Journal• ISSN: 0274-9696
Cost engineering
About: Cost engineering is an academic journal. The journal publishes majorly in the area(s): Project management & Project management triangle. It has an ISSN identifier of 0274-9696. Over the lifetime, 206 publications have been published receiving 2360 citations.
Topics: Project management, Project management triangle, Cost contingency, Cost estimate, Schedule (project management)
Papers
Journal Article•
TL;DR: In this article, a quantitative approach to construction risk management through an analytic hierarchy process (AHP) and decision tree analysis is presented, which demonstrates the project management effectiveness of using AHP and DTA.
Abstract: Time, cost and quality achievements on large-scale construction projects are uncertain because of technological constraints, involvement of many stakeholders, long durations, large capital requirements and improper scope definitions. Projects that are exposed to such an uncertain environment can effectively be managed with the application of risk management throughout the project life cycle. Risk is by nature subjective. However, managing risk subjectively poses the danger of non-achievement of project goals. Moreover, risk analysis of the overall project also poses the danger of developing inappropriate responses. This article demonstrates a quantitative approach to construction risk management through an analytic hierarchy process (AHP) and decision tree analysis. The entire project is classified to form a few work packages. With the involvement of project stakeholders, risky work packages are identified. As all the risk factors are identified, their effects are quantified by determining probability (using AHP) and severity (guess estimate). Various alternative responses are generated, listing the cost implications of mitigating the quantified risks. The expected monetary values are derived for each alternative in a decision tree framework and subsequent probability analysis helps to make the right decision in managing risks. In this article, the entire methodology is explained by using a case application of a cross-country petroleum pipeline project in India. The case study demonstrates the project management effectiveness of using AHP and DTA.
156 citations
Journal Article•
TL;DR: In this article, different motivational theories, addressing how one can improve labor productivity with the application of these theories, are discussed in this article are: Cussin's approach or management by threat which gives an overview of the construction labor management history and was common for managing construction labor in 1950s.
Abstract: Motivation is a factor that significantly influences productivity. A higher level of motivation can result in higher productivity. This article explains different motivational theories, addressing how one can improve labor productivity with the application of these theories. The theories discussed in this article are: Cussin's approach or management by threat which gives an overview of the construction labor management history and was common for managing construction labor in 1950s. Maslow's theory or Maslow's hierarchy of needs discusses the needs of the individuals to be motivated for higher productivity. McGregor's two theories (theory X and theory Y) touches on two totally different perceptions of labor and their related management styles for guiding human energy. Expectancy theory deals with human expectations after making efforts. Herzberg's theory looks to be an extension of Maslow's hierarchy of needs and expectancy theory. Wherever possible examples are presented to show where an intentional or unintentional use of motivation theories has led to improvement in productivity. With the help of these theories one can learn how to motivate construction crews for higher productivity. Besides better management practices in all levels of organizations playing important role, this article will also discusses the role of construction managers and construction management in terms of helping to motivate better productivity.
104 citations
Journal Article•
TL;DR: An overview of risk management, its concepts, components, and the associated terminology and methodology, together with different views on how risk management integrates into project management is provided in this paper, where the difference between a risk and an issue is demonstrated as are the timing mistakes often made by project teams regarding risk management.
Abstract: Risk management is often thought of in a negative light. However, it can also be thought of as a means to aid in seizing an opportunity and not just avoiding an unfavorable outcome. Risk management is in our everyday lives, whether we consciously think of it in these terms or not. This article serves to provide an overview of risk management, its concepts, components, and the associated terminology and methodology, together with different views on how risk management integrates into project management. The difference between a risk and an issue is demonstrated as are the timing mistakes often made by project teams regarding risk management.
76 citations
Journal Article•
TL;DR: A neural network method was applied to the cost estimation of timber bridges to illustrate the technique and a step-by-step validation is presented to make it easy to understand the application of neural networks to this estimation process.
Abstract: Neural network models, or more simply {open_quotes}neural nets,{close_quotes} have great potential application in speech and image recognition. They also have great potential for cost estimating. Neural networks are particularly effective for complex estimation where the relationship between the output and the input cannot be expressed by simple mathematic relationships. A neural network method was applied to the cost estimation of timber bridges to illustrate the technique. The results of the neural network method were evaluated by the coefficient of determination, The R square value for the key input variables. A comparison of the neural network results and the standard linear regression results was performed upon the timber bridge data. A step-by-step validation is presented to make it easy to understand the application of neural networks to this estimation process. The input is propagated from the input through each layer until an output is generated. The output is compared with the desired output and the error is distributed for each node in the outer layer. The error is transmitted backward (thus the phase {open_quotes}back propagation{close_quotes}) from the output layer to the intermediate layers and then to the input layer. Based upon the errors, the weights are adjusted and the procedure ismore » repeated. The number of training cycles is 15,000 to 50,000 for simple networks, but this usually takes only a few minutes on a personal computer. 7 refs., 4 figs., 11 tabs.« less
62 citations
Journal Article•
TL;DR: Some recent applications of parametric estimating in analyzing cost data and making predictions are discussed and the potential application of neural networks to estimating problems is investigated.
Abstract: Cost estimating is essentially a computational process that attempts to predict the final cost of a future project, even though not all of the parameters and conditions are known when the cost estimate is prepared. In general, estimating methods vary considerably, depending upon the available information, the nature of the project, and the time available to prepare the estimate. In this article, we discuss some recent applications of parametric estimating in analyzing cost data and making predictions. We also investigate the potential application of neural networks to estimating problems. Both of these methodologies use a parameter-based approach in modeling cost. However, the computational techniques used to analyze cost data and produce results are significantly different. As an illustration, we provide a numerical example that may be used to compare the performance of parametric estimating and neural networks. 8 refs., 3 figs., 3 tabs.
59 citations
Related Journals (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2009 | 26 |
| 2008 | 23 |
| 2007 | 20 |
| 2006 | 29 |
| 2005 | 25 |
| 2004 | 34 |