A thermal safety warning system was established based on a thermoregulation model to mitigate the thermal safety risk for elderly people under high-temperature conditions. Firstly, the improved model suitable for the elderly was established by replacing the physical model and adjusting the active and passive systems in the classical model. The improved model was validated using publicly available experimental data. Secondly, the model simulated the temperature changes in elderly people in high-temperature environments. Statistical analysis was used to assess the impact of various parameters on thermal safety. Finally, based on the analysis results and the improved model, a thermal safety risk warning system for elderly people was developed. The warning system was demonstrated through a case study. Results indicate that the improved model accurately simulates the body temperature of elderly people, with a root mean squared error less than 0.12 ℃. Physical activity intensity significantly impacts thermal safety, with a standardized regression coefficient β larger than 0.8. As heat exposure time increases, the impact of activity intensity on thermal safety is decreased (β decreases from 0.945 to 0.806), while the influence of environmental factors is increased (β of temperature and humidity increases from 0.249 and 0.137 to 0.370 and 0.348). In the case study, the safe duration of continuous activities for the resting and working elderly people in Baoding/Hong Kong is 172/175 minutes and 108/122 minutes, respectively. The highest thermal safety risk period for elderly people on that day is between 17:00 and 18:00.

In order to explore the evolution mechanism and the best joint strategy for improvement of construction safety climate, with the help of the Mobius ring structure, a three-factor structure model of construction safety climate based on cognition-behavior-environment was constructed from three dimensions of construction workers' cognition, behavior and environment. According to the classification standard of the three-dimensional spatial structure model of construction safety climate, the grading standard of safety climate was divided. The DBN was used to study the changes of the construction safety climate with time. The results show that in terms of influencing factors, safety incentives have the greatest impact on enterprise safety climate and its evolution. In terms of dimensions, the behavioral dimension has the greatest impact. The best joint strategy to improve the construction safety climate is to strengthen the control of safety incentives, safety supervision, workers' safety response, safety consciousness and workers' learning and communication in turn.

In order to explore the effect of cognitive load on workers' hazard identification behavior，a cognitive experiment based on eye-tracking technology for construction site hazard identification was designed firstly. In this experiment，a N-digit task was introduced to increase the cognitive load. Secondly，the gaze and glance data were collected to analyze the static attention allocation，and the scanning path was processed to extract the dynamic transfer characteristics of attention. Finally，three parameters of variance review probability (RP)，transition probability (TP) and switching probability (SP) were selected as the quantitative parameter values to classify the scanning patterns of hazard identification，which explored the influence of cognitive load on hidden hazard identification from the perspective of visual behavior performance. The results show that the level of cognitive load negatively affects hazard identification performance. The subjects with high cognitive load show longer first fixation time，fewer fixation counts and saccade counts，and there is no significant difference in fixation percentage and mean fixation duration. Additionally，based on the attention characteristics，three scanning patterns are identified: sequential inspection，repeated comparison and random discovery. With the improvement of cognitive load level，subjects will pay more attention to identifying single hazard but neglect others during sequential inspection，and reduce the attention in the hazard area but still keep the fixation point quickly and frequently switching during repeated comparison，while the number and time of inspection of hazard areas are reducing simultaneously during random discovery.

In order to solve the problem of bolt breaking and losing anchor due to the influence of strong mining stress in deep mining roadway, and effectively improve the pre-tightening force of the bolt, a stress uniform bolt with high pre-tightening force was designed. The theoretical analysis, static load drawing and drop hammer impact test, numerical calculation and DIC method were used to study the load-displacement distribution characteristics of the bolt in the process of static load drawing, and the stress concentration position of the bolt was obtained. The mechanical response characteristics of the bolt under dynamic load were studied by using Ansys numerical simulation of drop hammer impact test and SHPB impact test. The results show that the thread deformation of the ordinary bolt nut decreases exponentially along the axial direction away from the extrusion surface, and the strain is concentrated in the first three circles of the thread. The stress uniformly distributed bolt can achieve thread strain coordination, and the mechanical environment is good. The change of bolt axial force is divided into a rising zone, an oscillating zone and a stable zone. When the drop hammer impulse is the same, the stress uniform distribution bolt can reduce the impact force to 64% of the original, and reduce the amplitude frequency of bolt axial force. The stress uniform distribution bolt can reduce the amplitude and frequency of the stress wave waveform. The research results have been applied to deep roadways such as Zhuji mine, Paner mine and Dingji mine. The bolt has no broken anchor phenomenon, and the control effect of the roadway surrounding rock is good.

To address the high rates of missed detections and false alarms, as well as the poor robustness in fatigue driving detection for open-pit mine truck drivers, a fatigue driving detection model for mine truck drivers (EBS-YOLO) based on the improved YOLOv8 is constructed to enhance the overall performance of fatigue detection. Firstly, YOLOv8 was used as the basic model for fatigue detection, and a small target detection layer was added to enhance the model's attention to small targets. Secondly, the bottleneck attention module (BAM) was used to improve the model performance to extract small target features, especially eye features. Finally, all cross-stage aggregation modules (C2f) in the backbone network were replaced with efficient multi-scale attention (EMA) modules, thereby effectively reducing model parameters and computational overhead to meet the requirements of a lightweight model. The results showed that the improved YOLOv8 model had a great detection effect with the accuracy, recall rate, and average detection accuracy reaching 93.6%, 93.9%, and 96.5%, respectively, and the memory size of the model was only 4.9 MB. Compared with the YOLOv8 model, the improved model can quickly and accurately identify the fatigue state of mining truck drivers, meet real-time requirements, and effectively prevent fatigue-driving accidents.

