**3. Application and Validation**

The procedure set on DataBAES has been applied to about 30 buildings for a total of about 100 case studies, i.e., AA examined in terms of vulnerability and damage (level II) in relation to their bearing SE. Figure 7 shows how the analysed buildings appear on the main page of the web archive. Visualisation of the quick results of the present damage and vulnerability conditions for both AA and SE is possible (Figure 8), thus providing a comparison at a glance among case studies and the possibility of ranking them in order to manage an emergency or in order to identify a priority in intervention actions.


**Figure 7.** Screenshot from DataBAES archive listing buildings analysed in terms of both artistic assets (AA) and structural elements (SE).


**Figure 8.** View of the summary of results of case studies for analysed buildings.

Furthermore, for each building, additional data for the evaluation of damage and vulnerability conditions for each of the identified case studies is summarised and easy to consult (see example in Figures 9 and 10).


**Figure 9.** Church of S. Silvestro (L'Aquila, Italy): general data and identification of the case studies of the building.


**Figure 10.** Church of S. Silvestro (L'Aquila, Italy): damage and vulnerability data for each AA and SE pairs.

This systematic approach provided a general view of combined information between AA and SE (the detailed data of which are included in the level II survey form) for rapid comparative evaluations, but also allowed for two developments in the research, as specified in the following. The data collected from selected case studies have been used to structure a hierarchical approach and identify key factors to predict the prioritization of interventions (this analysis is described in [35]). Furthermore, the collected data have been analysed in terms of the frequency of parameters, which occurred in similar contexts (e.g., the type of building and the main structural bearing material), in order to find possible trends and correlation curves. This analysis is described in the following.

### *Damage Correlation between Artworks and Structural Macro-Elements*

The study focused on eight Italian masonry buildings struck by earthquakes, which occurred in 2009 in the Abruzzo region (with a Richter scale magnitude of M = 5.8) and in 2012 in Emilia-Romagna and Lombardia (M = 5.9). The basic structural material of those buildings is typical of buildings in mountainous (Abruzzo) and level areas (Emilia-Romagna and Lombardia) where there is prevalence of stone or clay brick masonries, respectively. A group of multidisciplinary experts surveyed both AA and the connected SE, so that a list of case studies (as defined by the DataBAES archive) was identified. Mural paintings (including frescoes) and stuccoes were found as AA in the buildings; therefore, the following analysis refers to this type of artwork. Artistic assets were inspected to detect a series of typical surface alterations (i.e., lack, *lacunae*, detachment, and cracking) that can be influenced by structural damage. For structural macro-elements, the out-of-plane and in-plane mechanisms of the walls, as well as the damage to the columns and vaults, were taken into consideration. Table 2 lists the mechanisms detected for the selected buildings according to the cataloguing of the II level survey form (see also Figure 3).


**Table 2.** Identification of macro-element mechanisms involving artworks in analysed buildings.

The combination of the case studies representing mural paintings (30 case studies) and stuccoes (20 case studies) in the buildings allowed preliminary elaborations of data regarding the frequency of the occurrence of the deterioration of AA in relation to the mechanisms of their supporting SE. The majority of case studies detected in the buildings referred to the in-plane damage of the walls, for either mural paintings or stuccoes, followed by the damage of the vaults and the out-of-plane mechanisms of walls, respectively, for mural paintings and stuccoes. Figure 11 shows the results obtained for the artworks detected in the buildings. Most deterioration for AA refers to cracking and detachment, either for the cases of mural paintings or stuccoes. Especially in mural paintings, the *lacunae* are not connected to out-of-plane mechanisms, as they do not involve substrate layers;

on the contrary, as expected, *lacunae* are more frequent in the case of in-plane mechanisms. Lack and detachment particularly affect the stuccoes, due to loss of the material of their typical slender protruding portions.

**Figure 11.** Frequency of the occurrence of the deterioration types of AA combined with mechanisms of SE detected for mural paintings and stuccoes.

Figure 12 shows the evaluation of average damage according to the indications of the II level survey form, performed for both mural paintings and stuccoes, as well as an overall judgment encompassing the condition of the structural macro-element (i.e., wall, vault, or column). The highest values of average damage (higher than 2) mainly refer to the out-of-plane mechanisms of walls for stuccoes, and the damage of the vaults, followed by the in-plane damage of the walls, for mural paintings.

