925 resultados para Project Life Cycle


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With the inevitable increase in size and complexity of construction projects, the need for proper control is increasing. Considering the fact that each project strives for excellence, numerous studies have been conducted over the years to measure performance and investigate factors that are really critical towards project success. Earned Value Management is a project performance evaluation technique which enables industry professionals to closely monitor project performance in both time and cost .The aim of this paper is to examine the result of proper Earned Value Management (EVM) implementation on different project life cycle (PLC) and validate the impact on project success.

The study investigates different success factors in construction industry with special focus on previous researchers’ work which studied the importance of cost control in project success especially in fragmented industry like construction, followed up with three different case studies to analyze the positive impact of EVM implementation on construction projects. Furthermore, for data triangulation purpose, case study analysis will be supported by interviews with specialists working in the UAE construction industry to cross check the outcomes of previous researches.
The research shows that EVM application on cost control in construction projects is not only a crucial management task which is a key to the success of the business but also its influence on project success depends on the time of implementation. It requires a number of up-to-date input data consistently throughout the construction phase. Assigning the right budgets, calculating accurate estimates and monitoring actual costs throughout different project stages are the three main drivers of an effective control through PLC staring from inception stage till completion. EVM proved to be of vital importance due to alarming escalation of construction costs which needs to be especially monitored and controlled. Senior management support and availability of professional staff to execute cost control systems are key factors towards successful implementation

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Indoor air pollution from combustion of solid fuels is the fifth leading contributor to disease burden in low-income countries. This, and potential to reduce environmental impacts, has resulted in emphasis on use of improved stoves. However, many efforts have failed to meet expectations and effective coverage remains limited. A disconnect exists between technologies, delivery methods, and long-term adoption. The purpose of this research is to develop a framework to increase long-term success of improved stove projects. The framework integrates sustainability factors into the project life-cycle. It is represented as a matrix and checklist which encourages consideration of social, economic, and environmental issues in projects. A case study was conducted in which an improved stove project in Honduras was evaluated using the framework. Results indicated areas of strength and weakness in project execution and highlighted potential improvements for future projects. The framework is also useful as a guide during project planning.

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Information management is a key aspect of successful construction projects. Having inaccurate measurements and conflicting data can lead to costly mistakes, and vague quantities can ruin estimates and schedules. Building information modeling (BIM) augments a 3D model with a wide variety of information, which reduces many sources of error and can detect conflicts before they occur. Because new technology is often more complex, it can be difficult to effectively integrate it with existing business practices. In this paper, we will answer two questions: How can BIM add value to construction projects? and What lessons can be learned from other companies that use BIM or other similar technology? Previous research focused on the technology as if it were simply a tool, observing problems that occurred while integrating new technology into existing practices. Our research instead looks at the flow of information through a company and its network, seeing all the actors as part of an ecosystem. Building upon this idea, we proposed the metaphor of an information supply chain to illustrate how BIM can add value to a construction project. This paper then concludes with two case studies. The first case study illustrates a failure in the flow of information that could have prevented by using BIM. The second case study profiles a leading design firm that has used BIM products for many years and shows the real benefits of using this program.

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The objective of this research is to investigate the consequences of sharing or using information generated in one phase of the project to subsequent life cycle phases. Sometimes the assumptions supporting the information change, and at other times the context within which the information was created changes in a way that causes the information to become invalid. Often these inconsistencies are not discovered till the damage has occurred. This study builds on previous research that proposed a framework based on the metaphor of ‘ecosystems’ to model such inconsistencies in the 'supply chain' of life cycle information (Brokaw and Mukherjee, 2012). The outcome of such inconsistencies often results in litigation. Therefore, this paper studies a set of legal cases that resulted from inconsistencies in life cycle information, within the ecosystems framework. For each project, the errant information type, creator and user of the information and their relationship, time of creation and usage of the information in the life cycle of the project are investigated to assess the causes of failure of precise and accurate information flow as well as the impact of such failures in later stages of the project. The analysis shows that the misleading information is mostly due to lack of collaboration. Besides, in all the studied cases, lack of compliance checking, imprecise data and insufficient clarifications hinder accurate and smooth flow of information. The paper presents findings regarding the bottleneck of the information flow process during the design, construction and post construction phases. It also highlights the role of collaboration as well as information integration and management during the project life cycle and presents a baseline for improvement in information supply chain through the life cycle of the project.

