Tunnel linings require routine inspection as they have a big impact on a tunnel’s safety and longevity. In this study, the convolutional neural network was utilized to develop the MFF-YOLO model. To improve feature learning efficiency, a multi-scale feature fusion network was constructed within the neck network. Additionally, a reweighted screening method was devised at the prediction stage to address the problem of duplicate detection frames. Moreover, the loss function was adjusted to maximize the effectiveness of model training and improve its overall performance. The results show that the model has a recall and accuracy that are 7.1% and 6.0% greater than those of the YOLOv5 model, reaching 89.5% and 89.4%, respectively, as well as the ability to reliably identify targets that the previous model error detection and miss detection. The MFF-YOLO model improves tunnel lining detection performance generally.

As clean renewable energy with strong advantages, hydropower plays an extremely important role in promoting green development and energy allocation patterns. Hydropower project construction is characterized by long duration, large scale, high cost, many participants, and complex construction conditions, and is closely related to the economy, society, and ecological environment, and its construction management mode and construction risk management have become the focus of extensive attention from all walks of life. In this paper, the risk evaluation index system of hydropower engineering EPC project is constructed, and the linear weighted combination method is introduced to determine the comprehensive weights based on the calculation of weights by sequential relationship method and entropy weight method, and the improved fuzzy normal distribution is introduced as the subordinate function distribution of fuzzy evaluation level based on DS evidence theory and fuzzy theory. The risk evaluation model of a hydropower engineering EPC project is also established. Meanwhile, the model was analyzed with hydropower project examples to verify the accuracy and practicality of the model, which can guide hydropower project stakeholders to manage hydropower project risks comprehensively, collaboratively, and efficiently, and provide decision support for hydropower project construction risk management.

One of the greatest engineering feats in history is the construction of tunnels, and the management of tunnel safety depends heavily on the detection of tunnel defects. However, the real-time, portability, and accuracy issues with the present tunnel defect detection technique still exist. The study improves the traditional defect detection technology based on the knowledge distillation algorithm, the depth pooling residual structure is designed in the teacher network to enhance the ability to extract target features. Next, the MobileNetv3 lightweight network is built into the student network to reduce the number and volume of model parameters. The lightweight model is then trained in terms of both features and outputs using a multidimensional knowledge distillation approach. By processing the tunnel radar detection photos, the dataset is created. The experimental findings demonstrate that the multidimensional knowledge distillation approach greatly increases the detection efficiency: the number of parameters is decreased by 81.4%, from 16.03 MB to 2.98 MB, while the accuracy is improved by 2.5%, from 83.4% to 85.9%.