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Model updating procedures based on experimental data are commonly used in case of historic buildings to identify numerical models that are subsequently employed to assess their structural behaviour. The reliability of these models is closely related to their ability to account for all the uncertainties that are involved in the knowledge process. In this regard, to handle these uncertainties and quantify their propagation, Bayesian inference is frequently employed being able to deal with the effects of parameter uncertainty, observation errors and model inadequacy. The computation of the posterior distribution through Bayesian inference needs–however–the evaluation of the likelihood function, which requires solving complex multi-dimensional integration problems. To bridge this shortcoming, the paper compares two Bayesian inference approaches to show how different approximations affect the results of simulated inference: a discrete approach for the likelihood computation in the Bayesian Model Updating (BMU) and a Monte Carlo likelihood-free method known as Approximate Bayesian Computation (ABC) are reported. As reference, the typology of historic masonry towers was considered by using their natural frequencies as experimental data for model updating. The two procedures provide very similar results supporting the validity of both methods despite ABC turns out to be a more flexible approach.

The application of wireless sensor network technology for long-term monitoring of a historic tunnel-like masonry structure in Italy is described. The monitoring was performed within the activity of a research project aimed at providing a general framework to check the structural health of heritage structures and detect, in real time, any potential damage that may compromise their safety. The selected case study (a tunnel-like masonry structure) represented a challenging experiment, both in terms of the type of structure itself and the technical solution employed for installing the monitoring system. After a brief description of the installed monitoring system and the design of the sensor layout, the lessons learned after 1 year of monitoring are summarised.

This paper reports on a pilot project for long-term structural health monitoring of historical city gates. This structural typology is what today remains of the defensive structures that characterised the medieval centres of many European cities. Even though in the last years the scientific community has paid great attention to the structural analysis and on the structural health monitoring of masonry towers, which are apparently similar to the typology of city gates, only a few studies explicitly approach such heritage buildings. In most cases, the surviving gates are immersed in the daily vehicular-traffic flows and hence continuously subjected to traffic-induced vibration. Consequently, a key point both for their conservation is the assessment of the optimal sensors position to be used for reliable structural health monitoring procedures. These issues are here discussed with reference to a specific case: the San Niccolò gate in Florence (Italy). First, an expeditious experimental campaign performed with a reduced number of accelerometers is reported. This test is aimed to identify a numerical model that is subsequently employed to design the optimal sensor position of long-term structural monitoring. This optimal sensor grid must be assessed to ensure a low-cost and sustainable dynamic monitoring system but, at the same time, to maximize the information contents.

In this paper the actual dynamic behavior of the civic Clock tower of Rotella, a little village in central Italy heavily damaged by the recent 2016 seismic sequence, is thoroughly investigated by means of a detailed numerical model built and calibrated using the experimental modal properties obtained through Ambient Vibration Tests. The goal is to update the uncertain parameters of two behavioral material models applied to the Finite Element Model (elastic moduli, mass densities, constraints, and boundary conditions) to minimize the discrepancy between experimental and numerical dynamic features. A sensitivity analysis was performed with the definition of a metamodel to reduce the computational strain and try to define the necessary parameters to use for the calibration process. Due to the high nonlinear dependency of the objective function of this optimization problem on the parameters, and the likely possibility to get trapped in local minima, a machine learning approach was meant. A fully automated Finite Element Model updating procedure based on genetic algorithms and global optimization is used, leading to tower uncertain parameters identification. The results allowed to create a reference numerical replica of the structure in its actual health state and to assess its dynamic performances allowing better control over their future evolution.