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Queensland Department of Main Roads, Australia, spends approximately A$ 1 billion annually for road infrastructure asset management. To effectively manage road infrastructure, firstly road agencies not only need to optimise the expenditure for data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. This paper presents the results of case studies in using the probability-based method for an integrated approach (i.e. assessing optimal costs of pavement strength data collection; calibrating deterioration prediction models that suit local condition and assessing risk-adjusted budget estimates for road maintenance and rehabilitation for assessing life-cycle budget estimates). The probability concept is opening the path to having the means to predict life-cycle maintenance and rehabilitation budget estimates that have a known probability of success (e.g. produce budget estimates for a project life-cycle cost with 5% probability of exceeding). The paper also presents a conceptual decision-making framework in the form of risk mapping in which the life-cycle budget/cost investment could be considered in conjunction with social, environmental and political issues.

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Understanding the differences between the temporal and physical aspects of the building life cycle is an essential ingredient in the development of Building Environmental Assessment (BEA) tools. This paper illustrates a theoretical Life Cycle Assessment (LCA) framework aligning temporal decision-making with that of material flows over building development phases. It was derived during development of a prototype commercial building design tool that was based on a 3-D CAD information and communications technology (ICT) platform and LCA software. The framework aligns stakeholder BEA needs and the decision-making process against characteristics of leading green building tools. The paper explores related integration of BEA tool development applications on such ICT platforms. Key framework modules are depicted and practical examples for BEA are provided for: • Definition of investment and service goals at project initiation; • Design integrated to avoid overlaps/confusion over the project life cycle; • Detailing the supply chain considering building life cycle impacts; • Delivery of quality metrics for occupancy post-construction/handover; • Deconstruction profiling at end of life to facilitate recovery.

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With increasing pressure to provide environmentally responsible infrastructure products and services, stakeholders are putting significant foci on the early identification of financial viability and outcome of infrastructure projects. Traditionally, there has been an imbalance between sustainable measures and project budget. On one hand, the industry tends to employ the first-cost mentality and approach to developing infrastructure projects. On the other, environmental experts and technology innovators often push for the ultimately green products and systems without much of a concern for cost. This situation is being quickly changed as the industry is under pressure to continue to return profit, while better adapting to current and emerging global issues of sustainability. For the infrastructure sector to contribute to sustainable development, it will need to increase value and efficiency. Thus, there is a great need for tools that will enable decision makers evaluate competing initiatives and identify the most sustainable approaches to procuring infrastructure projects. In order to ensure that these objectives are achieved, the concept of life-cycle costing analysis (LCCA) will play significant roles in the economics of an infrastructure project. Recently, a few research initiatives have applied the LCCA models for road infrastructure that focused on the traditional economics of a project. There is little coverage of life-cycle costing as a method to evaluate the criteria and assess the economic implications of pursuing sustainability in road infrastructure projects. To rectify this problem, this paper reviews the theoretical basis of previous LCCA models before discussing their inability to determinate the sustainability indicators in road infrastructure project. It then introduces an on-going research aimed at developing a new model to integrate the various new cost elements based on the sustainability indicators with the traditional and proven LCCA approach. It is expected that the research will generate a working model for sustainability based life-cycle cost analysis.

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The mining/quarrying industry is a sector of industry where there are very few Life Cycle Assessment (LCA) tools, and where the role of LCA has been poorly investigated. A key issue is the integration of three inter-dependent life cycles: Project, Asset and Product. Given the unique features of mining LCAs, this Note from the Field presents a common methodology implemented within the Sustainable Aggregates Resource Management (SARMa) Project (www.sarmaproject.eu) in order to boost adoption of LCA in the aggregate industry in South Eastern Europe. The proposed methodology emphasises the importance of resource efficiency and recycling in the context of a Sustainable Supply Mix of aggregates for the construction industry. Through its adoption, aggregate producers, recyclers, and governmental planners would gain confidence with LCA tools and conduct consistent and meaningful life cycle analyses of natural and recycled aggregates. © 2011 Elsevier Ltd. All rights reserved.

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Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

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The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.