T. Rivas

Surfactant-Synthesized Ormosils with Application to Stone Restoration

A challenging objective in monumental stone restoration is to synthesize crack-free silica materials for application as consolidants. Hydrophobicity is also a valuable property for such products; it is important to prevent the penetration of water because water is the main vehicle by which the agents of decay enter the pore structure of the stone. We report the development of a hydrophobic crack-free nanomaterial with application to stone restoration. Specifically, organically modified silicate (ormosil) has been synthesized by the co-condensation of tetraethoxysilane (TEOS) and hydroxyl-terminated polydimethylsiloxane (PDMS) in the presence of a nonionic surfactant (n-octylamine). The role played by the surfactant in the assembly of the organic-inorganic hybrid silica gel was investigated. We also prepared a crack-free material using the same synthesis but without adding PDMS to the starting sol. Finally, the effectiveness of the nanomaterials synthesized as a consolidant and hydrophobic protective treatment was evaluated on a particular widely used monumental stone. The high hydrophobicity of the organic-inorganic hybrid product synthesized in our laboratory is discussed as a function of the surface roughness of the material.

Explaining and predicting workplace accidents using data-mining techniques

Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

Reforestation planning using Bayesian networks

The aim of this research was to construct a reforestation model for woodland located in the basin of the river Liebana (NW Spain). This is essentially a pattern recognition problem: the class labels are types of woodland, and the variables for each point are environmental coordinates (referring to altitude, slope, rainfall, lithology, etc.). The model trained using data for existing wooded areas will serve as a guideline for the reforestation of deforested areas. Nonetheless, with a view to tackling reforestation from a more informed perspective, of interest is an interpretable model of relationships existing not just between woodland type and environmental variables but also between and among the environmental variables themselves. For this reason we used Bayesian networks, as a tool that is capable of constructing a causal model of the relationships existing between all the variables represented in the model. The prediction results obtained were compared with those for classical linear techniques, neural networks and support vector machines.

New Nanomaterials for Protecting and Consolidating Stone

The sol-gel process has been found to be successful in applications for the conservation and restoration of stone. However, a well-known drawback of the materials obtained by this process is their tendency to crack during drying inside the pores of the treated stone. In this article, we present an overview of our current research centred on producing crack-free sol-gel materials for consolidating and protecting building stone. A novel synthesis, in which a surfactant acts as a template to make the pore size of the gel network coarser and more uniform, is shown to provide an effective alternative for preventing the cracking of consolidants. We also highlight an alternative pathway, in which we add an organic component to the silica precursor in the presence of the surfactant. The hybrid organic-inorganic gel prepared in our laboratory provides excellent waterproofing to the stones under study.

Effectiveness of granite cleaning procedures in cultural heritage: A review.

Most of the Cultural Heritage built in NW Iberian Peninsula is made of granite which exposition to the environment leads to the formation of deposits and coatings, mainly two types: biological colonization and sulphated black crusts. Nowadays, another form of alteration derives from graffiti paints when these are applied as an act of vandalism. A deep revision needs to be addressed considering the severity of these deterioration forms on granite and the different cleaning effectiveness achieved by cleaning procedures used to remove them. The scientific literature about these topics on granite is scarcer than on sedimentary carbonate stones and marbles, but the importance of the granite in NW Iberian Peninsula Cultural Heritage claims this review centred on biological colonization, sulphated black crusts and graffiti on granite and their effectiveness of the common cleaning procedures. Furthermore, this paper carried out a review of the knowledge about those three alteration forms on granite, as well as bringing together all the major studies in the field of the granite cleaning with traditional procedures (chemical and mechanical) and with the recent developed technique based on the laser ablation. Findings concerning the effectiveness evaluation of these cleaning procedures, considering the coating extraction ability and the damage induced on the granite surface, are described. Finally, some futures research lines are pointed out.

Sulphur and oxygen isotope analysis to identify sources of sulphur in gypsum-rich black crusts developed on granites.

