Development of a machine learning based methodology for bridge health monitoring

Tesi en modalitat de compendi de publicacionsIn recent years the scientific community has been developing new techniques in structural health monitoring (SHM) to identify the damages in civil structures specially in bridges. The bridge health monitoring (BHM) systems serve to reduce overall life-cycle maintenance costs for bridges, as their main objective is to prevent catastrophic failures and damages. In the BHM using dynamic data, there are several problems related to the post-processing of the vibration signals such as: (i) when the modal-based dynamic features like natural frequencies, modes shape and damping are used, they present a limitation in relation to damage location, since they are based on a global response of the structure; (ii) presence of noise in the measurement of vibration responses; (iii) inadequate use of existing algorithms for damage feature extraction because of neglecting the non-linearity and non-stationarity of the recorded signals; (iv) environmental and operational conditions can also generate false damage detections in bridges; (v) the drawbacks of traditional algorithms for processing large amounts of data obtained from the BHM. This thesis proposes new vibration-based parameters and methods with focus on damage detection, localization and quantification, considering a mixed robust methodology that includes signal processing and machine learning methods to solve the identified problems. The increasing volume of bridge monitoring data makes it interesting to study the ability of advanced tools and systems to extract useful information from dynamic and static variables. In the field of Machine Learning (ML) and Artificial Intelligence (AI), powerful algorithms have been developed to face problems where the amount of data is much larger (big data). The possibilities of ML techniques (unsupervised algorithms) were analyzed here in bridges taking into account both operational and environmental conditions. A critical literature review was performed and a deep study of the accuracy and performance of a set of algorithms for detecting damage in three real bridges and one numerical model. In the literature review inherent to the vibration-based damage detection, several state-of-the-art methods have been studied that do not consider the nature of the data and the characteristics of the applied excitation (possible non-linearity, non-stationarity, presence or absence of environmental and/or operational effects) and the noise level of the sensors. Besides, most research uses modal-based damage characteristics that have some limitations. A poor data normalization is performed by the majority of methods and both operational and environmental variability is not properly accounted for. Likewise, the huge amount of data recorded requires automatic procedures with proven capacity to reduce the possibility of false alarms. On the other hand, many investigations have limitations since only numerical or laboratory cases are studied. Therefore, a methodology is proposed by the combination of several algorithms to avoid them. The conclusions show a robust methodology based on ML algorithms capable to detect, localize and quantify damage. It allows the engineers to verify bridges and anticipate significant structural damage when occurs. Moreover, the proposed non-modal parameters show their feasibility as damage features using ambient and forced vibrations. Hilbert-Huang Transform (HHT) in conjunction with Marginal Hilbert Spectrum and Instantaneous Phase Difference shows a great capability to analyze the nonlinear and nonstationary response signals for damage identification under operational conditions. The proposed strategy combines algorithms for signal processing (ICEEMDAN and HHT) and ML (k-means) to conduct damage detection and localization in bridges by using the traffic-induced vibration data in real-time operation.En los últimos años la comunidad científica ha desarrollado nuevas técnicas en monitoreo de salud estructural (SHM) para identificar los daños en estructuras civiles especialmente en puentes. Los sistemas de monitoreo de puentes (BHM) sirven para reducir los costos generales de mantenimiento del ciclo de vida, ya que su principal objetivo es prevenir daños y fallas catastróficas. En el BHM que utiliza datos dinámicos, existen varios problemas relacionados con el procesamiento posterior de las señales de vibración, tales como: (i) cuando se utilizan características dinámicas modales como frecuencias naturales, formas de modos y amortiguamiento, presentan una limitación en relación con la localización del daño, ya que se basan en una respuesta global de la estructura; (ii) presencia de ruido en la medición de las respuestas de vibración; (iii) uso inadecuado de los algoritmos existentes para la extracción de características de daño debido a la no linealidad y la no estacionariedad de las señales registradas; (iv) las condiciones ambientales y operativas también pueden generar falsas detecciones de daños en los puentes; (v) los inconvenientes de los algoritmos tradicionales para procesar grandes cantidades de datos obtenidos del BHM. Esta tesis propone nuevos parámetros y métodos basados en vibraciones con enfoque en la detección, localización y cuantificación de daños, considerando una metodología robusta que incluye métodos de procesamiento de señales y aprendizaje automático. El creciente volumen de datos de monitoreo de puentes hace que sea interesante estudiar la capacidad de herramientas y sistemas avanzados para extraer información útil de variables dinámicas y estáticas. En el campo del Machine Learning (ML) y la Inteligencia Artificial (IA) se han desarrollado potentes algoritmos para afrontar problemas donde la cantidad de datos es mucho mayor (big data). Aquí se analizaron las posibilidades de las técnicas ML (algoritmos no supervisados) teniendo en cuenta tanto las condiciones operativas como ambientales. Se realizó una revisión crítica de la literatura y se llevó a cabo un estudio profundo de la precisión y el rendimiento de un conjunto de algoritmos para la detección de daños en tres puentes reales y un modelo numérico. En la revisión de literatura se han estudiado varios métodos que no consideran la naturaleza de los datos y las características de la excitación aplicada (posible no linealidad, no estacionariedad, presencia o ausencia de efectos ambientales y/u operativos) y el nivel de ruido de los sensores. Además, la mayoría de las investigaciones utilizan características de daño modales que tienen algunas limitaciones. Estos métodos realizan una normalización deficiente de los datos y no se tiene en cuenta la variabilidad operativa y ambiental. Asimismo, la gran cantidad de datos registrados requiere de procedimientos automáticos para reducir la posibilidad de falsas alarmas. Por otro lado, muchas investigaciones tienen limitaciones ya que solo se estudian casos numéricos o de laboratorio. Por ello, se propone una metodología mediante la combinación de varios algoritmos. Las conclusiones muestran una metodología robusta basada en algoritmos de ML capaces de detectar, localizar y cuantificar daños. Permite a los ingenieros verificar puentes y anticipar daños estructurales. Además, los parámetros no modales propuestos muestran su viabilidad como características de daño utilizando vibraciones ambientales y forzadas. La Transformada de Hilbert-Huang (HHT) junto con el Espectro Marginal de Hilbert y la Diferencia de Fase Instantánea muestran una gran capacidad para analizar las señales de respuesta no lineales y no estacionarias para la identificación de daños en condiciones operativas. La estrategia propuesta combina algoritmos para el procesamiento de señales (ICEEMDAN y HHT) y ML (k-means) para detectar y localizar daños en puentes mediante el uso de datos de vibraciones inducidas por el tráfico en tiempo real.Postprint (published version