The toxicity of fibrous minerals is usually evaluated only in the case of biopersistent minerals because they can remain in the lungs or in other biologic environments for a long time causing several illnesses. Very scarce knowledge exists, at the date, on the accurate chemical composition and the effect of particulates and fibres with high solubility (in water and biological environments). To reduce this lack, natural fibrous epsomite from Perticara Mine (Central Italy) was investigated through SEM-EDS, XRPD, ICP-AES and alpha spectrometry measurements. The morphological and morphometrical investigations on the epsomite sample highlight the presence of a significant number of small fibres potentially inhalable for humans, with an equivalent aerodynamic diameter (Dae) value of 5.09 μm. Moreover, toxic elements (As, Co, Fe, Mn, Ni, Sr, Ti, Zn) and radioactive isotopes (210Po and 228Th) were detected in the epsomite fibres by chemical analysis. In particular, a surprisingly high amount of of 210Po (5.59 Bq/g) was detected in the investigated epsomite sample. The first results of this study were recently published by Giordani et al. (2022). Due to the high solubility of epsomite at lung conditions (37 °C and 100% relative humidity; Chipera and Vaniman, 2007), the inhaled fibres rapidly became a solution and can be potentially adsorbed from all parts of the respiratory tract. Consequently, the entire cargo of hazardous elements could be quickly released into the lung environment and thus affect human health. Natural epsomite is a widespread mineral (e.g., in mines, geological outcrops, mineral springs, efflorescence) and has several applications (Ruiz-Agudo et al., 2008).Our findings suggest great caution in handling epsomite samples, and our work can be considered a representative case study to investigate the interaction between soluble minerals and human health. These preliminary results can be the basis for further studies on the content of hazardous elements in building materials and regarding toxic elements interaction with humans. Giordani, M., Meli, M. A., Roselli, C., Betti, M., Peruzzi, F., Taussi, M., Valentini, L., Fagiolino, I. and Mattioli, M. 2022. Could soluble minerals be hazardous to human health? Evidence from fibrous epsomite. Environmental Research, 206, 112579. https://doi.org/10.1016/j.envres.2021.112579Chipera, S.J., Vaniman, D.T., 2007. Experimental stability of magnesium sulfate hydrates that may be present on Mars. Geochimica et Cosmochimica Acta 71, 1, 241-250. https://doi.org/10.1016/j.gca.2006.07.044.Ruiz-Agudo, E., Putnis, C.V., Rodriguez-Navarro, C., 2008. Interaction between epsomite crystals and organic additives. Crystal Growth and Design, 8(8), 2665-2673. https://doi.org/10.1021/c

This study explored the morphological, mineralogical, and physico-chemical features of carcinogenic erionite and other possibly hazardous zeolites, such as mesolite and thomsonite, while also investigating the interacting capability of the mineral surface at the liquid/solid interface. Extremely fibrous erionite is K+ and Ca2+-rich and shows the highest Si/Al ratio (3.38) and specific surface area (8.14 m2/g). Fibrous mesolite is Na+ and Ca2+-rich and displays both a lower Si/Al ratio (1.56) and a smaller specific surface area (1.56 m2/g). The thomsonite composition shows the lowest values of Si/Al ratio (1.23) and specific surface area (0.38 m2/g). Electron paramagnetic resonance data from selected spin probes reveal that erionite has a homogeneous site distribution and interacts well with all spin probes. The surfaces of mesolite and thomsonite are less homogeneous and closer polar sites were found through consequent interaction with the probes. The mesolite surface can also clearly interact but with a lower strength and may represent a potential health hazard for humans, though with a lower degree if compared to erionite. The thomsonite surface is not inert and interacts with the probes with a low-grade capability. We can expect small fragments of thomsonite to interact with the biological environment, though with a low-grade intensity.

From a toxicological point of view, particulates and fibres with high solubility in water and/or in biological environments have not been considered in detail and the knowledge to date in this area is very scarce. In this study, the water-soluble natural epsomite fibres from Perticara Mine (Italy) were investigated using SEM-EDS, XRPD, ICP-AES and alpha spectrometry measurements which were combined and integrated to characterise the fibres' morphology, crystal chemistry and mineralogy. The morphological and morphometric results showed that most of the fibres are of inhalable size (D

Creating three-dimensional as-built models from point clouds is still a challenging task in the Cultural Heritage environment. Nowadays, performing such task typically requires the quite time-consuming manual intervention of an expert operator, in particular to deal with the complexities and peculiarities of heritage buildings. Motivated by these considerations, the development of automatic or semi-automatic tools to ease the completion of such task has recently became a very hot topic in the research community. Among the tools that can be considered to such aim, the use of deep learning methods for the semantic segmentation and classification of 2D and 3D data seems to be one of the most promising approaches. Indeed, these kinds of methods have already been successfully applied in several applications enabling scene understanding and comprehension, and, in particular, to ease the process of geometrical and informative model creation. Nevertheless, their use in the specific case of heritage buildings is still quite limited, and the already published results not completely satisfactory. The quite limited availability of dedicated benchmarks for the considered task in the heritage context can also be one of the factors for the not so satisfying results in the literature.Hence, this paper aims at partially reducing the issues related to the limited availability of benchmarks in the heritage context by presenting a new dataset for semantic segmentation of heritage buildings. The dataset is composed by both images and point clouds of the considered buildings, in order to enable the implementation, validation and comparison of both point-based and multiview-based semantic segmentation approaches. Ground truth segmentation is provided, for both the images and point clouds related to each building, according to the class definition used in the ARCHdataset, hence potentially enabling also the integration and comparison of the results obtained on such dataset.

