Conceptual cost estimations using neuro-fuzzy and multi-factor evaluation methods for building projects

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, fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (2) 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.