Evaluación de la influencia de los factores ambientales en las propiedades dinámicas de sistemas estructurales de tierra

El comportamiento dinámico de las estructuras patrimoniales y modernas se ven afectadas por las condiciones medio ambientales ya que pueden enmascarar los cambios de las propiedades de vibración causados por el daño estructural. Las propiedades dinámicas son a menudo sensibles a los cambios de los factores ambientales que a su vez son variables generalmente no uniformes y dependientes del tiempo. La fácil toma de datos ambientales de las estructuras nos permite obtener modelos útiles para entender la relación entre las propiedades dinámicas y los efectos ambientales. La presente investigación tiene como objetivo presentar los resultados de cuantificar los efectos de las condiciones ambientales sobre las propiedades dinámicas de construcciones de tierra mediante pruebas de laboratorio. Como primer paso se construyeron especímenes cilíndricos de tierra para evaluar el proceso de secado en un ambiente controlado mediante el monitoreo continuo de la temperatura y humedad. El segundo paso fue construir un espécimen de acero para validar la metodología de automatización del monitoreo dinámico y ambiental. Como último paso se construyeron en el laboratorio de la PUCP tres muros de adobe que repliquen las características del material y las dimensiones de los muros del Complejo Arqueológico Huaca de La Luna (Trujillo-Perú). Por último se desarrolló un programa de monitoreo continuo a largo plazo que registra el comportamiento dinámico y ambiental y se realizó la correlación para lo cual se construyeron y compararon modelos estadísticos ARX y MLRM. El trabajo presenta los resultados de las mediciones y muestra que es posible distinguir los cambios de propiedades dinámicas debido a los efectos ambientales en sistemas estructurales de tierra. De la misma manera se comprobó experimentalmente la existencia de dos etapas en el monitoreo continuo de los muros: primero una etapa de secado y segundo una etapa de operación. Finalmente se obtuvo el tiempo de retraso de la temperatura interna y externa y cómo influye en el espesor de los muros ya que el material tierra tiene la propiedad de almacenar calor.Tesi

