Permeable road pavements, due to their open-graded design, suffer from low structural strength, restricting their use in areas with light traffic volume and low bearing capacity. To expand application of permeable road pavements, accurate simulation of stress parameters used in pavement design is essential. A 3D finite element (3D FE) model was developed using ABAQUS/CAE 2021 to simulate pavement stress responses. Utilizing a 53 cm thick permeable road pavement and a 315/80 R22.5 wheel as prototypes, the model was calibrated and validated, with its accuracy confirmed through t-test statistical analysis. Simulations of wheel speeds at 11, 15, and 22 m/s revealed significant impact on pavement depths of 3 cm and 8 cm, while minimal effects were observed at depths of 13 cm and 33 cm. Notably, stress values at a depth of 3 cm with 15 m/s speed in the open-graded asphalt concrete (OGFC) surface layer exceeded those at the speed of 11 m/s, while at a depth of 8 cm in the porous asphalt concrete (PAC) base layer, an opposite performance was observed. This may be attributed to the higher elastic modulus of the OGFC surface layer, which results in different response trends to velocity changes. Overall, lower speeds increase stress responses and prolong action times for both layers, negatively affecting pavement performance. Increasing the moduli of layers is recommended for new permeable road pavements for low-speed traffic. Furthermore, considering the effects of heavy loads and changes in wheel speed, the recommended design depth for permeable road pavement is 30 cm. These conclusions provide a reference for the design of permeable road pavements to address climate change and improve performance.

With the advancement of building digitalization and safety management, Working-at-Heights presents challenges due to its complex scenarios and dispersed regulations, making it difficult for on-site personnel to quickly and accurately query and assess compliance. While general-purpose large language models possess text capabilities, they lack professional knowledge adaptation, are not updated in a timely manner, and provide answers without sufficient evidence, thus failing to directly meet the safety and compliance requirements of Working-at-Heights. This paper deploys a localized retrieval-augmented generation (RAG) system based on RAGFlow and Docker. Addressing the safety consultation needs of Working-at-Heights, it transforms regulations and standards into a vector knowledge base. The system integrates keyword and semantic retrieval for precise positioning and aims to reduce hallucinations through source tracing and prompt constraints. Simultaneously, it connects to online retrieval to supplement the latest regulations, achieving a dual-track question–answering (Q&A) mechanism of “local priority, online supplementation.” A typical scenario test set of 100 questions was constructed to evaluate three system configurations across multiple dimensions, including accuracy, traceability, timeliness, and latency. The results show that faithfulness scores increased from 0.45 for the pure large model to 0.91 for local RAG and 0.97 for full-functionality integration, while context recall improved from 0.78 to 0.93 between the latter two configurations. However, response latency also increased from 2.1 s to 4.5 s and 6.8 s, respectively. These findings indicate that local RAG provides more reliable and traceable responses to basic compliance inquiries, whereas full-functionality integration offers superior performance in dynamic information supplementation and handling of unsafe requests. Overall, the integrated approach demonstrates potential for supporting on-site safety compliance consultation and risk assessment, providing a foundation for intelligent applications in construction safety management.

Thermal printing is a prevalent method due to its advantages of rapid output, cost effectiveness, and ease of use. However, the quality of thermal printing is influenced by the printing speed, the temperature, and the concentration and characteristics of the materials. This research employs a BP neural network to forecast print quality, utilizing two activation functions. The findings indicate that a dual-layer hidden configuration utilizing the GeLU activation function yields a lower root-mean-square error (RMSE). The optimal configuration identified consists of six neurons in the first hidden layer and three neurons in the second hidden layer. To enhance the predictive performance, a PSO algorithm was integrated with the PSO-BP model to refine the parameter selection, which included ambient temperature, printing speed, and printing concentration, with iterative training and validation conducted via the gradient descent algorithm. The PSO-BP network achieved an MAE of 0.1108, an RMSE of 0.145, an MSE of 0.021, and an R² value of 0.9916 in predicting print quality. These results substantiate the stability and reliability of the neural network model developed with the PSO algorithm. Further validation with ten sets of test samples demonstrated that the model attained an average absolute error of 2.77% in print quality predictions, indicating robust generalization capabilities and precise forecasting.