Machine learning-based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges

Structural health monitoring (SHM) is one of the main research topics in civil, mechanical and aerospace engineering. In this regard, modal parameters and their trends over time can be used as features and indicators of damage occurrence and growth. However, for practical reasons, output-only techniques are particularly suitable for the system identification (SI) of large civil structures and infrastructures, as they do not require a controlled source of input force. In this context, these approaches are typically referred to as operational modal analysis (OMA) techniques. However, the interpretation of the OMA identifications is a labour-intensive task, which could be better automated with artificial intelligence and machine learning (ML) techniques. In particular, clustering and cluster analysis can be used to group unlabelled datasets and interpret them. In this study, a novel multi-stage clustering algorithm for automatic OMA (AOMA) is tested and validated for SHM applications-specifically, for damage detection and severity assessment-to a masonry arch bridge. The experimental case study involves a 1:2 scaled model, progressively damaged to simulate foundation scouring at the central pier

A novel approach to damage localisation based on bispectral analysis and neural network

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation

Using video processing for the full-field identification of backbone curves in case of large vibrations

Nonlinear modal analysis is a demanding yet imperative task to rigorously address real-life situations where the dynamics involved clearly exceed the limits of linear approximation. The specific case of geometric nonlinearities, where the effects induced by the second and higher-order terms in the strain–displacement relationship cannot be neglected, is of great significance for structural engineering in most of its fields of application—aerospace, civil construction, mechanical systems, and so on. However, this nonlinear behaviour is strongly affected by even small changes in stiffness or mass, e.g., by applying physically-attached sensors to the structure of interest. Indeed, the sensors placement introduces a certain amount of geometric hardening and mass variation, which becomes relevant for very flexible structures. The effects of mass loading, while highly recognised to be much larger in the nonlinear domain than in its linear counterpart, have seldom been explored experimentally. In this context, the aim of this paper is to perform a noncontact, full-field nonlinear investigation of the very light and very flexible XB-1 air wing prototype aluminum spar, applying the well-known resonance decay method. Video processing in general, and a high-speed, optical target tracking technique in particular, are proposed for this purpose; the methodology can be easily extended to any slender beam-like or plate-like element. Obtained results have been used to describe the first nonlinear normal mode of the spar in both unloaded and sensors-loaded conditions by means of their respective backbone curves. Noticeable changes were encountered between the two conditions when the structure undergoes large-amplitude flexural vibrations

Video processing techniques for the contactless investigation of large oscillations

The experimental acquisition of large vibrations presents various technical difficulties. Especially in the case of geometric nonlinearities, dealing with very flexible, very light structures causes minimal variations in mass or stiffness to affect severely the dynamical response. Thus, sensors' added masses change the behaviour of the structure with respect to the unloaded condition. Moreover, the most common tools regularly employed for acquisition in vibration analysis - that is to say, laser vibrometers and accelerometers - are often designed with small amplitudes in mind. Their recordings are known to lack accuracy when the investigated structure undergoes large or very large motions, due to geometrical reasons. Image-based measurement techniques offer a valid solution to this problem. Here, an ensemble of three video processing techniques are benchmarked against each other and tested as viable options for the non-contact dynamic characterisation of slender beam-like structures. The methods have been applied to the case study of an aluminium spar for a highly-flexible airwing prototype and compared to the measurements recorded by a laser velocimeter and several Raspberry PI Inertial Measurement Units (IMUs), which also proved to be minimally invasive

RoIFusion: 3D Object Detection from LiDAR and Vision

When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensor (e.g. camera, LIDAR) typically increases the robustness of 3D detectors. However, the efficient and effective fusion of different features captured from LIDAR and camera is still challenging, especially due to the sparsity and irregularity of point cloud distributions. This notwithstanding, point clouds offer useful complementary information. In this paper, we would like to leverage the advantages of LIDAR and camera sensors by proposing a deep neural network architecture for the fusion and the efficient detection of 3D objects by identifying their corresponding 3D bounding boxes with orientation. In order to achieve this task, instead of densely combining the point-wise feature of the point cloud and the related pixel features, we propose a novel fusion algorithm by projecting a set of 3D Region of Interests (RoIs) from the point clouds to the 2D RoIs of the corresponding the images. Finally, we demonstrate that our deep fusion approach achieves state-of-the-art performance on the KITTI 3D object detection challenging benchmark.Comment: 11 page

Non-destructive testing on aramid fibres for the long-term assessment of interventions on heritage structures

High strength fibre reinforced polymers (FRPs) are composite materials made of fibres such as carbon, aramid and/or glass, and a resin matrix. FRPs are commonly used for structural repair and strengthening interventions and exhibit high potential for applications to existing constructions, including heritage buildings. In regard to aramid fibres, uncertainties about the long-term behaviour of these materials have often made the designers reluctant to use them in structural engineering. The present study describes simple and non-destructive nonlinearity tests for assessing damage or degradation of structural properties in Kevlar fibres. This was obtained by using high precision measurements to detect small deviations in the dynamic response measured on fibres and ropes. The change in dynamic properties was then related to a damage produced by exposure of the sample to UV rays for a defined time period, which simulated long-term sun exposure. In order to investigate the sensitivity of such an approach to damage detection, non-linearity characterisation tests were conducted on aramid fibres in both damaged and undamaged states. With the purpose of carrying out dynamic tests on small fibre specimens, a dedicated instrumentation was designed and built in cooperation with the Metrology Laboratory of the Department of Electronics at the Politecnico di Torino

Semi-active control of the rocking motion of monolithic art objects

The seismic behaviour of many art objects and obelisks can be analysed in the context of the seismic response of rigid blocks. Starting from the pioneering works by Housner, a large number of analytical studies of the rigid block dynamics were proposed. In fact, despite its apparent simplicity, the motion of a rigid block involves a number of complex dynamic phenomena such as impacts, sliding, uplift and geometric nonlinearities. While most of the current strategies to avoid toppling consist in preventing rocking motion, in this paper a novel semi-active on–off control strategy for protecting monolithic art objects was investigated. The control procedure under study follows a feedback–feedforward scheme that is realised by switching the stiffness of the anchorages located at the two lower corner of the block between two values. Overturning spectra have been calculated in order to clarify the benefits of applying a semi-active control instead of a passive control strategy. In accordance with similar studies, the numerical investigation took into account the dynamic response of blocks with different slenderness and size subject to one-sine pulse excitation