6 resultados para major infrastructure and construction projects

em Universidade do Minho


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Changes in population age structure are a major concern and represent a priority in the agendas and policies of the developed world, which are demanding for renewed models of social and healthcare as well as assistance services to the elderly population. Studies indicate that as far as possible these types of services should desirably be provided at the user’s home, and that ICT-based solutions can have tremendous impact on the delivery of new services. This paper highlight and discusses some of the main results of a project undertaken in a Portuguese Municipality that demonstrates the potential contribution of an e-Marketplace of care and assistance services to the well-being of elderly people. Studies undertaken allowed identifying the main services that should be provided by such e-Marketplace (termed GuiMarket), the relevance that the population grant to this platform and, conversely, the fact that the Digital Divide phenomena influences the potential utilization of this project (and alike projects). The findings support that there is a strong relation between age and qualifications, and between access to ICT and the intended use of GuiMarket.

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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.

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During the recent years followed by the Global Financial Crisis (GFC), most of business and industries around the globe have been hardly hit to the limit that it still struggling to survive, suffering from the crisis financial consequences. For instance, in the construction industry; many construction projects have been suspended or totally cancelled. Nevertheless, among this dilemma, a call has been raised to use the sustainable practices to mitigate the effects of the GFC on construction industry. For the first look, it seems that there is contradiction since the sustainable solutions are often associated with an increase in the initial cost, undoubtedly, the sustainable practices have many advantages in both economic and environment aspects, however, the question which needs to be addressed here is, to what extent using such sustainable practices can mitigate the negative effects of the economic downturn on construction industry. Therefore, it is a challenging argument for using such sustainable construction from its economic perspective, however, this paper is aiming to present the economical benefits of sustainable practices in construction industry, and trying to clear the doubt of the high initial costs of the sustainable construction through studying the life cycle benefit of green building.

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Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

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Dissertação de mestrado integrado em Engenharia Civil

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Dissertação de mestrado integrado in Civil Engineering