Construction and Characteristics of an Engineering Economic Risk Management Platform Based on the BO-GBM Model

Economic risk control is pivotal to the success of engineering projects. Traditional risk assessment methods often fall short in handling the high-dimensional, nonlinear, and strongly correlated risk factors prevalent in modern large-scale projects. To address these limitations, this study constructs an engineering economic risk management platform based on the BO-GBM model, which integrates Bayesian Optimization (BO) with a Gradient Boosting Machine (GBM). The platform employs a systematically constructed four-dimensional feature system encompassing 28 indicators across project ontology, market environment, execution process, and risk association dimensions. A rolling time window strategy is adopted for dynamic model training. Experimental validation on a dataset of 327 projects demonstrates the superior performance of the BO-GBM model: for classification tasks, it achieves an AUC of 0.927 and a recall rate of 91.3%, outperforming the standard GBM by 17.5 percentage points in recall; for regression tasks (cost deviation prediction), it attains an RMSE of 83,200 RMB and reduces the MAPE to 9.7%, surpassing mainstream baseline models. The platform's layered architecture (data, model, service, application layers) enables efficient risk identification and early warning: the time required for risk identification in large projects is drastically reduced from 42.6 hours to 0.52 hours, representing an 81.9-fold efficiency gain; the average single prediction response time is below 127 milliseconds, with a P95 response time of 427 milliseconds under 500 concurrent users; the early warning accuracy reaches 72.5%, with high-risk warnings issued up to 28 days in advance for cost risks and 42 days for schedule risks.

@article{Wang2025,
  title     = {Construction and Characteristics of an Engineering Economic Risk Management Platform Based on the BO-GBM Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {10},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Chaojian Wang and Die Liu},
  doi       = {10.14569/IJACSA.2025.0161010},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161010}
}