Asbestos Cement - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Asbestos Cement

The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform

Deborah Ramires – One of the best experts on this subject based on the ideXlab platform.

  • Method validation for the identification of AsbestosCement roofing
    Applied Geomatics, 2012
    Co-Authors: Lorenza Fiumi, Antonella Campopiano, Stefano Casciardi, Deborah Ramires

    Abstract:

    The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping AsbestosCement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as AsbestosCement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of AsbestosCement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of AsbestosCement in the studied area has been also collected. The AsbestosCement surfaces of buildings vary from 100 to 5,000 m^2, totalling to 30,800 m^2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km^2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of AsbestosCement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.

  • method validation for the identification of Asbestos Cement roofing
    Applied Geomatics, 2012
    Co-Authors: Lorenza Fiumi, Antonella Campopiano, Stefano Casciardi, Deborah Ramires

    Abstract:

    The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping AsbestosCement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as AsbestosCement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of AsbestosCement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of AsbestosCement in the studied area has been also collected. The AsbestosCement surfaces of buildings vary from 100 to 5,000 m2, totalling to 30,800 m2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of AsbestosCement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.

  • Risk assessment of the decay of Asbestos Cement roofs.
    Annals of Occupational Hygiene, 2009
    Co-Authors: Antonella Campopiano, Deborah Ramires, Aneta Maria Zakrzewska, Rosa Ferri, Antonio D'annibale, Giancarlo Pizzutelli

    Abstract:

    Objectives: In an assessment of the risk of Asbestos fibres release from Asbestos Cement materials, an important role is played by the assessment of the surface corrosion and by the disaggregation of Asbestos Cement. The aim of this work is to evaluate the differences among several methods used for the risk assessment that lead to a specific choice of abatement techniques. Methods: The state of deterioration of 40 Asbestos Cement roofs was evaluated using two priority assessment algorithms elaborated in Italy, the ‘pull-up test’ described by the Italian Organization for Standardization and the indicators described in the Italian legislation coupled with the observation of a small sample, taken from each roof, by a stereomicroscope. Results: The results obtained with the methods, proposed in this study, for the risk assessment of the decay of Asbestos Cement roofs show slight differences among them, only one deviates from the others in judgement on the state of conservation of the roof. Conclusions: It is very important to train the operator conducting the study since a completely subjectivity-free method does not exist. Whatever method is used will always be affected by the subjectivity linked to the competency and the training of the operator. Moreover, each method on its own cannot assess the risk of exposure to Asbestos, but reliable assessment of Asbestos-containing materials requires the use of more than one method, such as visual inspections, a pull-up test, and an assessment algorithm.

Antonella Campopiano – One of the best experts on this subject based on the ideXlab platform.

  • Method validation for the identification of AsbestosCement roofing
    Applied Geomatics, 2012
    Co-Authors: Lorenza Fiumi, Antonella Campopiano, Stefano Casciardi, Deborah Ramires

    Abstract:

    The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping AsbestosCement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as AsbestosCement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of AsbestosCement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of AsbestosCement in the studied area has been also collected. The AsbestosCement surfaces of buildings vary from 100 to 5,000 m^2, totalling to 30,800 m^2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km^2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of AsbestosCement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.

  • method validation for the identification of Asbestos Cement roofing
    Applied Geomatics, 2012
    Co-Authors: Lorenza Fiumi, Antonella Campopiano, Stefano Casciardi, Deborah Ramires

    Abstract:

