Damage detection on bridge structures based on static deflection measurements of a single point

The principle of structural health monitoring of the bridge is the assessment of the structure performance or safety level comparing with a reference system. The most used technique is the dynamic methods which are employed to determine the structural dynamic characteristics and thereafter to locate the damages or changes in some zones of the structure. While static methods are not widely used although they are simpler than dynamic methods and also they do not require sophisticated equipment. In the last decade, some recent researches develop the interesting deterministic or probabilistic methods to evaluate the flexural rigidity or stiffness on a beam, a structure or a bridge and thus detect any damage. The idea is to analyze the static deflections of one selected point or cross-section of a beam or a bridge with a variable position loading. The developed numerical approach uses an inverse method to solve the static equilibrium equations of a variable positions loading in the structure using the finite element method. A Matlab code is developed to solve this static inverse problem. By knowing the deflections amplitude of a selected point in the structure corresponding to several positions of a load, then the stiffness reduction factor in the bridge can be estimated. Some examples for a beam are treated to test this new method for assessing its rigidity

Truss Bridge Damage Localization and Severity Estimation Using Influence Lines

The safety of bridges is one of the primary concerns of researchers, engineers, and bridge owners and managers, especially when bridges are approaching the end of their intended service lives. The estimation of bridge condition and remaining service life is critical to prioritize the allocation of available funding for repairs and rehabilitation. Various methods, including both dynamic and static approaches, have been developed to detect and localize bridge damage and estimate its severity. This research presents a methodology for detecting a single damaged member in a truss bridge and estimating the severity of the damage using static vertical deflection influence lines (SDILs). The methodology is capable of making assessments using fewer sensors and measurement locations than other state of the art methodologies, thereby minimizing costs and service interruptions to bridge owners. This work comprises the development of the methodology and a parametric study to determine the sensitivity of the methodology to uncertainties faced in practice. The results show that the proposed methodology is able to identify the damaged member and estimate damage severity; performance results are given for various combination of measurement noise levels, number of simulations, and damage severities

