Search Results (3)

The challenging nature of nearshore tunnel construction environments introduces a multitude of potential hazards, consequently escalating the likelihood of incidents such as water influx. Existing construction safety risk management methodologies often depend on subjective experiences, leading to inconsistent reliability in assessment outcomes. The multifaceted nature of construction safety risk factors, their sources, and structures complicate the validation of these assessments, thus compromising their precision. Moreover, risk assessments generally occur pre-construction, leaving on-site personnel incapable of recommending pragmatic mitigation strategies based on real-time safety issues. To address these concerns, this paper introduces a construction safety risk assessment approach for nearshore tunnels based on multi-data fusion. In addressing the issue of temporal effectiveness when the conflict factor K in traditional Dempster–Shafer (DS) evidence theory nears infinity, the confidence Hellinger distance is incorporated for improvement. This is designed to accurately demonstrate the degree of conflict between two evidence chains. Subsequently, an integrated evaluation of construction safety risks for a specific nearshore tunnel in Ningbo is conducted through the calculation of similarity, support degree, and weight factors. Simultaneously, the Revit secondary development technology is utilized to visualize risk monitoring point warnings. The evaluation concludes that monitoring point K7+860 exhibits a level II risk, whereas other monitoring points maintain a normal status. Full article

Accurate detection of lane lines is of great significance for improving vehicle driving safety. In our previous research, by improving the horizontal and vertical density of the detection grid in the YOLO v3 (You Only Look Once, the 3th version) model, the obtained lane line (LL) algorithm, YOLO v3 (S × 2S), has high accuracy. However, like the traditional LL detection algorithms, they do not use spatial information and have low detection accuracy under occlusion, deformation, worn, poor lighting, and other non-ideal environmental conditions. After studying the spatial information between LLs and learning the distribution law of LLs, an LL prediction model based on long short-term memory (LSTM) and recursive neural network (RcNN) was established; the method can predict the future LL position by using historical LL position information. Moreover, by combining the LL information predicted with YOLO v3 (S × 2S) detection results using Dempster Shafer (D-S) evidence theory, the LL detection accuracy can be improved effectively, and the uncertainty of this system be reduced correspondingly. The results show that the accuracy of LL detection can be significantly improved in rainy, snowy weather, and obstacle scenes. Full article

In a multi-sensor system, due to the difference of performance of sensors and the environment in which the sensor collects evidence, evidence collected will be highly conflicting, which leads to the failure of D-S evidence theory. The current research on combination methods of conflicting evidence focuses on eliminating the problem of "Zadeh paradox" brought by conflicting evidence, but do not distinguish the evidence from different sources effectively. In this paper, the credibility of each piece of evidence to be combined is weighted based on historical data, and the modified evidence is obtained by weighted average. Then the final result is obtained by combining the modified evidence using D-S evidence theory, and the improved decision rule is used for the final decision. After the decision, the system updates and stores the historical data based on actual results. The improved decision rule can solve the problem that the system cannot make a decision when there are two or more propositions corresponding to the maximum support in the final combination result. This method satisfies commutative law and associative law, so it has the symmetry that can meet the needs of the combination of time-domain evidence. Numerical examples show that the combination method of conflict evidence based on historical data can not only solve the problem of “Zadeh paradox”, but also obtain more reasonable results. Full article