All along, safety accidents in the field of bridge construction have continued to arise, costing society's development financial and material resources and taking away people’s personal safety, turning into an urgent need for safety managers in the field to solve the subject research. Researchers look forward to continued innovative exploration to find new ways of working safely and using them in the construction industry. On this basis, BIM virtual scenes are introduced to create a universal scene specification dataset in the field of bridge construction and a dataset of unsafe potential hazard characteristics in the field of bridge construction. Nanobots are artificial intelligence systems built to simulate biological nanomachine constructions in life activities and important biological events in life processes. In this paper, BIM virtual scenarios incorporating intelligent nanomaterial robot mapping technology are migrated to bridge engineering safety management. According to the collected construction scene images and BIM (building information model) virtual scenes, build the construction scene dataset, use intelligent robots to obtain construction scenes, and load them into the trained construction on-the-spot neural network modeling to complete the bridge construction scene identification safety discrimination to select more comprehensive and more stringent management solutions as safety management personnel management methods on-the-spot.

What Is Known and Objective: Drug-induced neutropenia is the main reason for the dose limitation of docetaxel in patients with breast cancer. The area under the drug concentration-time curve (AUC) of docetaxel is associated with neutropenia. However, the optimal exposure to docetaxel for receiving postoperative adjuvant chemotherapy remains unclear. Therefore, we aimed to evaluate the relationship between the docetaxel AUC and neutropenia, identify potential influencing factors, and explore the best monitoring target for docetaxel when treating patients with earlystage breast cancer using a population pharmacokinetic (PopPK) model.Methods: Docetaxel plasma concentration, demographics, clinical data, and related laboratory data were collected. PopPK analyses were performed using a nonlinear mixed-effect modelling program. The docetaxel AUC was determined using the maximum a posteriori Bayesian (MAPB) method. The docetaxel exposure-toxicity threshold measured from the AUC for neutropenia was determined using the receiver operating characteristic (ROC) curve. The correlation between docetaxel exposure and neutropenia was analysed using multivariable logistic regression.Results: Among the 70 participants, 47 (67.1%) developed severe neutropenia. The PopPK analysis showed that the typical drug clearance (CL) rate was 37.4 L/h. Age was a significant covariate of CL rate, and aspartate aminotransferase and albumin levels were covariables of the volume of distribution. The multivariable regression analysis showed that AUC >3.0 mg.h/L (odds ratio [OR], 5.940; 95% confidence interval [CI], 1.693-20.843; P = 0.005), platinum use (OR, 0.156; 95% CI, 0.043-0.562; P = 0.005) and baseline haemoglobin level (OR, 0.938; 95% CI, 0.887-0.993; P = 0.027) were significant factors influencing the occurrence of grade 3/4 neutropenia. The AUC of first cycle may not predict the occurrence rates of grade 3/4 neutropenia in later cycles. What Is New and Conclusion:We developed a docetaxel PopPK model for patients with early-stage breast cancer. Age and AST and ALB levels were significant covariates. AUC estimated using the MAPB method can predict the toxicity of docetaxel in patients with breast cancer. Docetaxel AUC >3.0 mg.h/L, absence of platinum use and low baseline haemoglobin level were risk factors for docetaxel-induced grade 3/4 neutropenia.