Computer methods cost estimation

The first stage toward producing an accurate estimate is to use a standard cost code for all construction projects. Table 9-45 shows a suitable numerical cost code, and Table 9-46 shows a typical alphabetical-numerical code. The cost-code system can be used throughout the estimating and construction stages for the collection of cost data by manual or computer methods. There are numerous types of fixed-capital-cost estimates, but in 1958 the American Association of Cost Engineers defined five types as follows ... 

One of the most dependably accurate methods for deriving 95% confidence intervals for cost-effectiveness ratios is the nonparametric bootstrap method. In this method, one resamples from the smdy sample and computes cost-effectiveness ratios in each of the multiple samples. To do so requires one to (1) draw a sample of size n with replacement from the empiric distribution and use it to compute a cost-effectiveness ratio (2) repeat this sampling and calculation of the ratio (by convention, at least 1000 times for confidence intervals) (3) order the repeated estimates of the ratio from lowest (best) to highest (worst) and (4) identify a 95% confidence interval from this rank-ordered distribution. The percentile method is one of the simplest means of identifying a confidence interval, but it may not be as accurate as other methods. When using 1,000... 

The solution to the problem is obtained by solving mass and energy balances to yield the quantity and state (i.e., composition, temperature, pressure) of all the streams and the utility requirements. Additional parameters for the process equipment, sufficient so that stream specifications are met and the cost of the equipment can be estimated, are calculated. The cost of equipment, raw materials, and utilities is estimated and an economic analysis is carried out. Methods of cost estimation and economic analysis are presented later in this text. This entire procedure may be repeated many times to examine modifications of the process flow sheet or to find optimal values of key process variables. Computer software can greatly simplify these repetitive calculations for the engineer. But even without the need for repetition, the software may simplify the calculations and provide detail and accuracy that would have been impossible otherwise. 

This method is a valuable tool for the estimator. The system calculates the purchased price for each piece of equipment as well as simulates (based on preprogrammed models) quantity take-offs to generate field material and field labor for installation of each equipment item as well as the entire project. Engineering, overheads, and fees are also calculated. Certain minimum information must be input to the computer for equipment costs. More data given per piece of equipment results in a more accurate cost estimate of the equipment and project as a whole. 

Analysis tasks solve material and energy balances, design equipment by short-cut methods or by computer simulation, and generate cost estimates. 

A conceptually different and relatively new example of an inferential model, motivated by human performance problems specifically, is nonlinear causal resource analysis (NCRA) [Kondraske, 1988 Vasta and Kondraske, 1994]. Quantitative task demands, in terms of performance variables that characterize the involved subsystems, are inferred from a population data set that includes measures of subsystem performance resource availabilities (e.g., speed, accuracy, etc.) and overall performance on the task in question. This method is based on the following simple concept Consider a sample of 100 people, each with a known amount of cash (e.g., a fairly even distribution from 0 to 10,000). Each person is asked to try to purchase a specific computer, the cost of which is unknown. In the subgroup that was able to make the purchase (some would not have enough cash), the individual who had the least amount of cash provides the key clue. That amount of cash availability provides an estimate of the computer s cost (i.e., the unknown value). Thus, in human performance, demand is inferred from resource availabdities. 

Advances in mathematical approaches to the semiintegration of electrochemical data allowed Saveant etal. [22,27], as well as Oldham and Mahon, who introduced the so-called extended semiintegrals [25], to minimize the above-mentioned constraints, although at the cost of increased complexity in computation methods. The problem of the computation of edge diffusion effects with reasonable computational times was elegantly solved by the introduction of partial sphere approximations [26, 29], which simplify the two-dimensional diffusion problem into an easily solved one-dimensional one. Estimation of the planar component needed for semiintegral analysis can be performed by convolutive reshaping, as described by Mahon [29]. 

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