Abstract Fiber Reinforced Soils (FRS) are mixtures of discrete fibers with the soil to create a composite with improved mechanical properties compared to unreinforced material that depends on several soil and fiber properties. Therefore, comparative studies are needed to better understand their influence on FRS mechanical response. This paper analyzes the results of a comprehensive triaxial testing program performed on specimens of two different sands at the same relative density focusing on how the grain size distribution affects the composite behavior in terms of shear strength and dilatancy. It is shown that the grain size curve's uniformity coefficient (Cu) is one of the critical variables controlling FRS's dilatancy. Dune sand specimens (Cu = 1.79) presented dilatancy even for confining stresses as high as 300 kPa. The shear gains due to reinforcement were controlled by fiber length (L) and percentage (Pf), and size and shape of soil particles. River sand specimens with L = 51 mm and 1% fiber addition (dry mass) presented increments of 47.7 kPa in soil cohesion and a 5.2o increase in the soil friction angle compared to unreinforced material.

Abstract Geophysical methods are potent tools for geotechnical site characterization in a non-destructive way. They improve the extrapolation of punctual data from direct survey methods, allowing a fast and cost-effective evaluation of large areas. Ground Penetrating Radar (GPR) and DC electrical resistivity (ER) are the most requested methods for geotechnical and geoenvironmental applications. Their use, however, is usually uncoupled, with no sharing of information from one method to another to improve data interpretation. This case study illustrates the development of protocols and scripts in R© programming language for ER and GPR data analysis with Standard Penetration Tests (SPT) data to produce more accurate information on subsurface conditions concerning lithology, water content, and groundwater table (GWT) position. The SPT data were used to associate resistivity ranges with different soil lithologies and GPR pulse velocities for estimating the soil water content. Estimated water content values aided in interpreting ER data and locating the groundwater table. The contacts between layers in the radargrams allowed the refinement of the ER model, rendering 3D volumes for each soil layer in situ. nondestructive non destructive way costeffective cost effective areas (GPR (ER applications use however uncoupled interpretation R (SPT lithology GWT (GWT position model D situ

Resumo A corrosão é um dos principais fenômenos que geram manifestações patológicas nas estruturas de concreto armado em ambientes agressivos, sendo o íon cloro o maior responsável por sua ocorrência. Desta forma, entender os mecanismos de transporte deste íon através da microestrutura do concreto é de fundamental importância para impedir ou retardar a penetração destes agentes agressivos, visando garantir uma estrutura durável. Na literatura são vastos os estudos referentes à difusão de cloretos em concreto e a influência das adições pozolânicas neste mecanismo, entretanto, poucos correlacionam os diferentes métodos de análise. Assim, este trabalho visa determinar os coeficientes de difusão do íon cloro em concretos contendo diversos teores de adição de sílica ativa (5%, 10% e 15%) ou com variação do teor de argamassa (54%, 80% e 100%), comparando os resultados obtidos por meio dos ensaios de migração iônica e de coluna. Observou-se que, apesar das técnicas utilizadas serem bastante distintas, os valores de coeficientes de difusão obtidos foram semelhantes, colaborando para a validação de ambas as técnicas. Além disso, a variação no teor de argamassa acarreta uma redução na zona de transição interfacial agregado graúdo/argamassas e um aumento no teor de aluminatos, o que promove efeito semelhante ao uso de sílica ativa.

Abstract Corrosion is one of the main phenomena that lead to pathological manifestations in reinforced concrete structures under aggressive environments. with the chloride ion being the most responsible for its occurrence. In this way, understanding the transport mechanisms of this ion through the microstructure of the concrete is of fundamental importance to prevent or delay the penetration of these aggressive agents to guarantee a durable structure. In the literature, there are extensive studies concerning the diffusion of chlorides in concrete and the influence of pozzolanic additions in this mechanism. However, only a few correlate the different methods of analysis. This work aims to determine the chloride ion diffusion coefficients in concrete containing various levels of silica fume (5%, 10%, and 15%) or varying the mortar content (54%, 80%, and 100%), and compares the results obtained through column tests and chloride migration tests. It was observed that, although the techniques used were quite distinct, the diffusion values obtained were similar, contributing to the validation of both techniques. Furthermore, the variation in the mortar ratio causes a reduction in the interfacial transition zone of coarse aggregate/mortars and an increase in the content of aluminates, which promotes a similar effect to the use of silica fume.

