Range estimation of construction costs using neural networks with bootstrap prediction intervals

Modeling of construction costs is a challenging task, as it requires representation of complex relations between factors and project costs with sparse and noisy data. In this paper, neural networks with bootstrap prediction intervals are presented for range estimation of construction costs. In the integrated approach, neural networks are used for modeling the mapping function between the factors and costs, and bootstrap method is used to quantify the level of variability included in the estimated costs. The integrated method is applied to range estimation of building projects. Two techniques; elimination of the input variables, and Bayesian regularization were implemented to improve generalization capabilities of the neural network models. The proposed modeling approach enables identification of parsimonious mapping function between the factors and cost and, provides a tool to quantify the prediction variability of the neural network models. Hence, the integrated approach presents a robust and pragmatic alternative for conceptual estimation of costs.


Contextual Information Requirements of Cost Estimators from Past Construction Projects
Kiziltas, Semiha; Akinci, Burcu (American Society of Civil Engineers (ASCE), 2009-09-01)
Past project data sources provide key information for construction cost estimators. Previous research studies show that relying only on one's own experience during estimation results in estimators' bias. Having and referring to historical databases, containing objective information on what happened in past projects, are essential for reducing estimators' biases. The first step toward development of useful project history databases is to understand what information estimators require from past projects. The ...
Hybrid particle swarm optimization algorithm for obtaining pareto front of discrete time cost trade-off problem
Aminbakhsh, Saman; Sönmez, Rifat; Department of Civil Engineering (2013)
In pursuance of decreasing costs, both the client and the contractor would strive to speed up the construction project. However, accelerating the project schedule will impose additional cost and might be profitable up to a certain limit. Paramount for construction management, analyses of this trade-off between duration and cost is hailed as the time-cost trade-off (TCT) optimization. Inadequacies of existing commercial software packages for such analyses tied with eminence of discretization, motivated devel...
Cost estimation of trackworks of light rail and metro projects
Öztürk, Erhan; Gündüz, Murat; Department of Civil Engineering (2009)
The main objective of this work is to develop models using multivariable regression and artificial neural network approaches for cost estimation of the construction costs of trackworks of light rail transit and metro projects at the early stages of the construction process in Turkey. These two approaches were applied to a data set of 16 projects by using seventeen parameters available at the early design phase. According to the results of each method, regression analysis estimated the cost of testing sample...
Conceptual quantity modeling of single span highway bridges by regression, neural networks and case based reasoning methods
Aşıkgil, Mert; Sönmez, Rifat; Department of Civil Engineering (2012)
Conceptual estimation techniques play an important role in determining the approximate costs of construction projects especially during feasibility stages. Moreover, pre-design estimates are also crucial for the contractors. With the help of the conceptual predictions companies can determine approximate project costs and can gain several advantages before tendering phase. The main objective of this thesis is to focus on modeling of quantities instead of costs and to develop quantity take-off models for pre-...
Development of high performance heuristic and meta-heuristic methods for resource optimization of large scale construction projects
Abbasi Iranagh, Mahdi; Sönmez, Rifat; Department of Civil Engineering (2015)
Despite the importance of resource optimization in construction scheduling, very little success has been achieved in solving the resource leveling problem (RLP) and resource constrained discrete time-cost trade-off problem (RCDTCTP), especially for large-scale projects. The major objective of this thesis is to design and develop new heuristic and meta-heuristic methods to achieve fast and high quality solutions for the large-scale RLP and RCDTCTP. Two different methods are presented in this thesis for the R...
Citation Formats
R. Sönmez, “Range estimation of construction costs using neural networks with bootstrap prediction intervals,” EXPERT SYSTEMS WITH APPLICATIONS, pp. 9913–9917, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48756.