoring, data are measured and the appropriate features are extracted as well as compared (in some sense) to the reference. Any significant deviations from the reference are considered as signal novelty or damage. In this paper, the corpus of symbolic data analysis (SDA) is applied on the one hand for classifying different structural behaviors and on the other hand for comparing any structural behavior to the previous classification when new data become available. For this purpose, raw information (acceleration measurements) and also processed information (modal data) are used for feature extraction. Some SDA techniques are applied for data classification: hierarchy-divisive methods, dynamic clustering and hierarchy-agglomerative schemes. Results regarding experimental tests performed on a railway bridge in France are presented in order to show the efficiency of the described methodology. The results show that the SDA methods are efficient to classify and to discriminate structural modifications either considering the vibration data or the modal parameters. In general, both hierarchy-divisive and dynamic cloud methods produce better results compared to those obtained by using the hierarchy-agglomerative method. More robust results are given by modal data than by measurement data. [Copyright &y& Elsevier]