Construction Cost Estimation in Data-Poor Areas Using Grasshopper Optimization Algorithm-Guided Multi-Layer Perceptron and Transfer Learning

Accurate construction cost estimation is crucial for completing projects within the planned timeframe and budget. Using machine learning methods to predict construction costs has become a new trend. However, machine learning methods typically require a large amount of data for model training, which makes it particularly challenging in data-poor areas. This paper proposes a novel method, Grasshopper Optimization Algorithm-Guided Multi-Layer Perceptron with Transfer Learning (GOA-MLP-TL), specifically designed for construction cost estimation in data-poor areas. GOA-MLP-TL utilizes the global optimal search capability of the GOA to optimize the parameters of the MLP network. Additionally, an adaptation layer is added into the MLP network, using the Maximum Mean Discrepancy (MMD) measure as a regularization to bridge the gap between the source and target domains. The GOA-MLP-TL can effectively leverage the model trained on data-rich area, and transfer the knowledge to adapt the model suitable for data-poor areas. The proposed approach is verified on two datasets from different areas, and the experimental result shows that, compared to the traditional machine learning method MLP and GOA-MLP without transfer learning, the correlation coefficient (R2) of the proposed GOA-MLP-TL is improved by 12.05% and 6.90%, respectively. This demonstrate the effectiveness of GOA-MLP-TL for the construction cost estimation task in the data-poor area.

@article{Sha2024,
  title     = {Construction Cost Estimation in Data-Poor Areas Using Grasshopper Optimization Algorithm-Guided Multi-Layer Perceptron and Transfer Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xuan Sha and Guoqing Dong and Xiaolei Li and Juan Sheng},
  doi       = {10.14569/IJACSA.2024.01507129},
  url       = {https://doi.org/10.14569/IJACSA.2024.01507129}
}