The scientific community is hardly working to propose reliable methodologies of analysis and non-invasive technologies of investigation to assess the current state of conservation of historic buildings to verify their ability to resist future threats. These structures, mostly made of masonry, are difficult to assess due to the heterogeneity of materials and their mechanical behavior, but it is vital to preserve this invaluable cultural heritage by suitable structural assessment techniques. A great deal of research attention has been paid to monitoring their structural health; in many recent publications new advanced technological methods have been provided such as cheaper sensors, wireless connections, non-contact surveys and continuous monitoring. A bibliometric study has shown that more than half of the papers on Structural Health Monitoring (SHM) and Nondestructive Testing (NDT) on masonry have been published between 2018 and 2020, and 30% of those published in 2020 were on ‘slender’ elements like towers, chimneys or minarets. This paper presents a wide-ranging review of static and dynamic studies published on SHM and NDT of slender masonry structures summarizing and discussing the different experimental techniques used. With respect to the dynamic testing, Operational Modal Analysis (OMA) by accelerometers is the mostly frequent used technique by scholars, but other promising methods such as radar interferometry are also reported. This overall discussion is concluded with a short review of some examples on numerical structural health assessment and signal processing tools. An inclusive list of papers is provided describing the most important slender masonry structures characteristics, natural frequencies, experimental and numerical techniques employed and reference values. This paper, set on a practical perspective, is expected to be of interest to those researchers and practitioners who require an extensive and up-to-date review of this topic.

The manuscript provides a wide database for historic masonry towers collecting their modal, mechanical and geometrical features. This large amount of data was collected according to a literature review, then summing up as many as 56 different case studies. The collected data was then used to identify the main physical parameters influencing the modal behaviour of slender masonry towers, including the openings on the facades. After a critical discussion of the data reported in the database, the existing empirical or semi-empirical formulations available for the estimation of the first natural frequency of such structural typology are first evaluated. Subsequently the effects of the openings, as revealed through the analysis of the experimental results reported in the database, are discussed by comparison with a simple but effective numerical model. Despite the difficulties to quantify the effect of the openings along the height of the tower on the main frequency due to a cross-contribution of mass and stiffness, a simple but effective formulation is proposed which is able to account for this parameter.

This study analyses the seismic risk of a confined historic masonry tower located in Italy (San Gimignano) critically discussing the multilevel assessment path proposed by the Italian “Guidelines for the assessment and mitigation of the seismic risk of the cultural heritage”. Specifically, these guidelines identify a methodological path which aims to coordinate the phases of investigation and drive the grade of resolution of the structural assessment, depending on different possible objectives. The paper compares the results obtained with the three different levels of evaluation employed to estimate the seismic risk of the tower: level LV1 (analysis at territorial scale), level LV2 (local analysis) and level LV3 (global analysis). The main variables considered are the mechanical properties of the masonry walls and the degree of constraint generated by the buildings surrounding the tower with specific attention to the latter variable. Numerical insights on the seismic behaviour of the tower are presented and discussed through a comparative approach. The obtained results show significant differences between the numerical models of the confined and the isolated tower in terms of both capacity curves and damage pattern and point out the needs of additional studies regarding the numerical assessment of the effects of the interaction between a tower and its adjacent lower buildings.

An emerging problem for human health is the exposure to non-regulated mineral fibers with an asbestos-like crystal habit, particularly fibrous zeolites. This study aimed to determine if and how selected fibrous zeolites (erionite, mesolite, and thomsonite) induce toxicity effects on two different in vitro cellular models, the adherent murine hippocampal (HT22) and human immortalized T lymphocyte (Jurkat) cell lines. Before proceeding with the cellular tests, the three zeolite samples were investigated using scanning electron microscopy–energy-dispersive spectroscopy and X-ray powder diffraction techniques. The cells were treated with 0.1 µM and 1 µM of fibrous erionite, mesolite, and thomsonite for 12, 24, and 48 h. Results showed a cytotoxic effect of erionite in both cellular models and revealed different toxic behaviors of the mesolite and thomsonite fibers, suggesting other potential mechanisms of action. The outcome of this study would be a first step for further research on fine biochemical interactions of zeolite fibers with cells and future in vivo investigations.