We describe the results of sulphur and oxygen isotope analyses used to identify sources of the gypsum present in black crusts that grow on the granite of historical buildings. The crusts were sampled at various locations in and near the city of Vigo (NW Spain) and were analysed for their sulphur content and δ(34)S and δ(18)O isotope ratios. Sampled crusts had δ(34)S values of 7.3‰ to 12.9‰ and δ(18)O values of 6.56‰ to 12.51‰. Sampled as potential sulphur sources were bulk depositions, seawater, foundation, ashlar and construction materials and combustion residues. The results indicated marine and, to a lesser extent, anthropogenic, origins for the sulphur and ruled out the contribution of sub-soil sulphates by capillary rise from building foundations. Isotope analyses would indicate that cement and mortar were enriched in sulphur after their application in buildings. The fact that facade orientation (towards the sea or fossil fuel pollution sources) was correlated with sulphur isotope distribution pointed to various contributions to black crust formation.

A machine learning methodology for the analysis of workplace accidents

Abstract This article proposes a methodology for the analysis of the causes and types of workplace accidents (in this paper we focus specifically on floor-level falls). The approach is based on machine learning techniques: Bayesian networks trained using different algorithms (with and without a priori information), classification trees, support vector machines and extreme learning machines. The results obtained using the different techniques are compared in terms of explanatory capacity and predictive potential, both factors facilitating the development of risk prevention measures. Bayesian networks are revealed to be the best all-round technique for this type of study, as they combine a powerful interpretative capacity with a predictive capacity that is comparable to that of the best available techniques. Moreover, the Bayesian networks force experts to apply a scientific approach to the construction and progressive enrichment of their models and also enable the basis to be laid for an accident prevention policy that is solidly grounded. Furthermore, the procedure enables better variable definition, better structuring of the data capture, coding, and quality control processes.

Functional support vector machines and generalized linear models for glacier geomorphology analysis

We propose a functional pattern recognition approach to the problem of identifying the topographic profiles of glacial and fluvial valleys, using a functional version of support vector machines (SVMs) for classification. We compare a proposed functional version of SVMs with functional generalized linear models and their vectorial versions: generalized linear models and SVMs that use the original observations as input. The results indicate the benefit of our proposed functional SVMs and, in more general terms, the advantages of using a functional rather than a vectorial approach.

Functional experiment design for the analysis of colour changes in granite using new L * a * b * functional colour coordinates

We propose a functional data approach to evaluating colour changes in stone that is based on applying a functional experiment design to the tristimulus curves resulting from the product of the power spectral distribution of the source, the stone reflectance curve and the matching colour functions of the standard observer. The proposed method was applied to an analysis of colour changes in granite after the application of different desalination treatments. The results were compared with those obtained by the classical analysis of variance applied to the colorimetric coordinates L^*a^*b^*. The granite RGB and XYZ colour coordinate systems were obtained by integrating the respective tristimulus curves. The L^*a^*b^* coordinates, however, were obtained directly by transforming the XYZ coordinates, as no corresponding tristimulus functions have been proposed to date. With a view to comparing the results for these functional and scalar methods for a uniform colour measurement system, these functions, whose integral coincides with the L^*a^*b^* values, have been deduced and proposed for the first time. The results obtained demonstrate the usefulness of the additional information supplied by the functional approach. However, this information does not replace that produced by the scalar approach for the scalar coordinates, and so it is recommended to use both approaches. The new tristimulus functions associated with the L^*a^*b^* coordinates are perfectly interpretable in a way analogous to the coordinates themselves, i.e., as the degree of luminosity (L^*), the green-red relative position (a^*) and the blue-yellow relative position (b^*), except that they are interpreted for each infinitesimal wavelength interval. A brief introduction to the colour measurement problem and to functional statistical techniques is provided for readers coming from different disciplines.

Assessing the Environmental Impact of Slate Quarrying Using Bayesian Networks and GIS

This article describes how a Bayesian network incorporated in a geographical information system can be used to evaluate the environmental impact of a mine. Our Bayesian network was constructed from expert information and field data reflecting the susceptibility of these environmental elements to primary, secondary or synergic impacts. Once created the Bayesian network enabled us to do the following: 1) to draw inferences in relation to the environmental impact for points in a grid covering the studied area, for incorporation in a geographical information system and creating an environmental impact map; 2) to better understand the structure of relationships between mining tasks and different environmental variables; and 3) to draw on a knowledge system that is enriched each time new data is added.