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Este trabalho propõe a aplicação da técnica de redes neurais artificiais (RNAs) para a previsão de propriedades geotécnicas dos solos do município de São Carlos (SP), baseada em outras propriedades determinadas preliminarmente. Esse método tem a finalidade de simplificar o processo de obtenção das propriedades dos solos, eliminando a lentidão dos procedimentos de ensaios e os cálculos a serem realizados, além de reduzir a dificuldade de ter que fazê-los utilizando os métodos tradicionais. Foram simuladas cento e noventa e sete RNAs para a previsão das seguintes propriedades: unidade ótima, massa específica seca máxima, mini-CBR na umidade de moldagem obtido na umidade ótima, mini-CBR obtido após 24h de imersão na umidade ótima, expansão e contração obtidas na umidade ótima para as energias normal e intermediária. No treinamento das RNAs foi utilizada uma base de dados com um total de cento e uma amostras que, além de conter os valores das propriedades \"alvo\" para previsão, também contém: valor de azul (Va), coeficiente de atividade (CA), análise granulométrica por sedimentação (peneiras #0,42,#0,074 e #0,075), parâmetros da classificação MCT (c\', Pi, d\' e e\') e classificação por cores (croma, valor e matriz). O aplicativo utilizado para treinar as RNAs foi o EASYNN 7.5, que se baseia em redes Multiplayer Perceptron e no algoritmo de treinamento Backpropagation. Para a previsão de propriedades geotécnicas dos solos, os desempenhos das redes foram bastante bons para umidade ótima, massa específica seca máxima e contração nas energias normal e intermediária. Contudo, os desempenhos das RNAs para mini-CBR na umidade de moldagem, mini-CBR após 24h de imersão e expansão obtidas na umidade ótima de energias normal e intermediária foram menos satisfatórios. De maneira geral, os resultados obtidos nesse estudo sugerem que modelos que fazem uso das redes neurais artificiais para previsão de propriedades geotécnicas de solos para pavimentação apresentam-se como promissores e podem, no futuro, contribuir para a melhoria e redução de custos da fase de estudo geotécnico para implantação de vias em municípios de pequeno e médio portes. / The application of the technique of Artificial Neural Networks (ANNs) for the forecast of geotechnical properties of the soils in São Carlos, a municipal district in the of São Paulo State, based on other properties determined preliminary is the purpose of this work. This method has the goal of simplifying the process of obtaining the properties of the soils, eliminating the slowness of the tests procedures and the calculations to be accomplished, besides reducing the difficulty of having to do them using the traditional methods. One hundred and ninety seven ANNs were simulated for the forecast of the following properties: optimum moisture content, dry density, mini-CBR in the molding humidity obtained in the optimum moisture content, mini-CBR obtained after 24 h of soaking in the optimum moisture content, expansion and contraction obtained in the optimum moisture content for the normal and intermediate energies. In the training of ANNs a base of data was used with a total of one hundred and one samples that, besides containing the values of the properties \"objective\" for forecast, it also contains: methylene blue value (Va), activity coefficient (CA), granulometric analysis for sedimentation (sieves #0,42, #0,074 and #0,005), parameters of the MCT classification (c\', Pi, d\' and e\') and classification by colors (chroma, value and hue). The application used to train ANNs was EasyNN 7.5, that bases on nets Multilayer Perceptron and in the training algorithm Backpropagation. For the forecast of geotechnical properties of the soils, the performance of the nets were very good for optimum moisture content, dry density and contraction in the normal and intermediate energies. However, the performance of ANNs for mini-CBR in the molding humidity, mini-CBR after 24 h of immersion and expansion obtained in the optimun moisture content of the normal and intermediate energies were less satisfactory. In a general way, the results obtained in this study suggest that the models that use Artificial Neural Networks for forecast of geotechnical properties of soils come as promising and can, in the future, contribute for the improvement and costs reduction during the period of geothecnical study in the implantation of roads in small and medium sized municipal districts.

