Recursive partitioning and Gaussian Process Regression for the detection and localization of damages in pultruded Glass Fiber Reinforced Polymer material

In this paper, a methodology for the detection and localization of damages in composite pultruded members is proposed. This is particularly relevant to thin-walled pultruded members, which are typically characterized by orthotropic behavior, anisotropic along the fibers and isotropic in the cross section. Hence, a method to detect and localize damage, and the influence these might have on the performance of thin-walled Glass Fiber Reinforced Polymer (GFRP) members, is proposed and applied to both numerical and experimental data. Specifically, the numerical and experimental modal shapes of a narrow flange pultruded profile are analyzed. The reliability of the proposed semiparametric statistical method, which is based on Gaussian Processes Regression and Bayesian-based Recursive Partitioning, is analyzed on a narrow flange profile, artificially affected by sawed notches with incremental depth. The numerical investigation is carried out via finite element models (FEMs) of the cracked beam, where the dynamic parameters and the modal shapes are computed. In total, three different crack sizes are investigated, to compare the results with the experimental ones. Finally, the proposed approach is further extended and validated on numerically simulated frame structures

An Experimental Validation of Phase-Based Motion Magnification for Structures with Developing Cracks and Time-Varying Configurations

In this study, Computer Vision and Phase-Based Motion Magnification (PBMM) are validated for continuous Structural Health Monitoring (SHM) purposes. The aim is to identify the exact instant of occurrence for damage or abrupt structural changes from video-extracted, very low amplitude (barely visible) vibrations. The study presents three experimental datasets: a box beam with multiple saw cuts of different lengths and angles, a beam with a full rectangular cross section and a mass added at the tip, and the spar of a prototype High-Aspect-Ratio wing. Both mode-shape- and frequency-based approaches are considered, showing the potential to identify the severity and position of the damage as well A high-definition, high-speed camera and a low-cost commercial alternative have been successfully utilised for these video acquisitions. Finally, the technique is also preliminarily tested for outdoor applications with smartphone cameras

Monitoring of masonry historical constructions: 10 years of static monitoring of the world's largest oval dome

This paper presents the analyses conducted on the data acquired by the monitoring system of the “Regina Montis Regalis” Basilica of Vicoforte (Italy) in the decade 2004–2014. The Basilica is a building of great historical, architectural, and structural significance, owing its fame to its impressive masonry oval dome, the world's largest of this shape (internal axes of 37.23 by 24.89 m). The dome-drum system of the Basilica has suffered over the years of significant structural problems, partly due to the settlements of the building induced progressively by newly built masses and also to the sliding of the underground. In 1983, concerns over the severe settlements and cracking phenomena affecting the structure prompted the decision to undertake strengthening interventions. A special hooping system, consisting of 56 tie bars, placed around the oval perimeter of the dome, was thus conceived to limit the crack opening. The monitoring system of the Basilica installed in the early 1980s underwent several renovations, and in 2004, its acquisition procedure was automatized. One hundred twelve instruments, consisting of temperature sensors, crackmeters, load cells, pressure cells, wire gauges, hygrometer, piezometers, and hydrometer, are currently installed on the Basilica. This study is primarily focused on data acquired by the crackmeters, the extensometers along the main axes of dome, and the load cells placed at the ends of the tie bars. The main aim of the reported analysis is to evaluate the possible progression of the cracks on the Basilica, and the structural performance of the strengthening interventions put in place in 1985–1987

An Experimental Validation of Phase-Based Motion Magnification for Structures with Developing Cracks and Time-Varying Configurations

In this study, Computer Vision and Phase-Based Motion Magnification (PBMM) are validated for continuous Structural Health Monitoring (SHM) purposes. The aim is to identify the exact instant of occurrence for damage or abrupt structural changes from video-extracted, very low amplitude (barely visible) vibrations. The study presents three experimental datasets: a box beam with multiple saw cuts of different lengths and angles, a beam with a full rectangular cross section and a mass added at the tip, and the spar of a prototype High-Aspect-Ratio wing. Both mode-shape- and frequency-based approaches are considered, showing the potential to identify the severity and position of the damage as well A high-definition, high-speed camera and a low-cost commercial alternative have been successfully utilised for these video acquisitions. Finally, the technique is also preliminarily tested for outdoor applications with smartphone cameras

