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The dynamic response of a railway bridge depends on several parameters; the primary parameter is the fundamental natural frequency of vibration of the bridge itself. It is considered a critical parameter of the bridge as the driving or the forcing frequencies arising from moving trains may coincide with the fundamental frequency of the bridge and initiate a resonant response amplifying the bridge load effects. This condition may adversely affect the stresses experienced on bridge members and, consequently, the remaining fatigue life of the structure. Because the train adds additional time-varying mass to the bridge, this introduces a time-varying change in the bridge's fundamental natural frequency of vibration. As a result, train critical speeds will have a certain range depending on the train configuration. This article presents a simplified method using a power-law relationship to predict the frequency characteristics of a bridge as a function of the train-to-bridge mass ratio. The method is presented in a generalized form, which enables the frequency characteristics to be determined for any given combination of trains and simply supported bridges of short to medium span typically found on the UK rail network. The method is then demonstrated in a case study of a single-span, simply supported plate girder bridge. By considering the BS-5400 train traffic types, the proposed method is used to calculate bridge frequency effects, dynamic amplification, and train critical speed bandwidth for each train type. The simplicity of the proposed method, as it does not require any complex computational modeling, makes it an ideal and effective tool for the practicing engineer to carry out a quick and economical assessment of a bridge for any given train configuration. [ABSTRACT FROM AUTHOR]

Conventional bridge weigh-in-motion (BWIM) uses a bridge influence line to find the axle weights of passing vehicles that minimize the sum of squares of differences between theoretical and measured responses. An alternative approach, probabilistic bridge weigh-in-motion (pBWIM), is proposed here. The pBWIM approach uses a probabilistic influence line and seeks to find the most probable axle weights, given the measurements. The inferred axle weights are those with the greatest probability amongst all possible combinations of values. The measurement sensors used in pBWIM are similar to BWIM, containing free-of-axle detector sensors to calculate axle spacings and vehicle speed and weighing sensors to record deformations of the bridge. The pBWIM concept is tested here using a numerical model and a bridge in Slovenia. In a simulation, 200 randomly generated 2-axle trucks pass over a 6 m long simply supported beam. The bending moment at mid-span is used to find the axle weights. In the field tests, 77 pre-weighed trucks traveled over an integral slab bridge and the strain response in the soffit at mid-span was recorded. Results show that pBWIM has good potential to improve the accuracy of BWIM. Key words: bridge weigh-in-motion (BWIM), probabilistic bridge weigh-in-motion (pBWIM), bridge load modelling, vehicle loading, vehicle-bridge interaction finite element. Le pesage dynamique de pont (PDP) traditionnel a recours a une ligne d'influence de pont pour trouver les poids des essieux de vehicules passants qui minimisent la somme des carres de differences entre les reponses theoriques et mesurees. Une autre approche, soit le pesage dynamique de pont probabiliste (PDPP), est proposee ici. L'approche PDPP utilise une ligne d'influence probabiliste et cherche a trouver les poids les plus probables des essieux, etant donne les mesures. Les poids d'essieux inferes sont ceux ayant la plus grande probabilite parmi toutes les combinaisons possibles de valeurs. Les capteurs de mesure utilises pour le PDPP sont semblables a ceux pour le PDP, disposant de capteurs a detection libre d'essieux (DLE) pour calculer les espacements d'essieux et la vitesse de vehicules et de capteurs de poids pour enregistrer les deformations du pont. Le concept du PDPP est mis a l'essai ici utilisant un modele numerique et un pont en Slovenie. Dans le cadre d'une simulation, on genere de facon aleatoire deux cents camions qui passent sur une poutre simple de 6 m de long. Le moment flechissant au milieu de la travee est utilise pour trouver les poids des essieux. Dans le cadre des essais sur le terrain, soixante-dix-sept camions prepeses ont traverse un pont-dalle sans joints et la reponse en deformation de la sous-face au milieu de la travee a ete enregistree. Les resultats indiquent que le PDPP a un bon potentiel pour pouvoir ameliorer l'exactitude du PDP. [Traduit par la Redaction] Mots-cles : pesage dynamique de pont (PDP), pesage dynamique de pont probabiliste (PDPP), modelisation de charge de pont, chargement de vehicules, interaction vehicules-pont par elements finis., 1. Introduction As the main imposed loads on a bridge, the weights of vehicles play an important role in determining its safety (Yu et al. 2016). With increasing vehicle loading [...]

