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International Conference on Technologies and Innovation

CITI 2021: Technologies and Innovation pp 33–47Cite as

On Intelligent Fingerprinting of Antique Buildings from Clay Composition

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1460))

Abstract

Materials research in archaeological ceramic artifacts is a consolidated practice that helps architectural heritage preservation. Ancient buildings located within the historic centres, in particular, mark the image and history of each city at different periods, and when damaged historical masonry needs restoration actions, a good characterization of both new and old material is crucial to forecast the behaviour of the system. In this paper we consider 9 antique buildings and constructions of the Medieval city of Ferrara (NE Italy), and the geochemical characterization of samples from their bricks and terracottas. We apply an intelligent fingerprinting technique to major and trace elements, along with some of their ratios, with the purpose of identifying the smallest and more accurate subsets that allow to uniquely identify one building from all others. We obtain very encouraging results, with accuracies over 80% even with very small fingerprints.

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References

  1. Amorosi, A., Centineo, M., Dinelli, E., Lucchini, F., Tateo, F.: Geochemical and mineralogical variations as indicators of provenance changes in late quaternary deposits of SE Po plain. Sediment. Geol. 151(3–4), 273–292 (2002)

    Article  Google Scholar 

  2. Atkinson, P., Tatnall, A.: Introduction: neural networks in remote sensing. Int. J. Remote Sens. 4(18), 699–709 (1997)

    Article  Google Scholar 

  3. Azamathulla, H., Wu, F.: Support vector machine approach for longitudinal dispersion coefficients in natural streams. Appl. Soft Comput. 2(11), 2902–2905 (2011)

    Article  Google Scholar 

  4. Belkhiri, L., Mouni, L., Narany, T.S., Tiri, A.: Evaluation of potential health risk of heavy metals in groundwater using the integration of indicator kriging and multivariate statistical methods. Groundwater Sustain. Dev. 4, 12–22 (2017)

    Article  Google Scholar 

  5. Benedetto, C.D., Graziano, S., Guarino, V., Rispoli, C., Munzi, P., Morra, V., Cappelletti, P.: Romans’ established skills: mortars from d46b mausoleum, porta Mediana necropolis, CUMA (Naples). Mediterr. Archaeol. Archaeom. 18, 131–146 (2018)

    Google Scholar 

  6. Bianchini, G., Laviano, R., Lovo, S., Vaccaro, C.: Chemical-mineralogical characterisation of clay sediments around Ferrara (Italy): a tool for an environmental analysis. Appl. Clay Sci. 21(3–4), 165–176 (2002)

    Article  Google Scholar 

  7. Bianchini, G., Marrocchino, E., Moretti, A., Vaccaro, C.: Chemical-mineralogical characterization of historical bricks from Ferrara: an integrated bulk and micro-analytical approach. Geol. Soc. Lond. Special Publicat. 257(1), 127–140 (2006)

    Article  Google Scholar 

  8. Blo, G., Conato, C., Contado, C., Fagioli, F., Vaccaro, C., Dondi, F.: Metal content in river suspended particulate matter: data on PP river. Ann. Chim J. Anal. Environ. Cult. Herit. Chem. 94(5–6), 353–364 (2004)

    Google Scholar 

  9. Collette, Y., Siarry, P.: Multiobjective Optimization: Principles and Case Studies. Springer, Berlin (2004). https://doi.org/10.1007/978-3-662-08883-8

    Book  MATH  Google Scholar 

  10. Dean, T.: Land and Power in Late Medieval Ferrara: The Rule of the Este, pp. 1350–1450. No. 7, Cambridge University Press, Cambridge (1987)

    Google Scholar 

  11. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, London (2001)

    MATH  Google Scholar 

  12. Di Roma, A., Lucena-Sánchez, E., Sciavicco, G., Vaccaro, C.: Towards automatic fingerprinting of groundwater aquifers. In: Valencia-García, R., Alcaraz-Marmol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds.) CITI 2020. CCIS, vol. 1309, pp. 73–84. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62015-8_6

