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Bernold, L E, Lorenc, S J and Davis, M L (2001) Technological Intervention to Eliminate Back Injury Risks for Nailing. Journal of Construction Engineering and Management, 127(03), 245–50.
Chang, L and Chen, P (2001) BOT Financial Model: Taiwan High Speed Rail Case. Journal of Construction Engineering and Management, 127(03), 214–22.
El-Rayes, K (2001) Object-Oriented Model for Repetitive Construction Scheduling. Journal of Construction Engineering and Management, 127(03), 199–205.
El-Razek, M E A and Basha, I M (2001) Constructability Improvement of Bridges Using Stepping Formwork. Journal of Construction Engineering and Management, 127(03), 206–13.
Hegazy, T and Wassef, N (2001) Cost Optimization in Projects with Repetitive Nonserial Activities. Journal of Construction Engineering and Management, 127(03), 183–91.
Hiyassat, M A S (2001) Applying Modified Minimum Moment Method to Multiple Resource Leveling. Journal of Construction Engineering and Management, 127(03), 192–8.
Kangari, R and Bakheet, M (2001) Construction Surety Bonding. Journal of Construction Engineering and Management, 127(03), 232–8.
Mitropoulos, P and Howell, G (2001) Model for Understanding, Preventing, and Resolving Project Disputes. Journal of Construction Engineering and Management, 127(03), 223–31.
Moua, B and Russell, J S (2001) Comparison of Two Maintainability Programs. Journal of Construction Engineering and Management, 127(03), 239–44.
Oberlender, G D and Trost, S M (2001) Predicting Accuracy of Early Cost Estimates Based on Estimate Quality. Journal of Construction Engineering and Management, 127(03), 173–82.
- Type: Journal Article
- Keywords:
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)0733-9364(2001)127:3(173)
- Abstract:
The accuracy of an estimate is measured by how well the estimated cost compares to the actual total installed cost. The accuracy of an early estimate depends on four determinants: (1) who was involved in preparing the estimate; (2) how the estimate was prepared; (3) what was known about the project; and (4) other factors considered while preparing the estimate. This paper presents results of a research effort that developed an estimate scoring system to measure the impact of these four determinants on estimate accuracy. The estimate scoring system consists of 45 elements and is organized into 4 divisions. Data were collected from 67 projects, representing $5.6 billion in total installed costs, and used to correlate the estimate scores with estimated versus actual costs. Statistical analyses determined the relative influence of the 45 elements, based on collected project data. The results showed a significant correlation between the estimate score and the accuracy of the estimate. Computer software, the Estimate Score Program (ESP), was developed to automate the scoring procedure, assess estimate accuracy, and predict contingency, based on historical cost data. The estimator can enter the base estimate into ESP and then rate the estimate, relative to each of the 45 elements. ESP automatically calculates the estimate score, as the user rates each element. The user can query the ESP historical database to view the estimate scores and estimate accuracy of similar projects. A cumulative probability S-curve, generated by ESP, is based on projects selected in the query and the estimate score of interest. The user can also predict the cost range—upper and lower limits—of a desired confidence level. ESP can be used to “check” the amount of contingency determined by other methods, as well as a method of predicting its own contingency.