Politecnico di Milano
Recent publications
The Inverse Finite Element method (iFEM), employing a network of strain sensors installed on a structure reconstructs the displacement field independently of the structural loading conditions and material properties. However, the solution is compromised when the sensor network, due to logistic or cost constraints, is sparse and measureless finite elements are present. To overcome this issue the iFEM minimizes a weighted functional, assigning smaller weights to the elements missing experimental measures. Strain field pre-extrapolation techniques have been proposed to improve the iFEM performance, although still assigning arbitrarily small weights to the extrapolated strains. The current paper proposes a Gaussian Process as the pre-extrapolation technique for the strain field, which natively incorporates measurement uncertainty, therefore providing a metric to assign the functional weights, as well as confidence intervals for the displacement field computed through the iFEM. The proposed technique is validated with a virtual experiment; advantages and limitations of the proposed approach are also discussed.
Structural Health Monitoring (SHM) via data-driven techniques can be based upon vibrations acquired by sensor networks. However, technical and economic reasons may prevent the deployment of pervasive sensor networks over civil structures, thus limiting their reliability in terms of damage detection. Moreover, the effects of environmental (and operational) variability may lead to false alarms. To address these challenges, a multi-stage machine learning (ML) method is here proposed by exploiting autoregressive (AR) spectra as damage-sensitive features. The proposed method is framed as follows: (i) computing the distances between different sets of the AR spectra via the log-spectral distance (LSD), providing also the training and test datasets; (ii) removing the potential environmental variability by an auto-associative artificial neural network (AANN), to set normalized training and test datasets; (iii) running a statistical analysis via the Mahalanobis-squared distance (MSD) for early damage detection. The effectiveness of the proposed approach is assessed in the case of limited vibration data for the laboratory truss structure known as the Wooden Bridge. Comparative studies show that the AR spectrum is a reliable feature, sensitive to damage even in the presence of a limited number of sensors in the network; additionally, the multi-stage ML methodology succeeds in early detecting damage under environmental variability.
Palazzo Lombardia is one of the tallest buildings in Milan and the site of the regional government. For these reasons, some years ago a monitoring system was installed in order to assure its continuous operation. The system is based on accelerometers and clinometers at different floors used for dynamic and static monitoring, respectively. A statistical model was developed, such that the time trend of the first eigenfrequencies of the building were modelled through the trend of the clinometer signals and the root mean square (RMS) of some of the accelerometers. This because it was observed that the clinometer signals and the acceleration RMSs are linked to different environmental variables. As examples: the changes of the static configuration of the building due to sun exposure can be described by clinometer signals and acceleration RMSs can take into account the effect of wind. The use of these signals and indexes simplifies the development of the predictive model, compared to the use of measured environmental quantities. The model showed good performances in foreseeing the trend of the first eigenfrequencies. This paper analyses how the reliability of the model, developed with data acquired in 2015–2016, has changed relying on new data acquired in 2021–2022.
Although relevant examples of systems devoted to shape sensing, damage detection, load identification, etc., do exist, even based on fiber optic sensors, they are barely suited for installation on real aerospace applications due to important criticalities in the application of the fiber. However, all HUMS application based on fiber optics can be enabled thanks to the proposed Smart Veil: a technology consisting of a thin composite membrane incorporating fiber optics placed on a complex path. This integrated element makes the monitoring system easy to use/handle, robust and reliable in real operating scenarios, capable of guaranteeing precise measurements. The effectiveness of this technological solution is proven by means of the manufacturing of a sensorized helicopter blade mockup. Among the several techniques that can be adopted using a fiber optic, FBG (Fiber Bragg Grating) sensors have been selected, allowing a robust punctual measure in specific locations. The choice of sensors position was led by the idea to exploit a digital twin for shape reconstruction and load identification, so all the components of the relevant loads acting on a tail rotor blade during operation can be obtained: axial, beam bending, chord bending and torsion. All sensors benefit from the use of the smart veil that guarantees robust and precise measures. Particularly, the torsional sensors do, since they need to be placed on an ad hoc path. The static calibration test and a comparison with a strain gauges system permitted a validation and showed the advantages of the technological solution proposed.