Non-modal vibration-based methods for bridge damage identification

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Structure and Infrastructure Engineering on 2019, available online at: http://www.tandfonline.com/10.1080/15732479.2019.1650080Many methods of damage identification in bridge structures have focused on the use of numerical models, modal parameters or non-destructive damage tests as a means of condition assessment. These techniques can often be very effective but can also suffer from specific pitfalls such as, numerical model calibration issues for non-linear and inelastic behaviour, modal parameter sensitivity to environmental and operational conditions and bridge usage restrictions for non-destructive testing. This paper covers alternative approaches to damage identification of bridge structures using empirical parameters applied to measured vibration response data obtained from two field experiments of progressively damaged bridges subjected to ambient and vehicle-induced excitation, respectively. Numerous non-modal vibration-based damage features are detailed and selected for the assessment of either the ambient or vehicle-induced excitation data based on their inherent properties. The results of the application to two real bridges, one under ambient vibration and the other of forced vibration, demonstrate the robustness of the proposed damage features for damage identification using measurements of ambient and vehicle excitations. Moreover, this investigation has demonstrated that the novel empirical vibration parameters assessed are suitable for damage detection, localisation and quantification.Peer ReviewedPostprint (author's final draft

Damage identification in a benchmark bridge under a moving load using Hilbert-Huang Transform of transient vibrations

The damage identification process through Structural Health Monitoring (SHM) field has drawn extensive attention over the last decades for its numerous applications in failure prevention and maintenance decision-making. Several research in vibration-based methods for SHM have shown that a potential structural damage can be inferred from a change in the dynamic response of the structure. The aim of this paper is to detect and locate different damage scenarios in a benchmark bridge structure under a moving load based on Hilbert-Huang Transform (HHT). The data used in this study was obtained from the TU1402 benchmark towards enhancement of the value of SHM. The benchmark model consisted of a two-span steel bridge, where six levels of damage grouped in two damage region cases were introduced. In the proposed damage detection method, the transient vibration signals coming from a moving load in the bridge, are firstly decomposed into intrinsic mode functions (IMFs) using the Variational Mode Decomposition (VMD) approach. Then, the Hilbert Transform (HT) is applied to the IMFs. Lastly, the Marginal Hilbert Spectrum (MHS) and the Instantaneous Phase Difference (IPD) were used as damage indicators by comparing the undamaged condition of the bridge with each damage scenario. Results demonstrated that the proposed damage indicators were accurate for identifying and locating damage under transient vibration loads.The second author acknowledges the financial support provided by the Government of Peru through the Educational Credit Program PRONABEC, as well as his Ph.D. Scholarship. The authors are grateful to Prof. Eleni Chatzi, ETH Zurich, Switzerland for the valuable numerical benchmark data assessed within this study. Authors are indebted to the Secretaria d’ Universitats i Recerca de la Generalitat de Catalunya for the funding provided through AGAUR (2017 SGR 1481).Postprint (author's final draft

Damage detection of bridges considering environmental variability using Hilbert-Huang Transform and Principal Component Analysis