**Figure 12.** Average damage levels of AA combined with mechanisms of SE identified for mural paintings and stuccoes.

The analysis of the levels of overall damage associated with the macro-element supporting each artistic asset provided the distributions given in Figure 13. The results refer to the overall damage of both mural paintings and stuccoes and are based on the assumption that the damage of the AA (d1, d2, ... , d5) is induced by that of the SE (D1, D2, ... , D5) [24]. In general, the increase of damage in AA corresponds to an increasing damage in the supporting SE, with significant distribution at D2–D3 levels, i.e., from moderate (D2) to substantial to heavy damage (D3).

**Figure 13.** Distribution of the damage levels of AA versus each degree of SE damage.

The same trend is expressed by the curves in Figure 14, which better clarify the strong correlation between the damage of AA and SE.

**Figure 14.** Joint distribution of damage levels between AA and SE for both mural paintings and stuccoes.

### **4. Conclusions**

Artistic assets are commonly an important component in the evaluation of masonry buildings belonging to historic city centres for defining safety measures after the occurrence of an earthquake. However, these safety actions are often evaluated with the risk connected to the bearing components not being taken into account. In reality, the preservation of artistic heritage strongly depends on the mechanical behaviour of the building structure, the knowledge of which, together with collectable data on the joined assets, can provide useful information for possible conservation plans. A new web archive, called DataBAES, which aims to collect damage and vulnerability data of unmovable artworks (frescoes, stuccoes, mosaics) for priority ranking evaluations has been proposed. It is based on survey forms focused on two increasing levels of direct visual inspections applicable to AA that are integral to their bearing SE.

The I level form, called "Evaluation and correlation of damage of unmovable artistic assets and structural elements", allows for an expeditious survey of the damaged condition of artworks and related architectural elements after an earthquake occurs. At level I, the objectives of the visual inspection are as follows: helping the data collection in the post-emergency phases, qualifying the current damage level of artistic assets taking into consideration the behaviour of the supporting structural macro-elements, and allowing for potential safety measures. At such a level, possible significant vulnerability aspects (for both AA and SE) are simply identified, and do not contribute to the final judgment. A deeper evaluation of both damage and vulnerability aspects is allowed at level II, by means of the form "Evaluation and correlation of damage and vulnerability of unmovable artistic

assets and structural elements". It completes the investigation by detailing their mutual description, with reference to 50 possible mechanisms of macro-elements representing SE, as well as 25 potential deterioration indicators for AA. For both levels, the proposed approach provides a judgment based on a 1 to 5 scale, which shows the severity of increasing damage/vulnerability.

The procedure has been implemented on the web archive platform DataBAES, which provides a comprehensive view of both single and multiple case studies detected in different buildings. Hence, this approach can be applied for ranking artistic assets in a building, as well as whole buildings belonging to a historic city centre, according to the results obtained for their related assets, in order to plan preventive measures and the management of CH to a greater extent. Further developments in DataBAES could focus on the evaluation of structural interventions applied to buildings after previous earthquakes in order to highlight possible negative effects on the mechanical behaviour and, consequently, on the integrity of their joined artworks.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2071-1050/12/2/653/s1, DataBAES I level survey form: "Evaluation and correlation of damage of unmovable artistic assets and structural elements", DataBAES II level survey form: "Evaluation and correlation of damage and vulnerability of unmovable artistic assets and structural elements".

**Author Contributions:** Conceptualization, M.R.V.; Data curation, M.R.V., S.C. and G.G.; Funding acquisition, M.R.V.; Methodology, M.R.V. and S.C.; Software, G.G.; Supervision, M.R.V.; Validation, M.R.V.; Writing—original draft, M.R.V.; Writing—review & editing, M.R.V., S.C. and G.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research was funded by ProVaCi (Technologies for the Seismic Protection and Valorization of Cultural Heritage, Italy, 2011–2015).

**Acknowledgments:** The authors wish to acknowledge F. Vanin, A. Rinaldin, M. Munari, for their collaboration in collecting and processing data, and S. Ulizio for her contribution to the technical evaluation of the risk parameters. M. Giaretton and H. Branch are also acknowledged for their help with the translation phase of the survey forms and website.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


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