Abstract This paper explores the potential of machine learning techniques to predict the soil water retention curve based on physical characterization parameters. Results from 794 water retention and suction points obtained from 51 different soils were used in the algorithm. The soil properties used are the percentages of gravel, sand, silt, and clay, the plasticity index, the porosity, and the relation between the volumetric water content and total suction. The data were used as input for machine learning estimators to predict the volumetric water content of a soil with specified physical characterization parameters and suction, the techniques of artificial intelligence were developed in python. Results show that an extremely randomized trees' estimator can reach a coefficient of determination of 0.99 in the training dataset, with a coefficient of 0.90 in the cross-validation and testing dataset, which measures the generalization capacity. Furthermore, a continuous function can be obtained by fitting a model such as Cavalcante & Zornberg, or van Genuchten, or Costa & Cavalcante (bimodal) to the predictions of the machine learning for use in numerical methods. These results indicate that the proposed machine learning estimator can become an interesting alternative to estimate the soil water retention curve in engineering practice. This work is in progress and the predictions can be improved with the addition of new data. Know how to participate at the end of the paper.

O objetivo do presente trabalho foi estimar as doses de exposição aos contaminantes chumbo (Pb) e cádmio (Cd) em crianças de 0 a 17 anos de idade via consumo de alimentos vegetais cultivados em quintais contaminados, localizados no entorno de uma metalúrgica desativada, no município de Santo Amaro, Estado da Bahia. Para a estimativa das doses foi utilizada a metodologia de avaliação de riscos sobre a saúde humana, especificamente a etapa de análise da exposição, determinando as concentrações dos metais e variáveis sociodemográficas das crianças. As doses de exposição estimadas para ambos os contaminantes superaram os valores recomendados pela Organização Mundial da Saúde (OMS), atingindo valores máximos de exposição de 166,9 μg.kg−1 dia−1 e 2,95 μg.kg−1 dia−1 para o Pb e o Cd, respectivamente, por consumo de frutas. Os resultados comprovam que o consumo de alimentos vegetais cultivados em quintais pela população residente próxima à antiga fábrica é uma importante rota de exposição atual aos metais, representando um risco para a saúde da população.

This paper aims to estimate the exposure of children between 0 to 17 years old to lead (Pb) and cadmium (Cd) via ingestion of vegetables cultivated in contaminated backyards located in the surroundings of a deactivated lead metallurgy in Santo Amaro, Bahia, Brazil. For the estimation of the exposure doses, the risk assessment on human health methodology, exposure analysis, was used, determining the concentrations of metals and socio-demographic variables of children. The estimated doses for both contaminants exceeded the values recommended by the World Health Organization (WHO). Maximum obtained values were 166.9 μg.kg−1 day−1 and 2.95 μg.kg−1day−1 for Cd and Pb, respectively, due to fruits consumption. The results show that the consumption of vegetables cultivated in the backyards by the population living near the deactivated metallurgical plant plays an important role in the exposition of the population to metals.

This study evaluated the level of the metals Pb mg kg-1 and Cd mg kg-1 found in plant species and the contribution of the levels detected in contaminated soils located in the urban area of the city of Santo Amaro, Bahia. Levels were determined by graphite furnace and flame atomic absorption spectrometry. The results showed the presence of Pb and Cd in plant species at levels ranging from 0.18 to 118.2 mg kg-1 and 0.04 to 7.29 mg kg-1, respectively. The concentration values obtained varied according to the plant species and were not strongly influenced by the concentration values detected in the soil.