Experimental evaluation of the adhesion of a FRCM-tuff strengthening system

Abstract Nowadays, the use of innovative materials for the reinforcement of existing buildings are the most used technological solutions. Several reinforcement systems are available currently on the market and different research groups dealt with them from experimental point of view; these systems differ both in the reinforcing fibers used and the type of matrix applied. The most common reinforcement systems are those based on polymer matrix (FRP) provided by criteria and design rules consolidated in the application field for both new and existing buildings. In recent years scientifically-based cement matrix reinforcement systems (FRCM) are been used and experimented in the field of existing constructions. Unfortunately, there are currently no guidelines for qualification, as well as design criteria and application rules. It is a completely different reinforcement system compared to the common FRP reinforcements, in fact the cement matrix has a different mechanical behavior when applied to masonry supports. The mechanical behavior, already investigated by numerous authors, highlights the advantages that can be obtained with respect to a traditional reinforcement system. The aspect that still needs to be analyzed and studied is the adhesion between the existing support and the FRCM reinforcement system. In the present work, the attention is focused on the adhesion of a FRCM-tuff reinforcement system; for this purpose, experimental tests were carried out at the Materials and Structural Testing Laboratory of the Civil Engineering Department of the University of Calabria. The specimens consist of blocks of tuff, as regards the support, while the applied FRCM reinforcement system is based on basalt fibers and cement matrix. All results were compared with those obtained from previous research using other support materials and reinforcing fibers

experimental and analytical analysis on a masonry arch strengthened with basalt frcm

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Preliminary study on a novel Optimal Placed Sensors method based on Genetic Algorithm

The safeguarding of the historical and cultural heritage is one of the main research topics that has been addressed in recent years. Particular attention was given to the development of structural health monitoring systems that allowed the real time acquisition of different physical quantities that are stored in a cloud and compared with the health limit values of the structures obtained from numerical analysis previously carried out. One of the major problems highlighted by the use of these systems is related to the position and quantity of smart sensors to be used within the structure to be monitored. To avoid this, in this paper an Optimal Sensors Placement method was applied to a case study located in China. In particular, the positioning of the sensors was identified through an optimization workflow that adopt a Multi Objective Optimization engine called "Octopus"in Grasshopper3D. The identified optimal solutions have made it possible to detect the areas of the structure that will be subject to collapse during a seismic event

Simulation and Fast vulnerability analysis of a Chinese masonry pagoda

As an important historical relic of human being, masonry pagoda is the great significance in the eastern and western architectural cultures. Most of the existing masonry pagodas in China which have been seriously damaged urgently need detailed structural safety assessment, repair and reinforcement. The paper choose a Chinese masonry pagoda as a case, conducted a series simulation analysis with Abauqs. Through numerical simulation, the seismic performance of the pagoda can be evaluated, which can not only predict the hidden danger and weak link in its structure, but also provide useful reference for the reinforcement and repair of the pagoda. It also adopts a very convenient 3D CAD method to quickly assess the seismic vulnerability of existing masonry pagoda according the reference

Microstructural MRI basis of the cognitive functions in patients with Spinocerebellar ataxia type 2

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease involving the cerebellum. The particular atrophy pattern results in some typical clinical features mainly including motor deficits. In addition, the presence of cognitive impairments, involving language, visuospatial and executive functions, has been also shown in SCA2 patients and it is now widely accepted as a feature of the disease. The aim of the study is to investigate the microstructural patterns and the anatomo-functional substrate that could account for the cognitive symptomatology observed in SCA2 patients. In the present study, diffusion tensor imaging (DTI) based-tractography was performed to map the main cerebellar white matter (WM) bundles, such as Middle and Superior Cerebellar Peduncles, connecting cerebellum with higher order cerebral regions. Damage-related diffusivity measures were used to determine the pattern of pathological changes of cerebellar WM microstructure in patients affected by SCA2 and correlated with the patients' cognitive scores. Our results provide the first evidence that WM diffusivity is altered in the presence of the cerebellar cortical degeneration associated with SCA2 thus resulting in a cerebello-cerebral dysregulation that may account for the specificity of cognitive symptomatology observed in patients

Neural substrates of motor and cognitive dysfunctions in SCA2 patients: a network based statistics analysis

