Long term discharge simulation through a geomorphological model

Flow duration curve estimation must be performed on the basis of continuous rainfall-runoff simulations. In ungauged basins, a under-parameterised model is needed to reduce the uncertainty of the results. In this paper a geomorphological model based on a width function (WFIUH) was used to simulate low flows in a mean-sized basin in Central Italy. The WFIUH model [9]introduces a new approachto the curvenumber method and was used to evaluate the stream-flow for hourly event representation. The aim of this work is to evaluate the behaviour of the WFIUH model for long term simulation and then to compare the standard curve number approach to the curve number method implemented in the WFIUH model. To predict the behaviour of catchments for a long term, to know the response of catchments in different seasons or in different years, it is necessary to improve the model and to identify a new method for calculating base-flow. To obtain these results, it is necessary to separate base-flow and stream-flow, simulate the two contributions and build a unique series of values that reproduces the answer of the basin to different rainfalls during the year to estimate the low flow during a dry period. The model can also be used in ungauged basins because a unique parameter is used. © 2013 AIP Publishing LLC

On the use of domain adaptation techniques for bridge damage detection in a changing environment

Structural Health Monitoring of civil infrastructures often suffers from the limited availability of damage labelled data. The work here seeks to overcome this issue by using Transfer Learning approaches, in the form of Domain Adaptation, for leveraging information from a source structure with determined health-state labels to make inferences on an unlabeled monitored structure. The idea is to exploit source data to train a Machine Learning algorithm and achieve improved early-stage damage detection capabilities across a population of bridges. To account for differences in the underlying distributions of each structure, Transfer Learning is seen as a strategy enabling population-level bridge SHM. In this paper, the natural frequencies obtained from multiple vibration measurements are extracted to characterise different domains during pristine and abnormal conditions. Such damage-sensitive features are aligned via Domain Adaptation and used to train a standard classifier within a shared feature space. The methodology is validated on the heterogeneous population composed of the Z24 and S101 bridges. The results prove the effectiveness to successfully exchange damage labels, thus increasing available information for health-state inference for SHM applications with sparce datasets

An indoor air quality study at the Ambrosiana Art Gallery (Milan)

Indoor air quality in historical buildings and museums is receiving increasing concern nowadays among the scientific community. Many sources of pollutants, both gases and particles, are responsible for the accelerated decay of the works of art. Knowing the levels of indoor pollutants is of critical importance to apply conservation and preservation strategies of cultural heritage [1, 2]. Air quality at the Ambrosiana Art Gallery in Milan has been monitored (in the two periods October-November 2017 and March 2018) inside the room where the preparatory cardboard of the School of Athens (1509-1511), one of most important masterpieces by Raffaello Sanzio, is stored. The cardboard is currently undergoing restoration and will be exposed in a new showcase that will be realized according to the specific environmental and microclimatic conditions of the room. The objective of this study was to monitor the concentration and chemical composition of the aerosol particulate matter (PM) up to the nano fraction, which represents the most dangerous fraction for the works of art surfaces. The monitoring campaigns have been carried out in parallel in the Raffaello room (at present not accessible to visitors) and in a nearby room open to visitors. The set of instuments employed included: a TSP sampler (total suspended particles) (Tecora, Pollution Check, Bravo M2); a DustMonit (Contec) analyzer that measures the concentration of dust up to PM1 and provides 13 granulometric classes (up to 300 nm); a NanoScan Nanoparticle sizer 3910 (TSI) instrument that measures particles concentration up to 10 nm; two instruments for the determination of black carbon (BC) in continuous and in particular a SILIIS instrument (Sphere-Integrated Laser Induced Incandescence Spectroscopy) and a micro aethalometer (AE51 Magee Scientific). Quartz fiber filters have been employed to collect TSP samples to be submitted to chemical analysis. The filters have been fully chemically characterized: main ionic constituents and the carbonaceous fraction (i.e organic carbon, OC and elemental carbon, EC) have been analyzed by IC (ion chromatography) and TOT (thermal optical transmittance) respectively. A particles morphological characterization has been carried out on PM collected on polycarbonate filters by means of SEM-EDX (scanning electron microscopy coupled with energy dispersive X-ray spectroscopy). Outdoor PM concentrations, obtained for the two seasons (autumn 2017 and spring 2018) from ARPA monitoring stations placed in the city center, have been correlated with indoor data

An application of domain adaptation for population-based structural health monitoring

In the field of civil infrastructure, Structural Health Monitoring generally suffers from a scarcity of labelled damage-state data. To solve this issue, this work adopts a Transfer Learning approach for leveraging information from a source structure, characterised by a rich class of damage labels, to improve inferences on a target structure with limited knowledge. The goal is to train a machine learning algorithm on a bridge undergoing damage and to afterwards transfer the available labelled damage-state data across the members of the investigated population. Given possible differences exhibited by each structure, a domain adaptation technique in the field of statistic alignment, called Normal Condition Alignment (NCA), is applied to match different distributions in a shared feature space. The methodology is validated on a heterogeneous population composed of two numerical bridges of different geometry and materials, representing the Z24 and the S101 benchmark bridges. Finite Element Models are built to simulate healthy conditions and several damage cases. The natural frequencies describing such scenarios are considered as damage-sensitive features and thus employed to characterise the two domains and fed to a supervised learning-based classifier. The presented approach is deemed effective to provide mappings that allow the exchange of health-state information from source to target datasets, becoming a promising approach to be applied within a population of real bridges

The material soul: Strategies for naturalising the soul in an early modern epicurean context

We usually portray the early modern period as one characterised by the ‘birth of subjectivity’ with Luther and Descartes as two alternate representatives of this radical break with the past, each ushering in the new era in which ‘I’ am the locus of judgements about the world. A sub-narrative called ‘the mind-body problem’ recounts how Cartesian dualism, responding to the new promise of a mechanistic science of nature, “split off” the world of the soul/mind/self from the world of extended, physical substance—a split which has preoccupied the philosophy of mind up until the present day. We would like to call attention to a different constellation of texts—neither a robust ‘tradition’ nor an isolated ‘episode’, somewhere in between—which have in common their indebtedness to, and promotion of an embodied, Epicurean approach to the soul. These texts follow the evocative hint given in Lucretius’ De rerum natura that ‘the soul is to the body as scent is to incense’ (in an anonymous early modern French version). They neither assert the autonomy of the soul, nor the dualism of body and soul, nor again a sheer physicalism in which ‘intentional’ properties are reduced to the basic properties of matter. Rather, to borrow the title of one of these treatises (L’Âme Matérielle), they seek to articulate the concept of a material soul. We reconstruct the intellectual development of a corporeal, mortal and ultimately material soul, in between medicine, natural philosophy and metaphysics, including discussions of Malebranche and Willis, but focusing primarily on texts including the 1675 Discours anatomiques by the Epicurean physician Guillaume Lamy; the anonymous manuscript from circa 1725 entitled L’Âme Matérielle, which is essentially a compendium of texts from the later seventeenth century (Malebranche, Bayle) along with excerpts from Lucretius; and materialist writings such Julien Offray de La Mettrie’s L’Homme-Machine (1748), in order to articulate this concept of a ‘material soul’ with its implications for notions of embodiment, materialism and selfhood