    Chapter  Google Scholar 

  13. Durillo, J., Nebro, A.: Jmetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42, 760–771 (2011)

    Article  Google Scholar 

  14. Emmanouilidis, C., Hunter, A., Macintyre, J., Cox, C.: A multi-objective genetic algorithm approach to feature selection in neural and fuzzy modeling. Evol. Optim. 3(1), 1–26 (2001)

    Google Scholar 

  15. Farhadian, H., Katibeh, H.: New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis. Int. J. Mining Sci. Technol. 27(3), 415–421 (2017)

    Article  Google Scholar 

  16. Franzini, M., et al.: Revisione di una metodologia analitica per fluorescenza-x, basata sulla correzione completa degli effetti di matrice (1975)

    Google Scholar 

  17. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  18. Hoefs, J.: Geochemical fingerprints: a critical appraisal. Eur. J. Mineral. 22, 3–15 (2009)

    Article  Google Scholar 

  19. Jang, W.S., Engel, B., Yeum, C.M.: Integrated environmental modeling for efficient aquifer vulnerability assessment using machine learning. Environ. Model. Softw. 124, 104602 (2020)

    Google Scholar 

  20. Jiménez, F., Sánchez, G., García, J., Sciavicco, G., Miralles, L.: Multi-objective evolutionary feature selection for online sales forecasting. Neurocomputing 234, 75–92 (2017)

    Article  Google Scholar 

  21. Kozyatnyk, I., Lövgren, L., Tysklind, M., Haglund, P.: Multivariate assessment of barriers materials for treatment of complex groundwater rich in dissolved organic matter and organic and inorganic contaminants. J. Environ. Chem. Eng. 5(4), 3075–3082 (2017)

    Article  Google Scholar 

  22. Lachance, G.: Practical solution to the matrix problem in X-ray analysis. Can. Spectrosc. 11, 43–48 (1966)

    Google Scholar 

  23. Lary, D., Alavi, A., Gandomi, A., Walker, A.: Machine learning in geosciences and remote sensing. Geosci. Front. 7(1), 3–10 (2016)

    Article  Google Scholar 

  24. Lary, D., Muller, M., Mussa, H.: Using neural networks to describe tracer correlations. Atmos. Chem. Phys. 4, 143–146 (2004)

    Article  Google Scholar 

  25. Liu, H., Zhou, X., Zhang, X., Wu, K., Lu, C.: Experimental study and matrix effect correction of pseudobinary samples in XRF analysis. In: IOP Conference Series: Materials Science and Engineering. vol. 389. IOP Publishing (2018)

    Google Scholar 

  26. López-Arce, P., Garcia-Guinea, J., Gracia, M., Obis, J.: Bricks in historical buildings of Toledo city: characterisation and restoration. Mater. Character. 50(1), 59–68 (2003)

    Article  Google Scholar 

  27. Mair, A., El-Kadi, A.: Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA. J. Contamin. Hydrol. 153, 1–23 (2013)

    Article  Google Scholar 

  28. Marrocchino, E., Telloli, C., Vaccaro, C.: Geochemical and mineralogical characterization of construction materials from historical buildings of Ferrara (Italy). Geosciences 11(1), 31 (2021)

    Article  Google Scholar 

  29. McIver, K.A.: The ESTE monuments and urban development in renaissance Ferrara 13, 230–233 (1999)

    Google Scholar 

  30. McIver, K.A., Rosenberg, C.M.: The ESTE monuments and urban development in renaissance Ferrara 29, 121 (1998)

    Google Scholar 

  31. Menció, A., et al.: Nitrate pollution of groundwater; all right.., but nothing else? Sci. Total Environ. 539, 241–251 (2016)

    Google Scholar 

  32. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., Coello, C.C.: A survey of multiobjective evolutionary algorithms for data mining: Part I. IEEE Trans. Evol. Comput. 18(1), 4–19 (2014)

