Abstract
During the conceptual phase of a construction project, numerous uncertainties make accurate cost estimation challenging. This work develops a new model to calculate conceptual costs of building projects for effective cost control. The proposed model integrates four mathematical techniques (sub-models), namely, (1) the component ratios sub-model, (2) fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (3) regression sub-model, and (4) multi-factor evaluation sub-model. While the FALCON- and fmGA-based sub-model trains the historical cost data, three other sub-models assess the inputs systematically to estimate the cost of a new project. This study also closely examines the behavior of the proposed model by evaluating two modified models without considering fmGA and undertaking sensitivity analysis. Evaluation results indicate that, with the ability to more thoroughly respond to the project characteristics, the proposed model has a high probability of increasing estimation accuracies more than the three conventional methods, i.e., average unit cost, component ratios, and linear regression methods.
Highlights
Accurate cost estimation is a challenge for the project estimator during the conceptual phase of a construction project
Their work combined the fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm to build a training algorithm to deal with complicated relations among cost parameters in historical cost data, and applied a three-point cost estimation method to assess the uncertainties related to the inputs in predicting new project cost
If the training process of fast messy genetic algorithm (fmGA) continues to run for 200 generations (= 4 eras × 50 epochs), the average accuracy of the proposed model increases by 0.45% in the three projects over that running for only 52 generations
Summary
Accurate cost estimation is a challenge for the project estimator during the conceptual phase of a construction project. This study defines the conceptual phase as the phase at which approximately 30% of the design is completed, and at which cost estimate is generally considered a budget estimate or baseline cost for a construction project (Lai et al 2008; Cheng et al 2010; Petroutsatou et al 2012) This estimation challenge is difficult because only conceptual design drawings and specifications are available, and the estimations involve numerous assumptions (Asmar et al 2011). The construction industry has used several conventional conceptual cost estimation methods, such as average unit cost, cost indices, cost-capacity factors, and parametric estimation, to rapidly calculate total project cost (Barrie, Paulson 1992; Hendrickson, Au 2003; Hong et al 2011) These early cost estimations at the conceptual phase are reasonably precise ±25% (Petroutsatou et al.2012). To systematically deal with the three cost-estimation obstacles, this work proposes a new model that estimates building project conceptual costs using the component ratios, fuzzy adaptive learning control network (FALCON), fast messy genetic algorithm (fmGA), regression, and multi-factor evaluation methods
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