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease characterized by a progressive cerebellar syndrome, which can be isolated or associated with extracerebellar signs. It has been shown that patients affected by SCA2 present also cognitive impairments and psychiatric symptoms. The cerebellum is known to modulate cortical activity and to contribute to distinct functional networks related to higher-level functions beyond motor control. It is therefore conceivable that one or more networks, rather than isolated regions, may be dysfunctional in cerebellar degenerative diseases and that an abnormal connectivity within specific cerebello-cortical regions might explain the widespread deficits typically observed in patients. In the present study, the network-based statistics (NBS) approach was used to assess differences in functional connectivity between specific cerebellar and erebral “nodes” in SCA2 patients. Altered inter-nodal connectivity was found between more posterior regions in the cerebellum and regions in the cerebral cortex clearly related to cognition and emotion. Furthermore, more anterior cerebellar lobules showed altered inter-nodal connectivity with motor and somatosensory cerebral regions. The present data suggest that in SCA2 a cerebellar dysfunction affects long-distance cerebral regions and that the clinical symptoms may be specifically related with connectivity changes between motor and non-motor cerebello-cortical nodes

Improving accuracy on wave height estimation through machine learning techniques

Estimatabion of wave agitation plays a key role in predicting natural disasters, path optimization and secure harbor operation. The Spanish agency Puertos del Estado (PdE) has several oceanographic measure networks equipped with sensors for different physical variables, and manages forecast systems involving numerical models. In recent years, there is a growing interest in wave parameter estimation by using machine learning models due to the large amount of oceanographic data available for training, as well as its proven efficacy in estimating physical variables. In this study, we propose to use machine learning techniques to improve the accuracy of the current forecast system of PdE. We have focused on four physical wave variables: spectral significant height, mean spectral period, peak period and mean direction of origin. Two different machine learning models have been explored: multilayer perceptron and gradient boosting decision trees, as well as ensemble methods that combine both models. These models reduce the error of the predictions of the numerical model by 36% on average, demonstrating the potential gains of combining machine learning and numerical models

Functional changes of mentalizing network in SCA2 patients: novel insights into understanding the social cerebellum

In recent years, increasing evidence of the cerebellar role in social cognition has emerged. The cerebellum has been shown to modulate cortical activity of social brain regions serving as a regulator of function-specific mentalizing and mirroring processes. In particular, a mentalizing area in the posterior cerebellum, specifically Crus II, is preferentially recruited for more complex and abstract forms of social processing, together with mentalizing cerebral areas including the dorsal medial prefrontal cortex (dmPFC), the temporo-parietal junction (TPJ), and the precuneus. In the present study, the network-based statistics approach was used to assess functional connectivity (FC) differences within this mentalizing cerebello-cerebral network associated with a specific cerebellar damage. To this aim, patients affected by spinocerebellar ataxia type 2 (SCA2), a neurodegenerative disease specifically affecting regions of the cerebellar cortex, and age-matched healthy subjects have been enrolled. The dmPFC, left and right TPJ, the precuneus, and the cerebellar Crus II were used as regions of interest to construct the mentalizing network to be analyzed and evaluate pairwise functional relations between them. When compared with controls, SCA2 patients showed altered internodal connectivity between dmPFC, left (L-) and right (R-) TPJ, and right posterior cerebellar Crus II.The present results indicate that FC changes affect a function-specific mentalizing network in patients affected by cerebellar damage. In particular, they allow to better clarify functional alteration mechanisms driven by the cerebellar damage associated with SCA2 suggesting that selective cortico-cerebellar functional disconnections may underlie patients' social impairment in domain-specific complex and abstract forms of social functioning

Experimental and Numerical Analyses on Sandstone Elements Obtained by 3D Printing

The international cultural and historical heritage is often subject to degradation and damage. The main causes contributing to these phenomena are the chemical and mechanical actions due to acid rain, environmental pollution, and earthquakes. Other causes are the cycles of freezing and thawing that induce the manifestation of internal stresses leading to the deterioration of the material and the collapse of structural parts. In the field of architectural restoration, this problem has been addressed by two main solutions. The first involves cleaning processes that leave the missing parts visible; the second consists of introducing reproductions of the missing parts, creating a clear distinction between pre-existing and new elements. In both cases, the seismic behavior of the structure is modified; in the second solution, the added elements do not contribute to the structural strength since they are made of plaster or stucco. This work aims at presenting a preliminary study on the creation of replacements of missing elements within damaged heritage buildings. The work is structured in two distinct phases. In the first phase, specific cubic specimens, created with a 3D printer, are produced and subjected to uniaxial compression tests. The experimental campaign is carried out in order to provide useful information regarding the 3D material engineering constants that are currently absent in the literature. In the second phase, the experimental results are used in a numerical model to calibrate the mechanical properties of an equivalent homogeneous material