    Article  Google Scholar 

  33. Ozdemir, A.: Gis-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. J. Hydrol. 411(3), 290–308 (2011)

    Article  Google Scholar 

  34. Pizzol, L., Zabeo, A., Critto, A., Giubilato, E., Marcomini, A.: Risk-based prioritization methodology for the classification of groundwater pollution sources. Sci. Total Environ. 506, 505–517 (2015)

    Article  Google Scholar 

  35. Potts, P.: X-ray fluorescence analysis: principles and practice of wavelength dispersive spectrometry. In: A Handbook of Silicate Rock Analysis. pp. 226–285. Springer, Dordrecht (1987). https://doi.org/10.1007/978-94-015-3988-3_8

  36. Rosenberg, C.M.: The ESTE monuments and urban development in renaissance Ferrara (1997)

    Google Scholar 

  37. Rousseau, R.M.: Corrections for matrix effects in X-ray fluorescence analysis-a tutorial. Spectrochim. Acta Part B Atom. Spectrosc. 61(7), 759–777 (2006)

    Article  Google Scholar 

  38. Sanfilippo, G., Aquilia, E.: Multidisciplinary process aimed at the diagnosis and treatment of damages in stony cultural heritage: the balustrade of villa cerami (catania). Mediterr. Archaeol. Archaeom. 18(5) (2018)

    Google Scholar 

  39. Shahin, M., Jaksa, M., Maier, H.: Artificial neural network applications in geotechnical engineering. Aust. Geomech. 1(36), 49–62 (2001)

    Google Scholar 

  40. Singh, C.K., Kumar, A., Shashtri, S., Kumar, A., Kumar, P., Mallick, J.: Multivariate statistical analysis and geochemical modeling for geochemical assessment of groundwater of Delhi. India J. Geochem. Expl. 175, 59–71 (2017)

    Article  Google Scholar 

  41. Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Elsevier (2011)

    Google Scholar 

  42. Yi, J., Prybutok, V.: A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environ. Pollut. 3(92), 349–357 (1996)

    Article  Google Scholar 

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Acknowledgments

E. Lucena-Sánchez and G. Sciavicco acknowledge the support of the project New Mathematical and Computer Science Methods for Water and Food Resources Exploitation Optimization, founded by the Italian region Emilia-Romagna.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy

    Guido Sciavicco & Estrella Lucena-Sánchez

  2. Department of Physics, Informatics, and Mathematics, University of Modena and Reggio Emilia, Modena, Italy

    Estrella Lucena-Sánchez

  3. Department of Physics and Earth Science, University of Ferrara, Ferrara, Italy

    Elena Marrocchino & Carmela Vaccaro

Authors
  1. Elena Marrocchino
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  2. Guido Sciavicco
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  3. Estrella Lucena-Sánchez
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  4. Carmela Vaccaro
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Corresponding author

Correspondence to Estrella Lucena-Sánchez .

Editor information

Editors and Affiliations

  1. Universidad de Murcia, Espinardo, Spain

    Rafael Valencia-García

  2. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Martha Bucaram-Leverone

  3. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Javier Del Cioppo-Morstadt

  4. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Néstor Vera-Lucio

  5. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Emma Jácome-Murillo

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Marrocchino, E., Sciavicco, G., Lucena-Sánchez, E., Vaccaro, C. (2021). On Intelligent Fingerprinting of Antique Buildings from Clay Composition. In: Valencia-García, R., Bucaram-Leverone, M., Del Cioppo-Morstadt, J., Vera-Lucio, N., Jácome-Murillo, E. (eds) Technologies and Innovation. CITI 2021. Communications in Computer and Information Science, vol 1460. Springer, Cham. https://doi.org/10.1007/978-3-030-88262-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-88262-4_3

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  • Online ISBN: 978-3-030-88262-4

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