Resource Schedule of Concrete Fish Pond Construction Using Network Analysis

In the construction of building, preparation of bid, maintenance and planning of oil refinery and preparation for agricultural activities, there is a need to know the completion days of the project without delay and the earliest time and the latest time for which each activity will take. It was based on this that we decide to analyze the construction of concrete fish pond using Network Analysis through the use of Critical Path Method (CPM) and Program Evaluation Review Technique (PERT). Sixty-four days was arrived at for the completion of the construction using CPM while sixty-eight days with 99% probability was arrived at using PERT method. In deciding which of the method is best suitable for the construction of the fish pond, PERT serve as the best method due to the fact that it considers the Pessimistic Time (longest time possible and can be seen as usual delay) and Optimistic Time (shortest time possible if things go perfectly) as well as the probability [which is 99%] of completing the task within a specific time. The result established some useful facts for researchers in this area as well as managers of industry in carrying out their study from the feasibility stage to the other stages so as to have a good practical target towards the completion of the project as planned. Keyword: Network Analysis, Critical Path Method, Program Evaluation Review Technique, Pessimistic Time, Optimistic Time and Probability DOI: 10.7176/JMCR/57-04 Publication date:June 30th 201

Optimal Design of Functionally Graded Parts

Several additive manufacturing processes are capable of fabricating three-dimensional parts with complex distribution of material composition to achieve desired local properties and functions. This unique advantage could be exploited by developing and implementing methodologies capable of optimizing the distribution of material composition for one-, two-, and three-dimensional parts. This paper is the first effort to review the research works on developing these methods. The underlying components (i.e., building blocks) in all of these methods include the homogenization approach, material representation technique, finite element analysis approach, and the choice of optimization algorithm. The overall performance of each method mainly depends on these components and how they work together. For instance, if a simple one-dimensional analytical equation is used to represent the material composition distribution, the finite element analysis and optimization would be straightforward, but it does not have the versatility of a method which uses an advanced representation technique. In this paper, evolution of these methods is followed; noteworthy homogenization approaches, representation techniques, finite element analysis approaches, and optimization algorithms used/developed in these studies are described; and most powerful design methods are identified, explained, and compared against each other. Also, manufacturing techniques, capable of producing functionally graded materials with complex material distribution, are reviewed; and future research directions are discussed

Energy Management Systems for Smart Electric Railway Networks: A Methodological Review

Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO2 emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Transportation planning via location-based social networking data : exploring many-to-many connections

textToday’s metropolitan areas see changes in populations and land development occurring at faster rates than transportation planning can be updated. This dissertation explores the use of a new dataset from the location-based social networking spectrum to analyze origin-destination travel demand within Austin, TX. A detailed exploration of the proposed data source is conducted to determine its overall capabilities with respect to the Austin area demographics. A new methodology is proposed for the creation of origin-destination matrices using a peer-to-peer modeling structure. This methodology is compared against a previously examined and more traditional approach, the doubly-constrained gravity model, to understand the capabilities of both models with various friction functions. Each method is examined within the constructs of the study area’s existing origin-destination matrix by examining the coincidence ratios, mean errors, mean absolute errors, frequency ratios, swap ratios, trip length distributions, zonal trip generation and attraction heat maps, and zonal origin-destination flow patterns. Through multiple measures, this dissertation provides initial interpretations of the robust Foursquare data collected for the Austin area. Based upon the data analytics performed, the Foursquare data source is shown to be capable of providing immensely detailed spatial-temporal data that can be utilized as a supplementary data source to traditional transportation planning data collection methods or in conjunction with other data sources, such as social networking platforms. The examination of the proposed peer-to-peer methodology presented within this dissertation provides a first look at the potential of many-to-many modeling for transportation planning. The peer-to-peer model was found to be superior to the doubly-constrained gravity model with respect to intrazonal trips. Furthermore, the peer-to-peer model was found to better estimate productions, attractions, and zone to zone movements when a linear function was used for long trips, and was computationally more proficient for all models examined.Civil, Architectural, and Environmental Engineerin

A Peer-to-Peer Associative Memory Network for Intelligent Information Systems

The paper describes a highly-scalable associative memory network capable of handling multiple streams of input, which are processed and matched with the historical data (available within the network). The essence of the associative memory algorithm lies with in its highly parallel structure, which changes the emphasis from the high speed CPU based processing to network processing; capable of utilising a large number of low performance processors in a fully connected configuration. The approach is expected to facilitate the development of information systems capable of correlating multi-dimensional data inputs into human thought like constructs and thus exhibiting a level of self-awareness

Optimized assembly design for resource efficient production in a multiproduct manufacturing system

Resource efficiency is one of the greatest challenges for sustainable manufacturing. Material flow in manufacturing systems directly influences resource efficiency, financial cost and environmental impact. A framework for material flow assessment in manufacturing systems (MFAM) was applied to a complex multi-product manufacturing case study. This supported the identification of options to alter material flow through changes to the product assembly design, to improve overall resource efficiency through eliminating resource intensive changeovers. Alternative assembly designs were examined using a combination of intelligent computation techniques: k-means clustering, genetic algorithm and ant colony algorithm. This provided recommendations balancing improvement potential with extent of process modification impact