Stefano Mariani

Politecnico di Milano | Polimi · Department of Civil and Environmental Engineering

The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, an...

Size sorting, line focusing, and isolation of microparticles or cells are fundamental ingredients in the improvement of disease diagnostic tools adopted in biology and biomedicine. Microfluidic devices are exploited as a solution to transport and manipulate (bio)particles via a liquid flow. Use of acoustic waves traveling through the fluid provides...

Periodic elastic metamaterials (EMMs) display the capability to forbid the transmission of elastic waves for certain frequency ranges, leading to band gaps. If topology optimization strategies are exploited to tune the band gaps of EMMs, the said band gaps cannot be modified in real-time. This limitation can be overcome by allowing for active mater...

The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, an...

In this paper, the transient dynamic response of shear type multi-storey buildings subjected to earthquake ground motion is generated via adversarial learning technique under different damage conditions, starting from the relevant undamaged responses. A Representation Generative Adversarial Network (RepGAN) is trained on a database of synthetic acc...

Stochastic approaches to structural health monitoring (SHM) are often inevitably limited by computational constraints. For instance, for Markov chain Monte Carlo algorithms relying upon computationally expensive finite element models it is almost infeasible to sample the probability distribution of the structural state. To provide instead real-time...

This Issue of Engineering Proceedings assembles the papers presented at the 9th International Electronic Conference on Sensors and Applications (ECSA-9), held online on 1–15 November 2022, through the sciforum [...]

Polycrystalline silicon is a brittle material, and its strength results are stochastically linked to microscale (or even nanoscale) defects, possibly dependent on the grain size and morphology. In this paper, we focus on the out-of-plane tensile strength of columnar polysilicon. The investigation has been carried out through a combination of a newl...

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 opera...

Under a specific ground motion excitation, even if structural components all satisfy a target performance level, the serviceability of the structure might get affected by the performance of non-structural components. Although the overall performance of a structure is affected by the performance of both structural and non-structural components, seis...

Microelectromechanical systems (MEMS) are nowadays widespread in the sensor market, with several different applications. New production techniques and ever smaller device geometries require a continuous investigation of potential failure mechanisms in such devices. This work presents an experimental on-chip setup to assess the geometry- and materia...

Microfluidic devices can provide innovative means to handle and control the transport of (bio)particles within a fluid flow. The advantage of microscale devices is that different components can be integrated in a single chip at low cost, with a negligible power consumption, compared to alternative solutions. In this work, a numerical investigation...

Data-driven formulations are currently developed to deal with the complexity of the multi-physics governing the response of microelectromechanical systems (MEMS) to external stimuli and can be extremely helpful. Such devices are in fact characterized by a hierarchy of length and timescales, which are difficult to fully account for in a purely model...

Automated vibration-based structural health monitoring (SHM) strategies have been recently proven to be promising in the presence of aging and material deterioration threatening the safety of civil structures. Within such a framework, ensuring high-quality and informative data is a critical aspect that is highly dependent on the deployment of the s...

The structural health monitoring (SHM) of civil structures and infrastructures is becoming a crucial issue in our smart and hyper-connected age. Due to structural aging and to unexpected loading conditions, partially linked to extreme events caused by the climate change, reliable and real-time SHM schemes are currently facing a burst in development...

Recent advances in learning systems and sensor technology have enabled powerful strategies for autonomous data-driven damage detection in structural systems. This work proposes a novel method for the real-time localization of damage relying on a Siamese convolutional neural network. The method exploits a learnable mapping of raw vibration measureme...

We study the relationship between the architectural form of tall buildings and their structural response to a conventional seismic load. A series of models are generated by varying the top and bottom plan geometries of the buildings, and a steel diagrid structure is mapped onto their skin. A supervised machine learning approach is then adopted to l...

Data-driven approaches to structural health monitoring (SHM) have been recently shown to be a powerful paradigm, helping to lead to an evolution of traditional scheduled-based maintenance methodologies towards condition-based ones. Nevertheless, only few of them provide monitoring scenarios accounting for the varying loading and environmental condi...

Microsystems or Micro Electro Mechanical Systems (MEMS) are very small machines that over the last thirty years had an impressive development in terms of potentialities and diffusion. MEMS are now widespread as micro sensors and/or micro actuators and can be found in many objects of common use. The purpose of the present Chapter is to give an overv...

This paper presents a methodology to move toward reliable real-time structural health monitoring (SHM). The proposed procedure relies upon surrogate modeling based on a multi-fidelity (MF) deep neural network (DNN), conceived to map damage and operational parameters onto sensor recordings. Within a stochastic framework, the MF-DNN is adopted by a M...

This chapter is mainly based on the reliability experience gained by the authors during qualification, production ramp-up, and field quality of the main MEMS product families developed by STMicroelectronics in the last two decades. Even though out of the main scope of the chapter, characterization and basic failure modes studies of silicon as struc...

Background Due to their size, Micro Electromechanical Systems (MEMS) display performance indices affected by uncertainties linked to the mechanical properties and to the geometry of the films constituting their movable parts. Objective In this perspective, a recently proposed multiscale and hybrid solution for uncertainty quantification is discuss...

Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, unde...

This issue of Engineering Proceedings gathers the papers presented at the 8th International Electronic Conference on Sensors and Applications (ECSA-8), held online on 1–15 November 2021, through the sciforum.net platform developed by MDPI [...]

The last frontier of Structural Health Monitoring is real-time damage localization, which requires reliable and efficient statistical and computational tools. We treat the damage localization problem as a classification problem, considering a finite number of possible damage scenarios in a structure under varying loading conditions. A dataset of po...

Within a structural health monitoring (SHM) framework, we propose a simulation-based classification strategy to move towards online damage localization. The procedure combines parametric Model Order Reduction (MOR) techniques and Fully Convolutional Networks (FCNs) to analyze raw vibration measurements recorded on the monitored structure. First, a...

Aging of structures and infrastructures urges new approaches to ensure higher safety levels without service interruptions. Structural health monitoring (SHM) aims to cope with this need by processing the data continuously acquired by pervasive sensor networks, handled as vibration recordings. Damage diagnosis of a structure consists of detecting, l...

The seismic bearing capacity of a shallow strip footing above a void displays a complex dependence on several characteristics, linked to geometric problems and to the soil properties. Hence, setting analytical models to estimate such bearing capacity is extremely challenging. In this work, machine learning (ML) techniques have been employed to pred...

Civil structures and infrastructures such as buildings, bridges, tunnels and dams play a crucial role in our society. Their safety and health are threatened by different factors: aging, progressive accumulation of damage and alteration of working and environmental conditions with respect to the design ones, are just a few examples. Rebuilding these...

Civil structures, infrastructures and lifelines are constantly threatened by natural hazards and climate change. Structural Health Monitoring (SHM) has therefore become an active field of research in view of online structural damage detection and long term maintenance planning. In this work, we propose a new SHM approach leveraging a deep Generativ...

Microelectromechanical systems (MEMS) are often affected in their operational environment by different physical phenomena, each one possibly occurring at different length and time scales. Data-driven formulations can then be helpful to deal with such complexity in their modeling. By referring to a single-axis Lorentz force micro-magnetometer, chara...

To meet the need for reliable real-time monitoring of civil structures, safety control and optimization of maintenance operations, this paper presents a computational method for the stochastic estimation of the degradation of the load bearing structural properties. Exploiting a Bayesian framework, the procedure sequentially updates the posterior pr...

In this work, we exploit supervised machine learning (ML) to investigate the relationship between architectural form and structural efficiency under seismic excitations. We inspect a small dataset of simulated responses of tall buildings, differing in terms of base and top plans within which a vertical transformation method is adopted (tapered form...

In civil engineering, different machine learning algorithms have been adopted to process the huge amount of data continuously acquired through sensor networks and solve inverse problems. Challenging issues linked to structural health monitoring or load identification are currently related to big data, consisting of structural vibration recordings s...

Upscaling of the mechanical properties of polycrystalline aggregates might require complex and time-consuming procedures, if adopted to help in the design and reliability analysis of micro-devices. In inertial micro electro-mechanical systems (MEMS), the movable parts are often made of polycrystalline silicon films and, due to the current trend tow...

Several micro devices, such as micro-mirrors, are subjected to working conditions featuring alternating loadings that can possibly induce fatigue in the thin metal layers, which represent critical structural parts. The quantification of the degradation of the material properties under fatigue loading is a time-consuming task, and the effects of env...

The response of micromachines to the external actions is typically affected by a scattering, which is, on its own, induced by their microstructure and by stages of the microfabrication process. The progressive reduction in size of the mechanical components, forced by a path towards (further) miniaturization, has recently enhanced the outcomes of th...

The path towards miniaturization for micro-electro-mechanical systems (MEMS) has recently increased the effects of stochastic variability at the (sub)micron scale on the overall performance of the devices. We recently proposed and designed an on-chip testing device to characterize two sources of variability that majorly affect the scattering in res...

Ultrasonic motors are characterized by low speed and high-torque operation, without the need for gear trains. They can be compact and lightweight, and they can also work in the absence of applied loads, due to the frictional coupling between the rotor and the stator induced by the traveling wave. In this work, we discuss a concept design based on t...

Within a structural health monitoring (SHM) framework, we propose a simulation-based classification strategy to move towards online damage localization. The procedure combines parametric Model Order Reduction (MOR) techniques and Fully Convolutional Networks (FCNs) to analyze raw vibration measurements recorded on the monitored structure. First, a...

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitionin...

This issue of Engineering Proceedings gathers the papers presented at the 7th International Electronic Conference on Sensors and Applications (ECSA-7), held online on 15–30 November 2020 through the sciforum.net platform developed by MDPI.

The current miniaturization trend in the market of inertial microsystems is leading to movable device parts with sizes comparable to the characteristic length-scale of the polycrystalline silicon film morphology. The relevant output of micro electro-mechanical systems (MEMS) is thus more and more affected by a scattering, induced by features result...