    The aim of this multidisciplinary work is to assess the potentiality of remote sensing multispectral infrared visible imaging spectrometer (MIVIS) data classified for mapping AsbestosCement roofing. In order to validate the methodology, measurement were carried out on the ground in order to later verify the results between the processed data and reality. All roofs classified as AsbestosCement were then sampled and analysed by phase contrast optical microscopy and/or scanning electron microscopy. The average classification accuracy obtained corresponds to 89.1%, and the classification accuracy of the test pixels of AsbestosCement is equal to 94.3%. Only 5.7% of pixels were misclassified. Information about the presence of AsbestosCement in the studied area has been also collected. The AsbestosCement surfaces of buildings vary from 100 to 5,000 m2, totalling to 30,800 m2, which is approximately 400,400 kg of Asbestos surfaces in an area of 5.2 km2. The integration of these techniques, resulting from both MIVIS data classification and the results provided by laboratory analyses of the roofs samples, in particular from those not detected by processing MIVIS data, allowed the validation and improvement of this method, and the possibility to develop researches specifically aimed at highlighting the state of alteration of AsbestosCement surfaces. Regardless of these encouraging results, further testing in different areas is still needed in order to improve the methodology developed.

  • Risk assessment of the decay of Asbestos Cement roofs.
    Annals of Occupational Hygiene, 2009
    Co-Authors: Antonella Campopiano, Deborah Ramires, Aneta Maria Zakrzewska, Rosa Ferri, Antonio D'annibale, Giancarlo Pizzutelli

    Abstract:

    Objectives: In an assessment of the risk of Asbestos fibres release from Asbestos Cement materials, an important role is played by the assessment of the surface corrosion and by the disaggregation of Asbestos Cement. The aim of this work is to evaluate the differences among several methods used for the risk assessment that lead to a specific choice of abatement techniques. Methods: The state of deterioration of 40 Asbestos Cement roofs was evaluated using two priority assessment algorithms elaborated in Italy, the ‘pull-up test’ described by the Italian Organization for Standardization and the indicators described in the Italian legislation coupled with the observation of a small sample, taken from each roof, by a stereomicroscope. Results: The results obtained with the methods, proposed in this study, for the risk assessment of the decay of Asbestos Cement roofs show slight differences among them, only one deviates from the others in judgement on the state of conservation of the roof. Conclusions: It is very important to train the operator conducting the study since a completely subjectivity-free method does not exist. Whatever method is used will always be affected by the subjectivity linked to the competency and the training of the operator. Moreover, each method on its own cannot assess the risk of exposure to Asbestos, but reliable assessment of Asbestos-containing materials requires the use of more than one method, such as visual inspections, a pull-up test, and an assessment algorithm.

Ewa Wilk – One of the best experts on this subject based on the ideXlab platform.

  • AsbestosCement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)
    Remote Sensing, 2020
    Co-Authors: Małgorzata Krówczyńska, Edwin Raczko, Natalia Staniszewska, Ewa Wilk

    Abstract:

    Due to the pathogenic nature of Asbestos, a statutory ban on Asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of AsbestosCement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of AsbestosCement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of Asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of AsbestosCement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. AsbestosCement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of AsbestosCement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of AsbestosCement products in use is important for assessing environmental exposure to Asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats.

  • Modelling the Spatial Distribution of AsbestosCement Products in Poland with the Use of the Random Forest Algorithm
    Sustainability, 2019
    Co-Authors: Ewa Wilk, Małgorzata Krówczyńska, Bogdan Zagajewski

    Abstract:

    The unique set of physical and chemical properties of Asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used Asbestos-containing products. Since Asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of AsbestosCement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of AsbestosCement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on AsbestosCement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on Asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of AsbestosCement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of AsbestosCement products used in Poland. The total amount was estimated at 710,278,645 m2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of Asbestos exposure to humans.

  • modelling the spatial distribution of Asbestos Cement products in poland with the use of the random forest algorithm
    Sustainability, 2019
    Co-Authors: Ewa Wilk, Malgorzata Krowczynska, Bogdan Zagajewski

    Abstract:

    The unique set of physical and chemical properties of Asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used Asbestos-containing products. Since Asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of AsbestosCement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of AsbestosCement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on AsbestosCement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on Asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of AsbestosCement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of AsbestosCement products used in Poland. The total amount was estimated at 710,278,645 m2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of Asbestos exposure to humans.