@article{4609,
  author = {Guoqiang Li, Yinfa Wang},
  title = {An Evaluation Network Model for Transport Development Risk Assessment},
  journal = {Digital Signal Processing and Artificial Intelligence for Automatic Learning},
  year = {2025},
  volume = {4},
  number = {4},
  doi = {https://doi.org/10.6025/dspaial/2025/4/4/155-161},
  url = {https://www.dline.info/dspai/fulltext/v4n4/dspaiv4n4_2.pdf},
  abstract = {The paper proposes an integrated safety assessment algorithm for road construction by combining the Analytic
Hierarchy Process (AHP) and Back Propagation (BP) Neural Network. Traditional safety evaluation methods,
such as fuzzy comprehensive evaluation, lack adaptability to dynamic environmental changes and struggle
to update weights in real time. To address these limitations, the authors leverage AHP for systematic
determination of indicator weights and the BP neural network for its strong nonlinear modeling, self learning,
and adaptive capabilities. Four key safety indicators geological disasters, hazardous sources, hidden dangers,
and compliance with safety standards are selected for evaluation. The AHP method quantifies their relative
importance using expert scoring and pairwise comparisons. At the same time, the BP neural network processes
input data through a three-layer architecture to compute a safety risk coefficient. This coefficient maps to one
of five predefined safety risk levels, enabling practical risk categorization. Experimental validation on a real
infrastructure project demonstrates that the integrated algorithm significantly outperforms conventional
approaches, achieving an average relative error of only 0.004 compared to 0.0468 for traditional methods.
Results confirm the model’s high accuracy, reliability, and effectiveness in predicting safety risks. Despite its
advantages, the authors acknowledge remaining limitations and call for further refinement to better align
with public safety expectations. The study contributes a robust, data driven framework for improving safety
management in complex road construction environments.},
}