Structural Health Monitoring (SHM) systems have been heavily studied worldwide in the past decades. In this field, extensive research has been carried out on vibration-based damage detection (VBDD) techniques in civil structures, especially in bridges. Dynamic responses of a structure manifest a certain degree of sensitivity not only to structural damage but also to any change in operational and environmental conditions, these last factors can mask structural damages. In this sense, the main objective of this paper is to separate structural damage conditions from the changes caused by the environmental effects in a numerical benchmark bridge structure. Temperature is chosen as a global environmental parameter for its significant impact on the waveform, and the Instantaneous Phase Difference (IPD) obtained from an analysis of the Hilbert spectral is studied as the vibration damage feature. Principal Component Analysis (PCA) is applied mainly to the IPD in order to eliminate the environmental influence. Due to the lack of experimental data including the temperature effects, the effectiveness and robustness of the proposed procedure is applied to a numerical benchmark bridge structure generated as part of COST Action TU1402 on quantifying the value of information (VoI) in SHM. The benchmark model consisted of a two-span steel bridge under operational (vehicular traffic) and environmental variability, in which two levels of damage were introduced. The dynamic responses in both healthy and structural damage conditions were obtained from a nonlinear time-history analysis using an open access Python code. As the main concluding remark, the suitability of Hilbert-Huang Transform combined with a PCA-based approach and the instantaneous phase difference to achieve a more robust damage assessment algorithm is verified for the numerical benchmark bridge.The second author wishes to express their gratitude for the financial support received from PRONABEC Program of Peruvian Ministry of Education with the President of the Republic Scholarship for the great support on his PhD studies. The authors wish to thank the Prof. Eleni Chatzi, ETH Zurich, for their valuable sharing of the entire data from numerical benchmark bridge used in the present research. Authors are indebted to the Secretaria d’ Universitats i Recerca de la Generalitat de Catalunya for the funding provided through AGAUR (2017 SGR 1481).Postprint (author's final draft

Bridge damage detection and quantification under environmental effects by principal component analysis

The final authenticated version is available online at https://doi.org/10.1007/978-3-030-91877-4_22.Monitoring structural damage is widely used for sustaining and preserving the service life in civil structures, especially in bridges. The influence of environmental variability like temperature affects the dynamic behavior, which can mask subtler structural changes caused by damage. The direct application of vibration-based damage detection methods to measured responses without a prior treatment of the ambient data may lead to false condition assessments. In this article, the main objective is to separate the structural damage conditions from the changes caused by the environmental effects in a numerical benchmark bridge. The Principal Component Analysis (PCA) is applied to decide if the change in vibration characteristics is due to environmental effects or structural damages. The proposed approach in the use of PCA not only allows to detect the damage without the requirement of the baseline to consist of damage sensitivity features obtained from a wide range of environmental conditions, but also serves as a measure for its quantification. The effectiveness and robustness of the proposed methodology is applied to a benchmark bridge structure generated as part of COST Action TU1402 on quantifying the value of information (VoI) in SHM. The benchmark model consisted of a two-span steel bridge under environmental effects, in which two levels of damage were introduced.Peer ReviewedPostprint (author's final draft

Marginal Hilbert spectrum and instantaneous phase difference as total damage indicators in bridges under operational traffic loads

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Structure and infrastructure engineering on 2021, available online at: http://www.tandfonline.com/10.1080/15732479.2021.1982994.The challenges and future trends in the development of signal processing tools are being widely used for damage identification in bridges. Therefore, it is important to analyse the vibration signals in order to attain effective damage characterization. In this paper, the non-linear and non-stationary dynamic response of bridges under operational loads is studied. First, the signals are decomposed into intrinsic mode functions (IMF) by a novel Improved Completed Ensemble EMD with Adaptive Noise technique (ICEEMDAN). Hilbert-Huang transform is used to obtain their corresponding Hilbert spectra. The marginal Hilbert spectrum (MHS) of each IMF and the instantaneous phase difference (IPD) are proposed as total damage indicators (DI), in the sense that they are able to detect, localize and quantify damage under transient vibration due to traffic. The methodology was tested in two case studies: (i) a numerical model of a two-span steel bridge (ii) a dynamic test conducted on a real steel arch bridge subjected to a series of artificial damages. The experimental and real case results from the damage indices based on the extracted features demonstrate the robustness and more sensitivity of the novel Improved Completed Ensemble EMD with Adaptive Noise technique (ICEEMDAN) in addressing the damage location.Peer ReviewedPostprint (author's final draft