Vibration-based Structural Health Monitoring (SHM) methods often rely upon vibration responses measured with a pervasive network of sensors. In some cases, it does not look possible for technical and economic reasons to equip civil structures with a distributed sensing system. Hence, the amount of information to handle for damage detection may be s...

Tunnel-form structures represent a new type of structural systems with enhanced earthquake resistance and considerably reduced construction times, if compared to conventional reinforced concrete frames and dual systems. Due to the limited information about the seismic performance of tunnel-form buildings in the presence of vertical and horizontal i...

Current progress in sensor technology is setting the ground to push toward satisfactory solutions to challenging engineering problems, like e.g., system identification and Structural Health Monitoring (SHM). In civil engineering, SHM is often based on the analysis of vibrational recordings, represented by time histories of displacements and/or acce...

Dealing with complex engineering problems characterized by Big Data, particularly in the structural engineering area, has recently received considerable attention due to its high societal importance. Data-driven structural health monitoring (SHM) methods aim at assessing the structural state and detecting any adverse change caused by damage, so as...

The appraisal of energy demands of informal communities living in spaces which are often re-appropriated, raises the issue of social discrimination instilled by un-affordability resulting in lack of required thermal comfort conditions. The present work aims to design an affordable, adaptable and modular façade system to retrofit existing building f...

Time series analysis and novelty detection are effective and promising methods for data-driven structural health monitoring (SHM) based on the statistical pattern recognition paradigm. However, processing substantially large volumes of vibration measurements may represent a serious limitation, especially for long-term SHM programs of large-scale ci...

We propose a novel approach to structural health monitoring (SHM), aiming at the automatic identification of damage-sensitive features from data acquired through pervasive sensor systems. Damage detection and localization are formulated as classification problems, and tackled through fully convolutional networks (FCNs). A supervised training of the...

This issue of Proceedings gathers the papers presented at the 6th International Electronic [...]

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitat...

We propose a novel approach to Structural Health Monitoring (SHM), aiming at the automatic identification of damage-sensitive features from data acquired through pervasive sensor systems. Damage detection and localization are formulated as classification problems, and tackled through Fully Convolutional Networks (FCNs). A supervised training of the...

In this paper, a numerical and experimental study of the shock absorption properties of bike helmets is presented. Laboratory compression and tensile tests were carried out on samples of expanded polystyrene (EPS) and polycarbonate (PC), respectively constituting the internal shock absorption layer and the external hard shell of composite helmets....

The effects of temperature and environmental moisture on the viscoelastic behavior of polyurethane foams were investigated both theoretically and experimentally. It was shown that the effect of the environmental parameters can be explained in terms of a variation of the free volume of the solid fraction of the foams, thus allowing the use of the su...

Globally, buildings are responsible for a significant share in energy consumption and greenhouse gas emissions profiles. Various attempts are undertaken to increase the energy efficiency of buildings and reduce their environmental impact. In semi-continental climate conditions with very hot summers and extremely cold winters, buildings should be ca...

Data-driven damage localization is an important step of vibration-based structural health monitoring. Statistical pattern recognition based on the prominent steps of feature extraction and statistical decision-making provides an effective and efficient framework for structural health monitoring. However, these steps may become time-consuming or com...

Recent advances in sensor technologies coupled with the development of machine/deep learning strategies are opening new frontiers in Structural Health Monitoring (SHM). Dealing with structural vibrations recorded with pervasive sensor networks, SHM aims at extracting meaningful damage-sensitive features from the data, shaped as multivariate time se...

Pattern recognition can be adopted for structural health monitoring (SHM) based on statistical characteristics extracted from raw vibration data. Structural condition assessment is an important step of SHM, since changes in the relevant properties may adversely affect the behavior of any structure. It looks therefore necessary to adopt efficient an...

Deep Learning strategies recently emerged as powerful tools for the characterization of heterogeneous materials. In this work, we discuss an approach for the characterization of the mechanical response of polysilicon films that typically constitute the movable structures of micro-electro-mechanical systems (MEMS). A dataset of microstructures is di...

Monte Carlo analyses on statistical volume elements allow quantifying the effect of polycrystalline morphology, in terms of grain topology and orientation, on the scattering of the elastic properties of polysilicon springs. The results are synthesized through statistical (lognormal) distributions depending on grain size and morphology: such statist...

Resonance features of slender mechanical parts of Lorentz force MEMS magnetometers are affected by the (weakly) coupled thermo-electro-magneto-mechanical multi-physics governing their dynamics. We recently showed that reduced-order models for such parts can be written in the form of the Duffing equation, whose nonlinear term stems from the mechanic...

The development of new compliant resonant microsystems and the trend towards further miniaturization have recently raised the issue of the accuracy and reliability of computational tools for the estimation of fluid damping. Focusing on electrostatically actuated torsional micro-mirrors, a major dissipation contribution is linked to the constrained...

This issue of Proceedings gathers the papers presented [...]