Abstract
ABSTRACT Accurate conceptual cost estimation is vital in construction project management for effective feasibility studies before project initiation. Relying on rough experiential estimates can lead to significant errors and constrain bid prices, risking financial losses. Machine learning (ML) offers a way to bypass expert input and manual quantity surveying, addressing the challenge of inadequate initial estimation data. However, a demand-oriented conceptual cost estimation model based on ML is lacking in the preliminary design phase to assess cost-influencing factors comprehensively. This research develops an optimal model by comparing the conceptual cost estimation performance of hybrid Dung Beetle Optimizer (DBO) + Back-Propagation Neural Network (BPNN), Genetic Algorithm (GA) + BPNN, Particle Swarm Optimization (PSO) + BPNN, and single BPNN. First, the 20 key input variables affecting conceptual cost estimation were determined by the SHAP and correlation matrix together; Second, after the simulation experiment by using a dataset of 117 general building projects in MATLAB, the DBO+BPNN model with 12 hidden layer neurons achieves the best performance in cost estimation by the performance comparison based on score maker methods with 2 correlation metrics and 5 accuracy metrics. Importantly, implementing this model can provide decision-makers with reliable conceptual cost information, enhancing project success likelihood.
Published Version
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