Mood disorders can be difficult to diagnose, evaluate, and treat. They involve affective and cognitive components, both of which need to be closely monitored over the course of the illness. Current methods like interviews and rating scales can be cumbersome, sometimes ineffective, and oftentimes infrequently administered. Even ecological momentary assessments, when used alone, are susceptible to many of the same limitations and still require active participation from the subject. Passive, continuous, frictionless, and ubiquitous means of recording and analyzing mood and cognition obviate the need for more frequent and lengthier doctor’s visits, can help identify misdiagnoses, and would potentially serve as an early warning system to better manage medication adherence and prevent hospitalizations. Activity trackers and smartwatches have long provided exactly such a tool for evaluating physical fitness. What if smartphones, voice assistants, and eventually Internet of Things devices and ambient computing systems could similarly serve as fitness trackers for the brain, without imposing any additional burden on the user? In this chapter, we explore two such early approaches—an in-depth analytical technique based on examining meta-features of virtual keyboard usage and corresponding typing kinematics, and another method which analyzes the acoustic features of recorded speech—to passively and unobtrusively understand mood and cognition in people with bipolar disorder. We review innovative studies that have used these methods to build mathematical models and machine learning frameworks that can provide deep insights into users’ mood and cognitive states. We then outline future research considerations and conclude with discussing the opportunities and challenges afforded by these modes of researching mood disorders and passive sensing approaches in general.
The paper presents the exp-EIA© method applied to a Master of Science university architectural course at Politecnico di Milano for fostering a human-centered and evidence-based urban design approach. The method, coupling architectural and psychological perspectives, enables to investigate the people-environment relationship using Virtual/Augmented Reality technology and psychological assessment scales. The method adopts a tool designed ad hoc to collect data regarding the urban experience and represent the outcomes in various forms, including the cartographic one. The procedure includes a virtual exploration of an area through spherical panoramas and a survey investigating the area’s emotional and cognitive effects from specific points of view. An open discussion on the results with the 38 international students participating in the survey concluded the workshop. In particular, the collected individual reactions to the scenes were clustered, and the average emotional and cognitive results associated with the specific viewpoints were analyzed and discussed. Results show that some particular visual perspective constitutes robust social attractors characterized by intense emotional and cognitive reactions, whereas other points of view in the surroundings are socially negligible and emotionally neutral. This experiential approach favors practical considerations to inform the project phases from a sensory design perspective.
This paper describes a workflow to realize a Virtual tour with a 360° camera to be enjoyed with a new device, a 360° immersive theater. Since their inception, 360° cameras have been subjected to many applications that surpassed initial expectations: not only Virtual tours obtained by image stitching of panoramic photographs as a novel form of visualization but also photogrammetric projects for the creation of 3D Virtual environments too. In this paper we test a new immersive situation of a Virtual tour obtained by stitching of images, not only enjoyed through web application for a desktop or smartphone but through its projection inside an immersive theater of 7 m in diameter also. The use of equirectangular images in an immersive theater is a process already tested; the use of a Virtual tour is the implementation offered. To obtain it the geometry of the equirectangular images projected on the screen of the theater and the implementation of the activities related to its fruition have been investigated and made available through specific app carried out through Unity software.
According to Robert E. Schofield, looking back to the golden age of Scientific Societies we discover that, from the middle of XVII to the XIX century, rather than academic institutions they were considered as the proper alma mater by scientists [1]. Over time, the general reform of the university has gradually reversed this state of things, with few exceptions. This paper proposes some brief reflections on being a Scientific Society (of Geometry and Graphics) nowadays (in its 30th year), including a glance at the present COVID-19 pandemic impact.
The inter-institutional university research between the Politecnico di Milano and the Accademia di Brera (2021–2022), to which this survey has participated, has collected, classified and shared in digital formats the various archival and documentary sources about Camillo Boito: one of the most significant architects in the historical and cultural period of the Unification of Italy. Some of my previous publications on spherical photo-cameras, and about the devices for displaying these images through AR/VR viewers, are here contextualised in particular around the case of research on the meticulous spatiality conceived and realized by the architect Camillo Boito (1836–1914) in some significant projects. This further experience on the field made it possible to verify the functionality and effectiveness of the visualisation of spherical views, not only in technological terms, but in a comparative manner with respect to the usual methods of representing architecture, implying some methodological deductions. In this short essay I therefore briefly propose some methods and results from the survey on two case studies of 19th century interior spaces, through the graphic aspects that can be visualised by the geometric algorithms for their treatment as spherical images, and some considerations deduced from these experiments in the process of geometric visualisation of the designed space.
