Alexandre Cury

Assignment of structural behaviours in long-term monitoring: Application to a strengthened railway bridge

Novelty detection, the identification of data that is unusual or different, is relevant in a wide number of real-world scenarios, ranging from identifying unusual weather conditions to detecting evidence of damage in mechanical systems. Using novelty detection approaches for structural health monitoring presents significant challenges to the non-expert user. In this article, symbolic data analysis is introduced to model variability in tests. Hierarchy-divisive methods and dynamic clouds procedures are then used to discriminate structural changes used as novelty detection approaches for classifying structural behaviours. This article reports the study of experimental tests performed on a railway bridge in France. This bridge has undergone reinforcement works during the summer of 2003. Through the years of 2004–2006, new sets of dynamic tests were recorded. The main objective was to analyse the evolution of the bridge’s dynamic behaviour over time. To this end, the symbolic data analysis–based clustering methods are used for assigning new tests to clusters identified before and after strengthening or to highlight a totally different structural behaviour.

A two-step technique for damage assessment using numerical and experimental vibration data

The degradation process of structural systems is usually due to a combination of reasons such as design or constructive problems, unexpected loading or even natural causes. Such deterioration process results in damaged regions whose main characteristics are localized stiffness decreases in the structure. In this article, the efficiency of a damage detection technique is analyzed through experimental tests and numerical simulations performed in a cantilever beam involving several damage scenarios. The methodology consists of two stages: (i) damage location — determined by means of the strain energy deviation between damaged and undamaged structural vibration modes; and (ii) damage quantification — developed through the analysis of measured natural frequencies of a damaged structure and its respective undamaged numerical model. In general, this two-step strategy shows good results although more robust results are obtained by using numerical data rather than by experimental data.

Long term dynamic monitoring of a PSC box girder bridge

Structural Health Monitoring (SHM) aims to determine whether damage is present or not based on the analysis of measured dynamic characteristics of a monitored system. Normal changes are usually caused by modifications in environmental conditions such as humidity, wind and most important, temperature. Conversely, abnormal changes are generally caused by the presence of damage i.e. loss of structural mass and/or stiffness. This paper reports on the effect of temperature changes on the natural frequencies of the PI-57 motorway bridge located at Senlis, France. Dynamic measurements were carried out during almost two years and the goal is to develop a methodology for separating environmental influences from possible damage events. Hence, several regression and model fitting methods are used such as multiple linear regression and neural networks. It is observed that possible structural modifications show up as outliers in the statistical frequency-temperature relations.

A clustering-based strategy for automated structural modal identification

Structural health monitoring of civil infrastructures has great practical importance for engineers, owners and stakeholders. Numerous researches have been carried out using long-term monitoring, such as the Rio–Niterói Bridge in Brazil, the former Z24 Bridge in Switzerland and the Millau Bridge in France. In fact, some structures are continuously monitored to supply dynamic measurements that can be used for the identification of structural problems such as the presence of cracks, excessive vibration or even to perform a quite extensive structural evaluation concerning its reliability and life cycle. The outputs of such an analysis, commonly entitled modal identification, are the so-called modal parameters, that is, natural frequencies, damping rations and mode shapes. Therefore, the development and validation of tools for the automatic modal identification during normal operation is fundamental, as the success of subsequent damage detection algorithms depends on the accuracy of the modal parameters’ estimates. This work proposes a novel methodology to perform, automatically, the modal identification based on the modes’ estimates data generated by any parametric system identification method. To assess the proposed methodology, several tests are conducted using numerically generated signals, as well as experimental data obtained from a simply supported beam and from a motorway bridge.

Advanced Statistical Techniques Applied to Raw Data for Structural Damage Detection

Structural Health Monitoring is one of the most promising and challenging areas of research in the field of Civil Engineering. Over the last decades, researchers have focused on the development of consistent and reliable indicators aiming to detect, locate, quantify or even predict damage. More recently, some researchers are focusing on the use of raw time histories extracted from structural dynamic monitoring to build damage indicators. In this sense, this paper has as its main interest the use of high-order statistics (HOS) coupled with clustering techniques i.e. the k-means and c-means algorithms to detect structural modification (damage). The approach is applied directly to dynamic measurements (accelerations) obtained on site. The efficiency of such methodology is attested by means of a numerical study performed on a model of a simply supported beam and a study based on a real case railway bridge, in France. Results show that HOS coupled with clustering techniques are able to differentiate damage scenarios with adequate classification rates.