Static structural system identification using observability method

Cotutela Universitat Politècnica de Catalunya i Tongji UniversityDuring the construction and operation stages of structures, various factors lead to irreversible degradation that could affect the normal use and the public safety of these structures. In recent years, it has been common to carry out condition assessment of structures using Structural System Identification (SSI) methods. SSI is the application of parameter estimation in structural system. One key issue in SSI is to guarantee the observability of the parameters to be estimated. This was already addressed by the SSI by Observability Method (OM) using static tests. However, a systematic analysis of the effect of measurement and simulation errors for this method is lacking. A ramification of this analysis is the effective strategies to use redundant measurements to tackle measurement errors. Meanwhile, the linearization of unknowns in the SSI by OM might lead to the omission of observable unknowns. This PhD thesis presents a unified SSI method under the framework of OM for 2D structures modelled by beam elements. The method is based on the information (external loads, measured deflections and rotations) obtained during static tests. This work gathers six methodological contributions conceived to (1) extract as much information as possible from measurements to ensure the observability of target parameters; (2) analyze the effect of measurement errors and simulation errors on the estimation results; (3) propose different strategies to use redundant measurements to improve the estimation accuracy; (4) place the sensors in an optimal configuration to obtain robust estimations for the target parameters. Firstly, the procedure of the SSI by OM is presented and validated by error-free measurements in a beam-like structure. Then the effects of measurement errors and simulation errors on the accuracy of estimation result is analyzed for the minimum measurement sets that ensure the observability of all parameters. The studied factors include single measurement errors, random measurement errors, error levels and loading cases. The influence of the recursive process of SSI by OM is also discussed. To solve the problem of misjudging the minimum measurement sets caused by the linear assumption in the SSI by OM, the SSI by constrained OM is proposed. The nonlinear constraints are reintroduced by optimizations after the completion of the method when necessary. The method is validated by a simply supported beam and a high-rise frame. Due to the unsatisfactory SSI results from the SSI by OM using minimum sets, three ways of using redundant measurements are proposed. The SSI by compatible OM reduces the incompatibility due to measurement errors by imposing the compatibility conditions in beam-like structures. In the second method, the theoretical advantage of using rotations in SSI is justified by a statistical analysis using the analytical expression of the target parameters and the inverse distribution theory. Then four strategies to use redundant rotations are proposed and compared. The model averaging method using only rotations is proposed. As the SSI by compatible OM and the model averaging method are subjected to the limit of structure type or measurement type, the SSI by Measurement Error-Minimizing OM (MEMOM) is proposed. In this method, the measurement error terms are separated from the coefficient matrix of the observability equations and the estimations are obtained by minimizing the square sum of the ratios between the error terms and the measurements. The performance of the method is investigated with respect to factors including loading cases, parameterization, measurement types and constraint types. The Optimal Sensor Placement problem for static SSI is addressed in this thesis and is formulated as maximizing the determinant of the Fisher Information Matrix (FIM) using genetic algorithm. Meanwhile, the identifiability of the structural parameters is evaluated according to the diagonal elements of the inversed FIM.Durante las etapas de construcción y operación de las estructuras, varios factores conducen a una degradación irreversible que podría afectar el uso normal y la seguridad pública de estas estructuras. En los últimos años, ha sido común llevar a cabo la evaluación de las condiciones de las estructuras utilizando métodos de Identificación del Sistema Estructural (SSI). SSI es la aplicación de la estimación de parámetros en el sistema estructural. Un aspecto clave en SSI es garantizar la observabilidad de los parámetros a estimar. Esto ya fue abordado por el SSI mediante el Método de Observabilidad (OM) utilizando pruebas estáticas. Sin embargo, falta un análisis sistemático del efecto de los errores de medición y simulación para este método. Una ramificación de este análisis son las estrategias efectivas para usar mediciones redundantes para abordar los errores de medición. Mientras tanto, la linealización de incógnitas en el SSI por OM podría llevar a la omisión de incógnitas observables. Esta tesis doctoral presenta un método SSI unificado en el marco de OM para estructuras 2D modeladas por elementos de haz. El método se basa en la información (cargas externas, deflexiones medidas y rotaciones) obtenida durante las pruebas estáticas. Este trabajo reúne seis contribuciones metodológicas concebidas para (1) extraer tanta información como sea posible de las mediciones para garantizar la observabilidad de los parámetros objetivo; (2) analizar el efecto de errores de medición y errores de simulación en los resultados de la estimación; (3) proponer diferentes estrategias para usar medidas redundantes para mejorar la precisión de la estimación; (4) coloque los sensores en una configuración óptima para obtener estimaciones robustas para los parámetros objetivo. En primer lugar, el procedimiento de SSI por OM se presenta y valida mediante mediciones sin errores en una estructura similar a un haz. A continuación, se analizan los efectos de los errores de medición y simulación sobre la precisión del resultado de la estimación para los conjuntos mínimos de medición que garantizan la observabilidad de todos los parámetros. Los factores estudiados incluyen errores de medición únicos, errores de medición aleatoria, niveles de error y casos de carga. También se discute la influencia del proceso recursivo de SSI por OM. Para resolver el problema de juzgar erróneamente los conjuntos mínimos de medición causados ​​por la suposición lineal en el SSI por OM, se propone el SSI por OM restringido. Las restricciones no lineales son reintroducidas por optimizaciones después de la finalización del método cuando sea necesario. El método es validado por una viga simplemente compatible y un marco de gran altura. Debido a los resultados SSI insatisfactorios del SSI por OM que utilizan conjuntos mínimos, se proponen tres formas de utilizar medidas redundantes. El SSI por OM compatible reduce la incompatibilidad debida a errores de medición al imponer las condiciones de compatibilidad en estructuras similares a vigas. En el segundo método, la ventaja teórica de usar rotaciones en SSI se justifica mediante un análisis estadístico que utiliza la expresión analítica de los parámetros objetivo y la teoría de distribución inversa. Luego, se proponen y se comparan cuatro estrategias para usar rotaciones redundantes. Se propone el método de promediado modelo utilizando solo rotaciones. Como el SSI por el OM compatible y el método de promediado del modelo están sujetos al límite del tipo de estructura o tipo de medida, se propone el SSI mediante OM de minimización de errores de medición (MEMOM). En este método, los términos de error de medición se separan de la matriz de coeficientes de las ecuaciones de observabilidad y las estimaciones se obtienen al minimizar la suma cuadrada de las relaciones entre los términos de error y las mediciones. El rendimiento del método se investiga con respecto a factores que incluyen casos de carga, parametrización, tipos de medición y tipos de restricciones. El problema de la ubicación óptima del sensor para SSI estático se aborda en esta tesis y se formula como la maximización del determinante de la matriz de información de Fisher (FIM) mediante el uso de algoritmo genético. Mientras tanto, la identificabilidad de los parámetros estructurales se evalúa de acuerdo con los elementos diagonales de la FIM inversa.Postprint (published version