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The damage identification of railway bridges poses a formidable challenge given the large variability in the environmental/operational conditions that such structures are subjected to along their lifespan. Within a research project devoted to the continuous monitoring of a real in-operation bridge, the Mascarat Viaduct in Alicante (Spain), this paper investigates the potential use of strain sensors and autoregressive (AR) modelling to extract highly sensitive damage features. The viaduct, built in the beginning of the 20th century, belongs to the TRAM line 9 in the province of Alicante (Spain). The viaduct is instrumented with a dense network of strain sensors and accelerometers for monitoring the structural response under train passages. As an initial exploratory investigation, and with the aim of developing a physics-based digital twin of the viaduct, this work presents an introductory numerical assessment of the potential use of strain measurements and AR models for fast damage identification. To this aim, a finite element model (FEM) of the viaduct has been developed to simulate the dynamic response of the viaduct under moving train loads. On this basis, a two-class damage classifier (damaged or non-damaged) is developed by exploiting time series of continuously identified AR parameters. The presented results and discussion evidence the usefulness of the proposed methodology, offering an unsupervised damage detection approach with minimum computational efforts.

This work shows the preliminary results related to the evaluation of the dynamic behavior of a 5x5 m masonry cross vault after undergoing different changes in its structural behavior. It has been built using solid clay bricks and lime and sand mortar. The cross vault has been built in the laboratory specifically for carrying out these tests and evaluating its structural behavior. A device has been designed to be able to carry out a controlled settling of one of its supports without causing an instantaneous collapse of the structure. Dynamic identification using OMA was performed by installing seismic accelerometers at several selected points on the structure, using ARTEMIS software to analyze the results. The evolution of the dynamic characteristics was carried out in four different states: (i) After construction (ii) After the generation of generalized damage through the forced settlement of one of its supports. (iii) After repair using Textile Reinforced Mortars (TRM) (iv) After generating new damage by settling one of its supports in the TRM repaired structure. After the generation of the damage in the structure, important cracks are observed in various points of the structure, although no situation was reached that would foresee a possible collapse. The repair with TRM has consisted of the introduction of fiberglass mesh bands coated in lime and sand mortar. These bands have been arranged forming a cross in the upper part of the structure and in all the edge arches of the structure. In all these phases the OMA was developed under a situation of ambient vibrations, where these vibrations levels were very low due to the situation inside of a laboratory. After each of these phases, their main frequencies, their vibration modes and the structural damping factor of each of the identified modes have been obtained. It is observed how a loss of rigidity of the vault occurs after the production of damage and how it completely recovers by means of reinforcement and repair

The latest studies on failures in steel truss-type bridge found that they are highly vulnerable to damage and thus prone to potential local or total collapse. Many authors recommend monitoring the critical elements in the existing steel truss-type bridges in real time to anticipate any failures in local members. Although mechanical strain is the most frequently used variable for this purpose, this method also happens to be the most expensive monitoring strategy. This paper describes a case study of failure scenarios assessment in a steel truss-type railway bridge after extensive long-term monitoring conducted by the authors, based on measuring vertical deflections and modal frequencies. The structure has both an isostatic and a hyperstatic configuration, and was assessed by means of a combination of: (i) long-term monitoring results, and (ii) a finite element analysis to simulate several failure scenarios. A sensitivity study of the different failure scenarios has been carried out, identifying those that can be detected. The results are used to define practical recommendations for failure detection by measuring vertical deflections and modal frequencies.

Vehicular traffic represents the most influential loads on the structural integrity of railway bridges, therefore the design on dynamic criteria. This work explores the use of strain dynamic measurements to characterize the health condition of railway bridges under moving train loads. Specifically, the approach proposed in this work exploits the implementation of auto-regressive (AR) time series analysis for continuous damage detection. In this light, continuously extracted AR coefficients are used as damage-sensitive features. To automate the definition of the order of the AR model, the methodology implements a model selection approach based on the Bayesian information criterion (BIC), Akaike Information Criterion (AIC) and Mean Squared Error (MSE). In this exploratory investigation, the suitability and effectiveness of strain measurements against acceleration-based systems are appraised through a case study of a simply supported Euler-Bernoulli beam under moving loads. The moving loads problem in terms of vertical accelerations and normal strains is solved through modal decomposition in closed form. The presented numerical results and discussion evidence the effectiveness of the proposed approach, laying the basis for its implementation to real-world instrumented bridges.