Probabilistic analysis of traffic loading on a bridge traditionally involves an extrapolation from measured or simulated load effects to a characteristic maximum value. In recent years, long run simulations, whereby thousands of years of traffic are simulated, have allowed researchers to gain new insights into the nature of the traffic scenarios that govern at the limit state. For example, mobile cranes and low-loaders, sometimes accompanied by a common articulated truck, have been shown to govern in most cases. In this paper, the extreme loading scenarios identified in the long-run simulation are applied to a non- linear, two-dimensional (2D) plate finite element model. For the first time, the loading scenarios that govern in 2D nonlinear analyses are found and compared to those that govern for 2D linear and one-dimensional (1D) linear and nonlinear analyses. Results show that, for an isotropic slab, the governing loading scenarios are similar to those that govern in simple 1D (beam) models. Furthermore, there are only slight differences in the critical positions of the vehicles. It is also evident that the load effects causing failure in the 2D linear elastic plate models are significantly lower, i.e., 2D linear elastic analysis is more conservative than both 2D nonlinear and 1D linear and nonlinear analyses. Key words: nonlinear analysis, extreme traffic loading, traffic, long-run simulation. L'analyse probabiliste du volume de trafic sur un pont consiste habituellement a extrapoler une valeur caracteristique maximale a partir de valeurs d'effets de charge mesures ou simules. Au cours des dernieres annees, des simulations du trafic a long terme, au moyen desquelles on simule le trafic sur des milliers d'annees, ont permis aux chercheurs de mieux comprendre la nature des scenarios de trafic dont l'influence est predominante a l'etat limite. On a montre, par exemple, que les grues mobiles et vehicules a plate-forme surbaissee, parfois associees a des camions ordinaires semi-remorques, jouaient un role preponderant dans la plupart des cas. Dans le present article, on applique un modele d'analyse non lineaire bidimensionnelle (2D) de plaques par la methode des elements finis aux scenarios de volume de trafic extreme identifies lors des simulations du trafic a long terme. Pour la premiere fois, on identifie et on compare les scenarios de volume de trafic qui ont une influence predominante dans les analyses non lineaires 2D a ceux qui predominent dans les analyses lineaires 2D et les analyses lineaires et non lineaires unidimensionnelles. Les resultats montrent que, dans le cas d'une dalle de beton isotropique composant la structure d'un pont, les scenarios predominants de volume de trafic sont similaires a ceux qui predominent dans les modeles simples et unidimensionnels (poutres). De plus, les positions critiques des vehicules ne different que legerement les unes des autres. Il semble par ailleurs evident que les effets de charge qui occasionnent des dommages dans les modeles d'analyse elastique lineaire 2D de plaques sont bien plus faibles et donc que l'analyse elastique lineaire 2D est plus prudente que les analyses non lineaires 2D et que les analyses lineaires et non lineaires unidimensionnelles. [Traduit par la Redaction] Mots-cles: analyse non lineaire, volume de trafic extreme, trafic, simulation a long terme., Introduction A key part of bridge management is the assessment of the safety of bridge structures. A bridge is considered to be safe when its capacity to resist load exceeds [...]

With the growing number of well-aged bridges and the urgency in developing reliable, (pseudo-) real-time monitoring of structural safety and integrity, there is a worldwide and widespread campaign toward transforming structural health monitoring practice. Among these attempts, the application of data-driven approaches in developing damage identification techniques has received particular attention in recent years. Given the growing volume of structural health monitoring data, the power of data-driven approaches has been further exploited. These efforts have been predominantly focused on building and training algorithms using direct measurements from bridges. Although recent years have seen transformative technologies in producing cheap and wireless sensors, network-wide bridge instrumentation is logistically difficult and expensive. This has led to a new group of structural health monitoring systems entitled indirect or drive-by approaches. In drive-by systems, measurements from an instrumented vehicle are used to extract structural damage signatures. In other words, in these systems, the instrumented vehicle acts as both actuator and receiver while passing over a bridge. The main challenge in deploying drive-by approaches for damage identification purposes is that the signals collected on drive-by vehicles also embody signatures from the vehicle, road/rail profile and are easily contaminated by environmental and operational conditions. Furthermore, the majority of current drive-by damage identification systems rely on prior knowledge of vehicle or bridge dynamic characteristics which has led to limited application of the concept in practice so far. To address these challenges, this study employs a powerful class of deep learning algorithm to develop a damage identification system using measurements on an instrumented travelling train. The proposed algorithm is capable of automatically extracting damage signatures from train-borne measurements only. To demonstrate the algorithm's capability, the method is applied to measurements collected on a model instrumented train travelling on a simply supported model steel bridge. For this purpose, a deep convolutional neural network is built, optimised, trained and tested to detect damage using acceleration signals collected on the instrumented train only. The hyperparameters of the algorithm are optimised using the Bayesian optimisation technique. The accuracy of the algorithm is experimentally tested for four positive damage scenarios (combination of two different locations and intensity) and three different travelling speeds. This is the first demonstration of the data-driven drive-by damage identification system under scaled operational environment conditions. The performance of the proposed method is discussed under different travelling speeds and different damage states. The result shows that the proposed method can accurately and automatically detect and classify damage under varying speed, rail irregularities and operational noise using train-borne measurements only and offers a great promise in transforming the future of bridge damage identification system. [ABSTRACT FROM AUTHOR]

Resistance and loads are often correlated in time and space. The paper assesses the influence of these correlations on structural reliability/probability of failure for a typical two-lane reinforced concrete (RC) slab bridge under realistic traffic loading. Spatial variables for structural resistance are cover and concrete compressive strength, which in turn affect the strength and chloride-induced corrosion of RC elements. Random variables include pit depth and model error. Correlation of weights between trucks in adjacent lanes and inter-vehicle gaps are also included and are calibrated against weigh-in-motion data. Reliability analysis of deteriorating bridges needs to incorporate uncertainties associated with parameters governing the deterioration process and loading. One of the major unanswered questions in the work carried out to date is the influence of spatial variability of load and resistance on failure probability. Spatial variability research carried out to date has been mainly focused on predicting the remaining lifetime of a corroding structure and spatial variability of material, dimensional and environmental properties. A major shortcoming in the work carried out to date is the lack of an allowance for the spatial variability of applied traffic loads. In this article, a two-dimensional (2D) random field is developed where load effects and time-dependent structural resistance are calculated for each segment in the field. The 2D spatial time-dependent reliability analysis of an RC slab bridge found that a spatially correlated resistance results in only a small increase in probability of failure. Despite the fact that load effect at points along the length of a bridge is strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small. [ABSTRACT FROM AUTHOR]