Este trabalho propõe a aplicação da técnica de redes neurais artificiais (RNAs) para a previsão de propriedades geotécnicas dos solos do município de São Carlos (SP), baseada em outras propriedades determinadas preliminarmente. Esse método tem a finalidade de simplificar o processo de obtenção das propriedades dos solos, eliminando a lentidão dos procedimentos de ensaios e os cálculos a serem realizados, além de reduzir a dificuldade de ter que fazê-los utilizando os métodos tradicionais. Foram simuladas cento e noventa e sete RNAs para a previsão das seguintes propriedades: unidade ótima, massa específica seca máxima, mini-CBR na umidade de moldagem obtido na umidade ótima, mini-CBR obtido após 24h de imersão na umidade ótima, expansão e contração obtidas na umidade ótima para as energias normal e intermediária. No treinamento das RNAs foi utilizada uma base de dados com um total de cento e uma amostras que, além de conter os valores das propriedades \"alvo\" para previsão, também contém: valor de azul (Va), coeficiente de atividade (CA), análise granulométrica por sedimentação (peneiras #0,42,#0,074 e #0,075), parâmetros da classificação MCT (c\', Pi, d\' e e\') e classificação por cores (croma, valor e matriz). O aplicativo utilizado para treinar as RNAs foi o EASYNN 7.5, que se baseia em redes Multiplayer Perceptron e no algoritmo de treinamento Backpropagation. Para a previsão de propriedades geotécnicas dos solos, os desempenhos das redes foram bastante bons para umidade ótima, massa específica seca máxima e contração nas energias normal e intermediária. Contudo, os desempenhos das RNAs para mini-CBR na umidade de moldagem, mini-CBR após 24h de imersão e expansão obtidas na umidade ótima de energias normal e intermediária foram menos satisfatórios. De maneira geral, os resultados obtidos nesse estudo sugerem que modelos que fazem uso das redes neurais artificiais para previsão de propriedades geotécnicas de solos para pavimentação apresentam-se como promissores e podem, no futuro, contribuir para a melhoria e redução de custos da fase de estudo geotécnico para implantação de vias em municípios de pequeno e médio portes. / The application of the technique of Artificial Neural Networks (ANNs) for the forecast of geotechnical properties of the soils in São Carlos, a municipal district in the of São Paulo State, based on other properties determined preliminary is the purpose of this work. This method has the goal of simplifying the process of obtaining the properties of the soils, eliminating the slowness of the tests procedures and the calculations to be accomplished, besides reducing the difficulty of having to do them using the traditional methods. One hundred and ninety seven ANNs were simulated for the forecast of the following properties: optimum moisture content, dry density, mini-CBR in the molding humidity obtained in the optimum moisture content, mini-CBR obtained after 24 h of soaking in the optimum moisture content, expansion and contraction obtained in the optimum moisture content for the normal and intermediate energies. In the training of ANNs a base of data was used with a total of one hundred and one samples that, besides containing the values of the properties \"objective\" for forecast, it also contains: methylene blue value (Va), activity coefficient (CA), granulometric analysis for sedimentation (sieves #0,42, #0,074 and #0,005), parameters of the MCT classification (c\', Pi, d\' and e\') and classification by colors (chroma, value and hue). The application used to train ANNs was EasyNN 7.5, that bases on nets Multilayer Perceptron and in the training algorithm Backpropagation. For the forecast of geotechnical properties of the soils, the performance of the nets were very good for optimum moisture content, dry density and contraction in the normal and intermediate energies. However, the performance of ANNs for mini-CBR in the molding humidity, mini-CBR after 24 h of immersion and expansion obtained in the optimun moisture content of the normal and intermediate energies were less satisfactory. In a general way, the results obtained in this study suggest that the models that use Artificial Neural Networks for forecast of geotechnical properties of soils come as promising and can, in the future, contribute for the improvement and costs reduction during the period of geothecnical study in the implantation of roads in small and medium sized municipal districts.

Deep anthropogenic topsoils – those augmented through long-term additions of mineral bulk among fertilising agents – retain in both their physical and chemical make-up significant indicators for cultural activity. This project researched the geographical distribution and historical context of deep anthropogenic topsoils in Scotland and the Isles, and used this information to investigate the impact of current land cover upon the cultural information they retain. In so doing, the project investigated the potential for conservation of this significant cultural resource. A review of the historical information available on agricultural and manuring practices for Scotland identified several factors likely to affect deep topsoil distribution and frequency. These were: the availability of bulk manures to Scottish farmers, the significance of the seaweed resource in determining fertiliser strategies in coastal areas, and the influence of urban settlement and associated patterns of domestic and industrial waste disposal on the location of deep topsoils. Evidence for widespread deep topsoil development was limited. The primary data source used – the First Statistical Account of Scotland – was manipulated into a spatial database in ArcView GIS, to which geographical data from the Soil Survey of Scotland and national archaeological survey databases were added. This was used to devise a survey programme aiming both to investigate the potential factors affecting soil development listed above, and to locate deep topsoil sites for analysis. Three sites were identified with deep topsoils under different cover types (woodland, arable and pasture). The urban-influenced context of two of these highlighted the significance of urban settlement to the location of Scottish deep topsoils. Analysis of pH, organic matter, and total phosphorus content showed a correlation between raised organic matter and a corresponding increase in phosphorus content in soils under permanent vegetation. By contrast, soils under arable cultivation showed no such rise. This was attributed to the action of cropping in removing modern organic inputs prior to down-profile cycling. The potential for pasture and woodland cover to affect relict soil signatures was therefore observed. Thin section analysis aimed to both provide micromorphological characterisation of the three deep topsoil sites and investigate the effect of modern land cover on micromorphological indicators. Distinctive differences in micromorphological character were observed between the rural and urban deep topsoils, with the latter showing a strong focus on carbonised fuel residues and industrial wastes. All sites showed a highly individual micromorphological character, reflective of localised fertilising systems. There was no correlation between land cover type and survival of material indictors for anthropogenic activity, with soil cultural indicators surviving well, particularly those characteristic of urban-influenced topsoils. Suggestions for preservation strategies for this potentially rare and highly localised cultural resource included the incorporation of deep anthropogenic topsoil conservation into current government policy relating to care of the rural historic environment, and the improvement of data on the resource through ongoing survey and excavation.