In the analysis of vibration of footbridges in vertical direction, for crowd situations, there is evidence in the literature that the whole effect of pedestrian action is not well modeled when applying current force-only models to represent such an action. In these models, the action of each pedestrian is represented by a pulsating force applied on the structure. In this paper, a crowd load model is proposed for sparse and dense crowds (with densities up to around 1.0 pedestrian/m²) in which biodynamic models are added to represent the whole action of pedestrians. The focus of the investigation is on vibration effects in vertical direction. Comparisons with measurements on a prototype footbridge were carried out and made it possible to identify differences in the structural response when applying force-only and force-biodynamic models to represent the pedestrian action. The latter (proposed) model resulted in a better agreement with the measurements.

A significant number of accidents has occurred with demountable grandstands in the past as well as recently. The performance of such structures is the subject of this paper. First, structural details and some structural systems of actual grandstands are presented and discussed. Next, specifics of the design, erection, and inspection process are detailed. Finally, a survey of cases of collapse of demountable grandstands is presented and discussed. Ninety three cases of collapse were found between the years 1889 and 2008, in which more than 85 people died and 6,350 were injured. From these, 35 cases in which information about the causes of collapse were available were selected. An analysis of the causes showed that unexpected or excessive loads were the major contributing cause, followed by problems with the supports, connections, and bracing. Possible measures that could be adopted aimed at minimizing the risk of accidents with these structures are discussed.

The present work evaluates the deep learning algorithm called Sparse Auto-Encoder (SAE) when applied to the characterization of structural anomalies. This study explores the SAE’s performance in a supervised damage detection approach to consolidate its application in the Structural Health Monitoring (SHM) field, especially when dealing with real-case structures. The main idea is to use the SAE to extract relevant features from the monitored signals and the well-known Support Vector Machine (SVM) to classify such characteristics within the context of an SHM problem. Vibration data from a numerical beam model and a highway viaduct in Brazil are considered to assess the proposed approach. In both analyzed examples, the efficiency of the implemented methodology achieved more than 99% of correct damage structural classifications, supporting the conclusion that SAE can extract relevant characteristics from dynamic signals that are useful for SHM applications.

Traffic noise is a highly relevant environmental impact in cities. Models to estimate traffic noise, in turn, can be useful tools to guide mitigation measures. In this paper, the applicability of models to estimate noise levels produced by a continuous flow of vehicles on urban roads is investigated. The aim is to identify which models are more appropriate to estimate traffic noise in urban areas since several models available were conceived to estimate noise from highway traffic. First, measurements of traffic noise, vehicle count and speed were carried out in five arterial urban roads of a brazilian city. Together with geometric measurements of width of lanes and distance from noise meter to lanes, these data were input in several models to estimate traffic noise. The predicted noise levels were then compared to the respective measured counterparts for each road investigated. In addition, a chart showing mean differences in noise between estimations and measurements is presented, to evaluate the overall performance of the models. Measured Leq values varied from 69 to 79 dB(A) for traffic flows varying from 1618 to 5220 vehicles/h. Mean noise level differences between estimations and measurements for all urban roads investigated ranged from −3.5 to 5.5 dB(A). According to the results, deficiencies of some models are discussed while other models are identified as applicable to noise estimations on urban roads in a condition of continuous flow. Key issues to apply such models to urban roads are highlighted.