Intelligent compaction technology for geomaterials. A demonstration project

Intelligent Compaction (IC), which is a part of Compaction Management, is a real time automatic adjustment and continuous compaction control technology of geomaterials or asphalt layers. The adjustment of the compaction parameters by the equipment is conducted simultaneously to the compaction process, as well as the continuous measurement of a dynamic compaction value, which is an indicator of the material’s degree of compaction. This study seeks to assess the advantages and disadvantages of IC, as well as formulating a comparison with conventional compaction methods in terms of efficiency. This goal was achieved through in situ application of various technologies to two distinct types of material: a soil-rockfill mixture and a sandy soil. Data was obtained and analysed by the IC continuous information, as well as by the application of several different conventional compaction control tests and methods. Results show that the IC technology presents a superior performance, as well as various advantages when compared to conventional compactors.Fundação para a Ciência e a Tecnologia (FCT

Earthwork optimization system for sustainable highway construction

In highway construction, earthworks refer to the tasks of excavation, transportation, spreading and compaction of geomaterial (e.g. soil, rockfill and soil-rockfill mixture). Whereas relying heavily on machinery and repetitive processes, these tasks are highly susceptible to optimization. In this context Artificial Intelligent techniques, such as Data Mining and modern optimization can be applied for earthworks. A survey of these applications shows that they focus on the optimization of specific objectives and/or construction phases being possible to identify the capabilities and limitations of the analyzed techniques. Thus, according to the pinpointed drawbacks of these techniques, this paper describes a novel intelligent earthwork optimization system, capable of integrating DM, modern optimization and GIS technologies in order to optimize the earthwork processes throughout all phases of design and construction work. This integration system allows significant savings in time, cost and gas emissions contributing for a more sustainable construction

L’optimisation moderne dans les travaux de terrassement

Earthworks tasks are often regarded in transportation projects as some of the most demanding processes. In fact, sequential tasks such as excavation, transportation, spreading and compaction are strongly based on heavy mechanical equipment and repetitive processes, thus becoming as economically demanding as they are time-consuming. Moreover, actual construction requirements originate higher demands for productivity and safety in earthwork constructions. Given the percentual weight of costs and duration of earthworks in infrastructure construction, the optimal usage of every resource in these tasks is paramount. Considering the characteristics of an earthwork construction, it can be looked at as a production line based on resources (mechanical equipment) and dependency relations between sequential tasks, hence being susceptible to optimization. Up to the present, the steady development of Information Technology areas, such as databases, artificial intelligence and operations research, has resulted in the emergence of several technologies with potential application bearing that purpose in mind. Among these, modern optimization methods (also known as metaheuristics), such as evolutionary computation, have the potential to find high quality optimal solutions with a reasonable use of computational resources. In this context, this work describes an optimization algorithm for earthworks equipment allocation based on a modern optimization approach, which takes advantage of the concept that an earthwork construction can be regarded as a production line.RÉSUMÉ Les travaux de terrassements sont souvent considérés dans les projets d’infrastructure de transport comme un des processus les plus exigeants. En effet, des tâches séquentielles comme l’excavation, le transport, le régalage et le compactage sont fortement basées sur des équipements mécaniques lourds et des processus répétitifs, dont leur ampleur économique, étant donnée aussi le temps de réalisation. En outre, la construction actuelle est plus exigeante au niveau de la productivité et la sécurité dans les travaux de terrassements. Compte tenu du poids relatif des coûts et de la durée des travaux de terrassement dans les projets de construction d’infrastructures, l’utilisation optimale de toutes les ressources allouées à ces tâches est primordiale. Dans ce contexte les différentes phases des travaux de terrassements peuvent être considérées comme une ligne de production basée sur les ressources (équipement mécanique) et les relations de dépendance entre les tâches séquentielles et donc être susceptible d’optimisation. Jusqu’à présent, le développement des technologies de l’information, comme les bases de données, l’intelligence artificielle et la recherche opérationnelle, a donné lieu à l’émergence de plusieurs technologies applicables à ce bout. Parmi celles-ci, les méthodes modernes d’optimisation, tels que les algorithmes génétiques, sont mises en évidence en raison de leur fiabilité et aussi du réduit effort de calcul. Dans ce contexte, ce travail décrit un algorithme d’optimisation d’affectation de l’équipement de terrassements sur la base des approches d’optimisation modernes, tenant au compte l’idée selon laquelle les travaux de terrassement peut être considérée comme une ligne de production.(undefined

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.The authors wish to thank FCT for the financial support under the doctoral Grant SFRH/BD/71501/2010, as well as the construction company that kindly provided the real-world data. Also, we wish to thank Olaf Mersmann for kindly providing the R code for the SMS-EMOA algorithm

The role of bacteria in pine wilt disease: insights from microbiome analysis.