To improve the detection efficiency and automation level of detecting road surface pits and grooves in road safety inspection work, reduce the probability of traffic accidents. A road surface pit and groove hazard intelligent detection model based on an improved YOLOv5s was proposed. This method incorporated the ASFF module into the original YOLOv5s network, replaced the backbone network with the FasterNet network, and further introduced the Efficient Channel Attention (ECA) module. Ablation experiments are conducted to analyze the effect of the improved module on performance of the detection model, to verify the target detection effect, and to develop an interactive visualized detection interface. The results show that the improved model accuracy, recall rate, and average detection accuracy have increased by 4.1%, 9.9% and 5.6% respectively. Compared to the original network, the improvement is significant. It demonstrats good detection performance that meets the application requirements for automated detection of road surface pits and grooves, thereby enhancing inspection efficiency and effectively reducing traffic accidents caused by road surface pits and grooves.

In order to solve the problems of complex structure, large scale and difficulty in balancing detection accuracy and efficiency of the current forest fire detection algorithm based on deep learning, a lightweight forest fire detection algorithm based on YOLOv5s was proposed. Firstly, an optimized background difference technique was used to eliminate the interference of fire-like objects in the background image, thus reducing the time required for image analysis. Secondly, a group blending strategy was designed to optimize the conventional convolution, and an efficient channel attention (ECA) mechanism and depthwise separable convolution were incorporated into the C3 module of feature extraction, which enhanced the ability of image feature extraction and fusion and at the same time effectively reduces the number of model parameters. Then, a dynamic non-monotonic focusing mechanism was used to optimize the WIOU loss function, reducing the harmful gradients generated by low-quality samples. Finally, sufficient experimental comparisons between the proposed algorithm and other algorithms on the constructed forest fire dataset. The results show that the proposed algorithm shows good generalization in various scenarios, and the detection accuracy of the flame target can reach 86.1%, which is 2.7% higher than that of the standard YOLOv5s, and the detection speed is increased by 11.4%, which effectively reduces the fire false alarm rate and enhances the detection performance of the model.

In order to make up for the deficiency of VR technology in safety education theory support and practical effect tests, the relevant influencing factors affecting the effect of engineering safety education were taken as the entry point. SEM was used to establish an analytical framework for the influencing factors of safety education and the cognitive experiments were carried out to test the effect. The results show that the frequency of education, the mode of education, individual initiative, fun and comfort are the main factors affecting the effectiveness of construction safety education. The hybrid mode of "VR + lecture" is the most effective educational method at present, but the use of VR technology still requires personnel assistance. The optimal frequency of education is recommended to be less than 30 days. The main factors of fun, comfort are significantly positively correlated with educational effect, but comfort is more important in the application of construction safety education with VR technology.

To prevent accidents and disasters, such as the collapse of existing roads nearby, caused by deep and large foundation pit excavation and support, it is necessary to ensure the safe operation of adjacent roads during the construction of deep and large foundation pits. Taking a deep and large foundation pit project near a city trunk road in Kunming as an example, on the basis of in-depth research at the site, the three-dimensional finite element numerical simulation software-new eXperience of GeoTechnical analysis system(MIDAS GTS NX) is used for simulation and calculation, and combined with on-site monitoring, to analyse the force and deformation characteristics of the foundation pit support structure and the deformation characteristics of the adjacent existing road under the existing excavation and support scheme. The study results indicated that the forces and deformations of the supporting piles and anchor cables were within design limits after the foundation pit excavation and support were complete. The displacement near the existing road increased with the increase of pit excavation depth, and become stable after the excavation was completed. The maximum deformation of the existing road occurred at a position 2.5 times the excavation depth from the foundation pit boundary. Furthermore, the deformation did not reach the alarm threshold for road displacement caused by foundation pit excavation. Thus, the existing support scheme can ensure the safety of both the foundation pit and the adjacent road.

An improved object detection algorithm for high-consequence areas was proposed to solve the problems of the sensitive and complex external environment of the overseas section of the China-Myanmar oil and gas pipeline, difficulty in manual inspection, and high-risk factors. Firstly, a convolutional block attention module was used to adaptively learn channel and spatial attention to enhance the network's perception and generalization capabilities. Then, focal and efficient intersection over union(Focal-EIoU) loss was used to comprehensively consider the target features and their associations to deal with the issues of class imbalance, reduce the interference of easy-to-classify samples, and enhance the robustness of the model. Finally, the improved model was used to intelligently recognize regional attributes of China-Myanmar oil and gas pipeline remote sensing images. Furthermore, the proposed YOLO model was validated against related ablation experiments. The results showed that for the feature recognition of remote sensing images of the China-Myanmar oil and gas pipeline, the proposed model reached a mean average precision (mAP) of 68.2% for the field, green space, settlement, and river. The model performance was improved by 29%, 21.6%, and 10.7% compared with YOLOv5, YOLOx, and YOLOv8, respectively.