In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.

One of the most critical tasks for pavement maintenance and road safety is the rapid and correct identification and classification of asphalt pavement damages. Nowadays, deep learning networks have become the popular method for detecting pavement cracks, and there is always a need to further improve the accuracy and precision of pavement damage recognition. An improved YOLOv4-based pavement damage detection model was proposed in this study to address the above problems. The model improves the saliency of pavement damage by introducing the convolutional block attention module (CBAM) to suppress background noise and explores the influence of the embedding position of the CBAM module in the YOLOv4 model on the detection accuracy. The K-means++ algorithm was used to optimize the anchor box parameters to improve the target detection accuracy and form a high-performance pavement crack detection model called YOLOv4-3. The training and test sets were constructed using the same image data sources, and the results showed the mAP (mean average precision) of the improved YOLOv4-3 network was 2.96% higher than that before the improvement. The experiments indicate that embedding CBAM into the Neck module and the Head module can effectively improve the detection accuracy of the YOLOv4 model.

The prediction of bridge service performance is essential for bridge maintenance, operation, and decision making. As a key component of the superstructure, the performance of the main girders is critical to the structural safety of the bridge. This study makes full use of the inspection records from the Bridge Management System (BMS) in Shanghai and performs pre-processing work on a large amount of data. Recent advances in survival analysis were utilized to investigate the inspection records of over 40,000 reinforced concrete bridge main girders over a 14-year period. Survival analysis methods based on the Weibull distribution were used to predict the service performance of the main girders, and, in addition, a COX proportional hazards model was used to analyze the effect of different covariates on the survival of the main girders. The results show that the deterioration rate of main girders increases with age, with an average life of 87 years for main girders in Shanghai. The grade of the road on which the bridge is located and the position of the main girder in the bridge superstructure have a significant impact on the probability of survival of the main girder. It can be concluded that more attention should be paid to the inspection and maintenance of side girders on branch roads to reduce the pressure on bridge management in the future. Furthermore, the analysis in this study found that the deterioration rate of the main girders is faster than the deterioration rate of the whole bridge and superstructure, and, therefore, more attention and necessary preventive maintenance measures should be taken in the maintenance and management of the main girders.