Identification of weak non-linearities in cables of cable-stayed footbridges

A time-frequency identification technique for the non-linear identification of a cable element was proposed in this paper. A polynomial form of non-parametric method was used. A long cable of a newly constructed cable-stayed footbridge was modelled in the ANSYS structural software. The model was reduced to a SDoF system, by applying a harmonic force in the first modal frequency and the first mode shape. A good match between the identified and numerical data was obtained. Some interesting non-linear phenomena were observed: only a cubic type of non-linearity was identified. Moreover, the values of the damping and cubic parameters stabilised at higher load amplitudes. However, parameter relevant to linear-frequency was increasing with the loading amplitude showing a typical hardening behaviour of cable structures. Superharmonics were present in the response at higher loading amplitudes. Therefore, the identification procedure was found to be effective at higher load amplitude

Identification of weak non-linearities in cables of cable-stayed footbridges

A time-frequency identification technique for the non-linear identification of a cable element was proposed in this paper. A polynomial form of non-parametric method was used. A long cable of a newly constructed cable-stayed footbridge was modelled in the ANSYS structural software. The model was reduced to a SDoF system, by applying a harmonic force in the first modal frequency and the first mode shape. A good match between the identified and numerical data was obtained. Some interesting non-linear phenomena were observed: only a cubic type of non-linearity was identified. Moreover, the values of the damping and cubic parameters stabilised at higher load amplitudes. However, parameter relevant to linear-frequency was increasing with the loading amplitude showing a typical hardening behaviour of cable structures. Superharmonics were present in the response at higher loading amplitudes. Therefore, the identification procedure was found to be effective at higher load amplitudes

Progressive damage in terms of resonant frequency in aramid samples to UV light exposure

A recently conceived experimental testing device, based on an optical measurement system and electro-magnetic driving force was used to characterize damping and the non-linear material response, and to carry out an experimental campaign on several Kevlar® fibre samples. The samples were exposed to UV light for different lengths of time. The results show that it is possible to observe an increase in the non-linear response of the material even at low vibratory amplitudes and that the quality factor tends to decrease when the material is damaged

Experimental modal analysis of structural systems by using the fast relaxed vector fitting method

System identification (SI) techniques can be used to identify the dynamic parameters of mechanical systems and civil infrastructures. The aim is to rapidly and consistently model the object of interest, in a quantitative and principled manner. This is also useful in establishing the capacity of a structure to serve its purpose, thus as a tool for structural health monitoring (SHM). In this context, input–output SI techniques allow precise and robust identification regardless of the actual input. However, one of the most popular and widely used approaches, the Rational Fraction Polynomial (RFP) method, has several drawbacks. The fitting problem is nonlinear and generally non-convex, with many local minima; even if linearised via weighting, it can become severely ill-conditioned. Here, a novel proposal for the broadband macro-modelling of structures in the frequency domain with several output and/or input channels is presented. A variant of the vector fitting approach, the Fast Relaxed Vector Fitting (FRVF), applied so far in the literature only for the identification of electrical circuits, is translated and adapted to serve as a technique for structural SI and compared with other traditional techniques. A study about the robustness of the FRVF method with respect to noise is carried out on a numerical system. Finally, the method is applied to two experimental case studies: a scaled model of a high-aspect-ratio (HAR) wing and the well known benchmark problem of the three-storey frame of Los Alamos laboratories. Promising results were achieved in terms of accuracy and computational performance

A machine learning approach for automatic operational modal analysis

One of the major applications of Structural Dynamics in Civil, Mechanical, or Aerospace Engineering regards the dynamic characterisation of man-made structures and components. Yet, traditional Experimental Modal Analysis (EMA) needs dedicated setups which may not be always available where and when needed. For these and other reasons, output-only Operational Modal Analysis (OMA) is regarded as a more practical and convenient alternative. Many OMA algorithms have been reported in the scientific literature during the last twenty and more years. In this study, an Automatic OMA method is presented. The proposed algorithm is completely independent of the user experience, fully objective, and based on statistical principles and a Machine Learning (ML) clustering approach. The AOMA code is firstly applied to a numerical case study, to test all the parameters which control the process. An Airbus H135 helicopter blade is then analysed to verify the performance of the algorithm experimentally