In recent years, the numerous advantages introduced by Building Information modelling (BIM) have led in its application on the heritage environment and giving birth to the concept of H-BIM (Heritage BIM). The resulting demand in heritage survey data processing has focused this research on the development of strategies and methods to improve the construction of three-dimensional and informative models starting from 3D point clouds. The implementation of an automated procedure is fundamental for easing and speeding up the survey data processing and one of the most challenging tasks to achieve this purpose is the problem of semantic segmentation. The research presented in this paper aims at testing already existing methods and exploring new strategies for 3D point cloud semantic segmentation on heritage scenarios focusing on deep learning and neural network techniques.

Proteins are essential to life, and the evaluation of their content, identification, and modification represents a fundamental assay in biochemistry research. Different analytical techniques and protocols have been specifically designed but have rarely been compared. Here, we test and compare a variety of methodologies and treatments for the quantification of proteins in Amphistegina lessonii, a larger symbiont-bearing benthic foraminiferal species. These analyses specifically include (a) lysis buffer (homemade vs. RIPA), (b) protein assays (Lowry, BCA, and Bradford), (c) ultrasonic bath treatment, and (d) protein staining (silver staining vs. Coomassie blue). On the basis of the comparative outcome, we suggest using the homemade lysis buffer, Lowry or BCA assays, ultrasonic bath treatment, and silver stain to maximize the extraction and characterization of protein for A. lessonii. This protocol might be suitable and extended to other benthic foraminiferal species, including the smaller ones.

This paper reports the results of a ground penetrating radar (GPR) survey of the ground-floor of Academia Gallery (Florence, Italy) where the Michelangelo’s David is exhibited to the public. The equipment used was a step-frequency GPR operating in the 100 MHz-1 GHz band, named ORFEUS. The survey covered an area of 13 m × 7.3 m, and the scans were performed along two orthogonal directions. Acquisitions in the same direction were separated by 0.25 m from each other. The GPR was able to confirm the underground structure, as it can be deducted by planimetry and historical documentation. In particular, the radar clearly detected the air-conditioning ducts under the floor and an approximately circular foundation below the basement of the statue.

In recent few decades, operational modal analysis (OMA), also known as output-only or ambient vibration test (AVT), has become a powerful tool for a wide range of applications in the field of civil engineering. The output-only modal test represents an effective alternative to forced vibration techniques when historical structures are investigated. This method takes advantages from natural sources of vibration (wind, traffic, etc.) instead of having to shake the structure artificially. This paper shows how the dynamic characteristics of a building (i.e. frequency, modal shapes and damping ratios) can be automatically identified by enhanced frequency domain decomposition (EFDD) technique. The assessment of the dynamic characteristics is performed analyzing the Baptistery of San Giovanni in Firenze (Italy) where a network of seismic sensors was temporarily installed. The modal parameters are used to calibrate a 3D finite element (FE) numerical model, by using a genetic algorithm (GA). This procedure allows to obtain accurate and robust model capable to reproduce the actual dynamic behavior of the structure by minimizing the uncertainties related to the mechanical parameters. This paper, providing an illustrative case study in the field of health monitoring of monumental structures, confirms that the OMA technique is able to derive effective information on the dynamic behavior of the analyzed building, which is useful to tune reliable and robust numerical models to be employed for structural analysis.

In order to estimate their behaviour as realistic as possible, masonry structures and historical complex buildings are frequently assessed with demanding non-linear procedures, whether for static or dynamic purposes. However, uncertainties related to the epistemic and aleatory field, especially for historical masonry, can widely influence the output reliability and diminish the value of the deterministic approach (one input – one output) in a decision making workflow: for instance, the necessity for urgent structural interventions or even the possibility to close the building or civil object for safety reasons. This paper aims to show how these uncertainties could be quantified, based on a multi-parameter input approach, and consequently handled. In sequence, (i) the knowledge, based on the state-of-the-art survey of the object (geometry, quality and characteristics of the materials, etc.) and scientific or engineering contents (constitutive laws, possible sources of excitation, solution methods, etc.) can be put inside a numerical model in order to find a solution for the desired output, (ii) input parameters could be treated as variables, so that the model space could be explored for different sets of values and (iii) a probabilistic model or distribution could be adopted for each parameter, based on previous known observations. The outputs of such a model will be of statistic nature and will have a probabilistic distribution as well: based on this data, produced by the numerical model (f.i. a finite element model), a relationship between inputs and outputs could be established in an approximated mathematical formulation, the so-called metamodel. In this context, the newly generated (meta)model has the following characteristics: (i) it has been validated as the correct representation of the original model, (ii) it is much less computationally demanding than the original model, (iii) it establishes a direct relationship between the input parameters and the output, (iv) it can be used both for input parameters calibration, based on new observations, and, possibly, for the risk assessment. Based on these considerations, the metamodel could be assumed as a fast and validated decision-making tool. An elucidating example is herein discussed with reference to a historic masonry tower.