Monitoring structural behavior of earth structures during construction and in service is a common practice done for safety reasons, consolidation control and maintenance needs. Several are the techniques available for measuring displacements, water pressures and total stresses, not only in these geotechnical structures but also at their foundations. Materials testing has been used for calibrating models for structural design and behavior prediction, and these models can be validated with instrumentation data as well. Relatively recent investigation on the behavior of these materials considering their degree of saturation focuses on monitoring the evolution of water content or suction as function of soil-atmosphere interaction, necessary to predict cyclic and/or accumulated displacements, and has huge potential to predict the impact of climate changes on the performance of existing geotechnical structures. This new need justifies the investment on developing sensors able to be used for in situ monitoring of water in the soils, such as those presented here. Testing and monitoring becomes even more important nowadays when, for sustainability purposes, traditional construction materials are replaced by other geo-materials with unknown behavior and long-term performance (mainly accumulated displacements). Existing experimental protocols and monitoring equipment are used for such cases, however new techniques must be developed to deal with particular behaviors. Three case studies are presented and discussion is made on monitoring equipment used and how monitored data helped understanding the behaviors observed.KeywordsSuctionClimate changesNon-traditional geo-materialsMonitoringAccumulated displacementsChemical propertiesMineralogy
Condition-based monitoring applied to railway bridges represents a topic of major importance and interest among developed countries, such as Italy. In fact, bridges and viaducts represent key components of the transportation network, and therefore they increasingly draw infrastructure managers’ attention. The present work is the result of a project carried out by Politecnico di Milano, consisting of a large experimental campaign conducted on a series of viaducts of the Italian railway network. The ensemble of structures under investigation is composed by 11 viaducts, for a total amount of spans equal to 138. According to a similarity criterion, the latter were subdivided into 8 groups, featured by different properties. Due to its transient nature, the experimental campaign was conducted by means of wireless accelerometers, and it consisted in the extraction of the main modal parameters of each analyzed viaduct span, as well as the characterization of the trains travelling on the line. Through the adoption of an operation modal analysis (OMA) technique, it was then possible to construct a large database of the dynamic features concerning the studied viaducts. This database may be exploited for future studies as an important baseline reference condition, by which potential outlier values may be captured, as a sign of damage occurrence among the monitored structures.KeywordsOperational modal analysisRailway bridgesWireless sensors networkStructural health monitoringCondition-based monitoring
Gaussian mixture models (GMM) are the most-widely employed approach to perform model-based clustering of continuous features. Grievously, with the increasing availability of high-dimensional datasets, their direct applicability is put at stake: GMMs suffer from the curse of dimensionality issue, as the number of parameters grows quadratically with the number of variables. To this extent, a methodological link between Gaussian mixtures and Gaussian graphical models has recently been established in order to provide a framework for performing penalized model-based clustering in presence of large precision matrices. Notwithstanding, current methodologies do not account for the fact that groups may be under or over-connected, thus implicitly assuming similar levels of sparsity across clusters. We overcome this limitation by defining data-driven and component specific penalty factors, automatically accounting for different degrees of connections within groups. A real data experiment on handwritten digits recognition showcases the validity of our proposal.
Robust inference for the Cluster Weighted Model requires the specification of a few hyper-parameters. Their role is crucial for increasing the quality of the estimators, while arbitrary decisions about their value could severely hamper inferential results. To guide the user in the delicate choice of such parameters, a monitoring approach has been introduced in the recent literature, yielding an adaptive method. The approach is here exemplified, via the analysis of a dataset on the effect of punishment regimes on crime rates.
This manuscript, the first of a two-part series, presents a methodology for efficiently implementing an equivalent circuit of nonlinear loudspeakers in the discrete-time domain. This is a crucial step that will allow us to design new algorithms for loudspeaker virtualization in part II of this series. The presented implementation, in fact, is based on Wave Digital Filter (WDF) principles, which lead to a fully explicit model, in the sense that no iterative solvers are needed to compute the output signal. The proposed WDF is highly efficient and modular, since the reference circuit is modeled as a computable interconnection of input-output processing blocks. The accuracy of the implementation is confirmed by comparing it to a ground-truth SPICE simulation of the reference circuit and to measurements on real loudspeakers. This confirms that the proposed Wave Digital modeling approach can be reliably used for the rapid simulation of the transduction behavior of a loudspeaker (within the frequency limits of validity of the equivalent circuit), and can be employed to develop the digital preprocessing method for loudspeaker virtualization described in the second manuscript of this two-part series.
