Construction Planning And Management By U Srivastava Pdf UPD Free
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Construction Planning And Management By U Srivastava Pdf Free
Abstract:An efficient highway infrastructure network is a determining factor in promoting the socio-economic development of countries and regions. Highway planning activities are highly important because their results ensure that projects are delivered within budget, schedule, and scope. Therefore, academics and professionals have promoted various methodological and technological advances focused on improving highway planning processes, evidenced by many scientific documents that address the topic. Despite progress, few studies have focused on analyzing the state of the knowledge structure and the technological and methodological trends of the highway planning topic. Considering this gap, this study presents trends in highway planning and its knowledge structure based on a bibliometric analysis from January 2015 to September 2021. The research method is based on a bibliometric analysis composed of five main stages: (1) scope definition, (2) selection of bibliometric analysis techniques, (3) data collection, (4) bibliometric analysis execution, and (5) evidence analysis and synthesis. Information from 1703 journal papers was collected and analyzed. The findings show that the main trends of highway planning focus on life cycle analysis, computational tools, smart cities, sustainability issues, construction processes, new equipment and materials, and multi-objective optimization, among others. Thus, the findings of this study allow the reader to identify the methodological and technological trends in highway planning and their knowledge gaps to guide future studies in the field.Keywords: planning; construction; highway; road infrastructure; bibliometric analysis
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For solving a task, the selection of the type of reinforcement learning that needs to be performed is based on the space or the scope of the problem. For example, DRL is the best way for problems involving many parameters to be optimized. By contrast, derivative-free reinforcement learning is a technique that performs well for problems with limited parameters. Some of the applications of reinforcement learning are business strategy planning and robotics for industrial automation. The main drawback of Reinforcement Learning is that parameters may influence the speed of learning. Here are the main motivations for utilizing Reinforcement Learning: