A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Stochastic Conceptual Cost Estimating of Highway Projects to Communicate Uncertainty Using Bootstrap Sampling
Conceptual cost estimating is typically completed early in the project lifecycle when little design work has been completed. Because little information is known at this early stage, the estimate usually deviates substantially from the actual construction cost. When expressed as a deterministic value, an estimate often leads to a false inference of accuracy by those not familiar with the vagaries of conceptual cost estimating, making it difficult for an agency to explain cost growth. A stochastic conceptual estimate allows an agency to produce a probability distribution of the likely construction costs and address the level of confidence in a given estimate. Named probability distributions are readily available for developing a stochastic estimate in a great deal of commercial software. However, instead of fitting available distributions, this research generates an empirical distribution to express a cost estimate range. Creating empirical distributions eliminates assumptions required for selecting named distributions. The stochastic data-driven model developed in this paper combines artificial neural networks and bootstrap sampling in a case study of 189 highway projects to train and test the estimating model.
Stochastic Conceptual Cost Estimating of Highway Projects to Communicate Uncertainty Using Bootstrap Sampling
Conceptual cost estimating is typically completed early in the project lifecycle when little design work has been completed. Because little information is known at this early stage, the estimate usually deviates substantially from the actual construction cost. When expressed as a deterministic value, an estimate often leads to a false inference of accuracy by those not familiar with the vagaries of conceptual cost estimating, making it difficult for an agency to explain cost growth. A stochastic conceptual estimate allows an agency to produce a probability distribution of the likely construction costs and address the level of confidence in a given estimate. Named probability distributions are readily available for developing a stochastic estimate in a great deal of commercial software. However, instead of fitting available distributions, this research generates an empirical distribution to express a cost estimate range. Creating empirical distributions eliminates assumptions required for selecting named distributions. The stochastic data-driven model developed in this paper combines artificial neural networks and bootstrap sampling in a case study of 189 highway projects to train and test the estimating model.
Stochastic Conceptual Cost Estimating of Highway Projects to Communicate Uncertainty Using Bootstrap Sampling
Gardner, Brendon J. (author) / Gransberg, Douglas D. (author) / Rueda, Jorge A. (author)
2016-10-21
Article (Journal)
Electronic Resource
Unknown
New Approach to Developing Conceptual Cost Estimates for Highway Projects
| British Library Online Contents | 2011
New Approach to Developing Conceptual Cost Estimates for Highway Projects
| Online Contents | 2011
Estimating Cost Contingency for Highway Construction Projects Using Analytic Hierarchy Processes
| BASE | 2014
A conceptual cost estimating computer system for building projects
| British Library Conference Proceedings | 2000