In the present paper, an Energy Management System is proposed to optimally schedule and operate a Virtual Power Plant (VPP) composed of charging stations for e-vehicles, stationary batteries, and renewable energy sources. The model is capable to optimize the bidding process on the Day-Ahead Market (DAM) through a two-stage stochastic formulation, which considers the uncertainties affecting the evaluation of the energy required for the next day. The stochastic scenarios are generated through a Monte Carlo procedure and clustered by a reduced domain k-means algorithm. To manage in real-time the operation of the VPP, a new Rolling Horizon mixed-integer linear programming model is adopted. The effectiveness of the tools developed is proved by numerical simulations reproducing the different operating conditions of the VPP. The benefits of the approach are confirmed by extensive analyses performed over a 4-month period. An increase of the profits of 23 % compared to a non-optimized strategy and of 6 % with respect to a deterministic optimization is observed.
Nonlinear frequency generation at the nanoscale is a hot research topic which is gaining increasing attention in nanophotonics. The generation of harmonics in subwavelength volumes is historically associated with the enhancement of electric fields in the interface of plasmonic structures. Recently, new platforms based on high-index dielectric nanoparticles have emerged as promising alternatives to plasmonic structures for many applications. By exploiting optically induced electric and magnetic response via multipolar Mie resonances, dielectric nanoelements may lead to innovative opportunities in nanoscale nonlinear optics. Dielectric optical nanoantennas enlarge the volume of light–matter interaction with respect to their plasmonic counterpart, since the electromagnetic field can penetrate such materials, and therefore producing a high throughput of the generated harmonics. In this review, we first recap recent developments obtained in high refractive index structures, which mainly concern nonlinear second order effects. Moreover, we discuss configurations of dielectric nano-devices where reconfigurable nonlinear behavior is achieved. The main focus of this work concerns efficient Sum Frequency Generation in dielectric nano-platforms. The reported results may serve as a reference for the development of new nonlinear devices for nanophotonic applications.
Cage stability is an essential indicator of the guaranteed efficiency and reliability of the rolling element bearing. Moreover, cage instability can greatly shorten the bearing's service life. The whirl characteristics of the cage caused by ball-cage collisions are closely related to the overall bearing skidding degree. To explore the stability and skidding characteristics of self-lubricated cages used in spacecraft angular contact bearings, a comprehensive bearing dynamic model focused on cage characteristics is proposed. The cage was divided into Nb (number of balls) segments owing to the low stiffness of the cage material (porous polyimide). The model comprised ball self-rotating and revolution motions with 4*Nb degrees of freedom (DOF) and cage motions with Nb + 3 DOF. In the latter, 3 represents the cage whirling motion in the translational and axial directions. The ball-pocket normal and tangential forces, ball-pocket axial collisions, the ball-raceway traction force and moment, imbalances, centrifugal force, and thin oil film lubrication are included in the model. A test bench for exploring the cage motion with a high-speed camera to capture cage images was developed. Three experimental case studies investigating the effects of operating speed and applied load validated the effectiveness and accuracy of the model. Several indicators describing cage stability and cage skidding degree were proposed based on the experimental and theoretical results. It was found that the rate of increase of the whirl radius reduced with a linear increase in the rotation speed. The whirling radius displayed an approximate hyperbolic downward trend with increasing axial force. The skidding results suggested that applying a large axial load to the bearing may have been counterproductive in preventing bearing skidding. In addition, the cage was prone to instability as the radial load increased owing to intensive cage-guide ring rubbing.
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29,761 members
Stefano Maffei
  • Department of Design
Giacomo Verticale
  • Department of Electronics, Information, and Bioengineering
Edie Miglio
  • Department of Mathematics "Francesco Brioschi"
Fabrizio D'Errico
  • Department of Mechanical Engineering
Paolo volonté
  • Department of Design
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