The recent developments of micro-electro-mechanical systems and wireless sensor networks allow today the use of low-cost and small-size sensors for continuous monitoring of civil structures. Both these features are very important for the low impact of the sensor grid in heritage structures, ensuring a low-cost and sustainable dynamic monitoring system. Over the last 20 years the use of sensor networks for continuous monitoring has received a growing interest. Anyway, still numerous questions remain opened about the sensitivity of measurement devices, the optimization of number and positioning of sensors, the energy efficiency of the network, and the development of algorithms for real-time data analysis. This paper, based on the aforementioned motivations, discusses about a monitoring system made of micro-electro-mechanical sensors connected through a wireless network. The architecture of the wireless sensor network and the automatized procedure proposed for the continuous processing of the recorded signals are discussed and described with reference to an explicative masonry tower case study. It is believed that the proposed technologies can provide an economical and relatively non-invasive tool for real-time structural monitoring and that, moreover, the availability of large amounts of data from actual measurements can give effective information on the structural behaviour of historic constructions.

Masonry towers constitute a significant part of the European built heritage that, due to their intrinsic geometric slenderness, result particularly vulnerable to horizontal loads. Their seismic assessment requires a numerical model able to reproduce the material nonlinear behavior and to account for the different sources of uncertainties. Among the uncertainties which may affect the reliability of the results, it is possible to list: (i) the building history, its evolution and the structural details, (ii) the masonry construction technique and the material properties and (iii) the boundary condition since in several cases towers are embedded in other buildings (such as churches bell-towers). This paper, to assess the seismic risk of historic masonry towers, investigates the reliability of a probabilistic approach aimed to include some of the sources of uncertainty above mentioned. The employed probabilistic framework was originally introduced in 1991 by Gusella, and it is here discussed by analyzing a representative case study: an historic masonry tower in San Gimignano (Italy). In a first part of the paper the results of ambient vibration tests are employed to tune a numerical model of the tower. The identified numerical model is subsequently employed to perform the non-linear time history analyses that are needed to express the seismic risk of the tower in terms of collapse probability.

The new Italian building code, published in 2018 [MIT in NTC 2018: D.M. del Ministero delle Infrastrutture e dei trasporti del 17/01/2018. Aggiornamento delle Norme Tecniche per le Costruzioni (in Italian), 2018], explicitly refers to the Italian “Guidelines for the assessment and mitigation of the seismic risk of the cultural heritage” [PCM in DPCM 2011: Direttiva del Presidente del Consiglio dei Ministri per valutazione e riduzione del rischio sismico del patrimonio culturale con riferimento alle norme tecniche per le costruzioni, G.U. n. 47 (in Italian), 2011] as a reliable source of guidance that can be employed for the vulnerability assessment of heritage buildings under seismic loads. According to these guidelines, three evaluation levels are introduced to analyse and assess the seismic capacity of historic masonry structures, namely: (1) simplified global static analyses; (2) kinematic analyses based on local collapse mechanisms, (3) detailed global analyses. Because of the complexity and the large variety of existing masonry typologies, which makes it particularly problematic to adopt a unique procedure for all existing structures, the guidelines provide different simplified analysis approaches for different structural configurations, e.g. churches, palaces, towers. Among the existing typologies of masonry structures there considered, this work aims to deepen validity, effectiveness and scope of application of the Italian guidelines with respect to heritage masonry towers. The three evaluation levels proposed by the guidelines are here compared by discussing the seismic risk assessment of a representative masonry tower: the Cugnanesi tower located in San Gimignano (Italy). The results show that global failure modes due to local stress concentrations cannot be identified if only simplified static and kinematic analyses are performed. Detailed global analyses are in fact generally needed for a reliable prediction of the seismic performance of such structures.