Pine Wilt Disease (PWD) has a significant impact on Eurasia pine forests. The microbiome of the nematode (the primary cause of the disease), its insect vector, and the host tree may be relevant for the disease mechanism. The aim of this study was to characterize these microbiomes, from three PWD-affected areas in Portugal, using Denaturing Gradient Gel Electrophoresis, 16S rRNA gene pyrosequencing, and a functional inference-based approach (PICRUSt). The bacterial community structure of the nematode was significantly different from the infected trees but closely related to the insect vector, supporting the hypothesis that the nematode microbiome might be in part inherited from the insect. Sampling location influenced mostly the tree microbiome (P < 0.05). Genes related both with plant growth promotion and phytopathogenicity were predicted for the tree microbiome. Xenobiotic degradation functions were predicted in the nematode and insect microbiomes. Phytotoxin biosynthesis was also predicted for the nematode microbiome, supporting the theory of a direct contribution of the microbiome to tree-wilting. This is the first study that simultaneously characterized the nematode, tree and insect-vector microbiomes from the same affected areas, and overall the results support the hypothesis that the PWD microbiome plays an important role in the disease's development

Characterization of Myeloid Cellular Populations in Mesenteric and Subcutaneous Adipose Tissue of Holstein-Friesian Cows

Immune cells resident in adipose tissue have important functions in local and systemic metabolic homeostasis. Nevertheless, these immune cell populations remain poorly characterized in bovines. Recently, we described diverse lymphocyte subpopulations in adipose tissue of Holstein-Friesian cows. Here, we aimed at characterising myeloid cell populations present in bovine adipose tissue using multicolour flow cytometry, cell sorting and histochemistry/immunohistochemistry. Macrophages, CD14(+)CD11b(+)MHC-II(+)CD45(+) cells, were identified in mesenteric and subcutaneous adipose tissue, though at higher proportions in the latter. Mast cells, identified as SSC-A(high)CD11b(-/+)CD14(-)MHC-II(-)CH138A(-)CD45(+) cells, were also observed in adipose tissue and found at higher proportions than macrophages in mesenteric adipose tissue. Neutrophils, presenting a CH138A(+)CD11b(+) phenotype, were also detected in mesenteric and subcutaneous adipose tissue, however, at much lower frequencies than in the blood. Our gating strategy allowed identification of eosinophils in blood but not in adipose tissue although being detected by morphological analysis at low frequencies in some animals. A population not expressing CD45 and with the CH138A(+) CD11b(-)MHC-II- phenotype, was found abundant and present at higher proportions in mesenteric than subcutaneous adipose tissue. The work reported here may be useful for further studies addressing the function of the described cells

Modulation of Leptin and Leptin Receptor Expression in Mice Acutely Infected with Neospora caninum

Neospora caninumis an apicomplexan parasite that in cattle assumes particular importance, as it is responsible for abortions reported worldwide. Leptin is an adipokine mainly secreted by adipocytes, which beside its role in maintaining metabolic homeostasis also has important effects in both innate and adaptive immunity. In previous work, we showed that mice chronically infected withN. caninumhad elevated serum leptin levels. Here, we sought to assess whether acute infection withN. caninuminfection influenced the production of this adipokine as well as leptin receptor mRNA levels. Our results show that acute infection withN. caninumled to decreased leptin serum levels and mRNA expression in adipose tissue. A decrease in leptin receptor transcript variant 1 mRNA (long isoform) and leptin receptor transcript variant 3 mRNA (one of the short isoforms) expression was also observed. An increase in the number of cells staining positive for leptin in the liver of infected mice was observed, although this increase was less marked in Interleukin (IL)-12/IL-23 p40-deficient mice. Overall, our results show thatN. caninuminfection also influences leptin production during acute infection

Salt pan brine water as a sustainable source of sulphated polysaccharides with immunostimulatory activity

Marine environments are an enormous source of materials with biological interest, such as sulphated polysaccharides, which have relevant biological activities. In this study the potential of salt pan brine water as an easily accessible source of sulphated polysaccharides was evaluated. This water revealed to have a high quantity of polymeric material, five times more than sea water, mainly composed by highly sulphated polysaccharides. Structural analysis identified a diversity of polysaccharides, namely rhamnans, fucans, mannans, xylomannans, glucuronomannans, galactans, and glucans. All these structures seem to form complexes that are resistant to the salt pan conditions along salt production. These polysaccharides showed in vitro stimulatory activity for B cells, suggesting their potential application in nutraceutical and biomedical fields. Salt pan brine water is a valuable source of environmentally friendly and low-cost available bioactive compounds prone to be exploited.publishe