Safety Situation Assessment of Underwater Nodes Based on BP Neural Network

Huang, Haijie; Liang, Kun; Huang, Xiangdang; Yang, Qiuling

doi:10.1007/978-3-030-62743-0_32

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Pan et al (13) followed the framework of "personnel-machine-materialmethod-environment, " and identified personnel-type, machinetype, environment-type, and management-type safety risks during shield construction; Liu et al (14) found out the shield construction safety risks by using a questionnaire survey, including tunnel excavation, shield machine launch, segment assembly, special procedures and conditions, shield machine arrival, grouting, lead excavation, slag removal, and shaft construction. As for safety risk assessment, previous researchers often used the analytic hierarchy process (15), cloud model (15), fault tree analysis (16), Bayesian network (17,18), and backpropagation (BP) neural network (19)(20)(21) as assessment techniques. For example, Chung et al (22) applied a Bayesian network model to evaluate safety risks during tunnel shield construction.…”

Section: Introductionmentioning

confidence: 99%

Safety risk assessment of subway shield construction under-crossing a river using CFA and FER

He,

Cui,

Cheng

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

Numerous subway projects are planned by China's city governments, and more subways can hardly avoid under-crossing rivers. While often being located in complex natural and social environments, subway shield construction under-crossing a river (SSCUR) is more susceptible to safety accidents, causing substantial casualties, and monetary losses. Therefore, there is an urgent need to investigate safety risks during SSCUR. The paper identified the safety risks during SSCUR by using a literature review and experts' evaluation, proposed a new safety risk assessment model by integrating confirmatory factor analysis (CFA) and fuzzy evidence reasoning (FER), and then selected a project to validate the feasibility of the proposed model. Research results show that (a) a safety risk list of SSCUR was identified, including 5 first-level safety risks and 38 second-level safety risks; (b) the proposed safety risk assessment model can be used to assess the safety risk of SSCUR; (c) safety inspection, safety organization and duty, quicksand layer, and high-pressure phreatic water were the high-level risks, and the onsite total safety risk was at the medium level; (d) management-type safety risks, environment-type safety risks, and personnel-type safety risks have higher expected utility values, and manager-type safety risks were expected have higher risk-utility values when compared to worker-type safety risks. The research can enrich the theoretical knowledge of SSCUR safety risk assessment and provide references to safety managers for conducting scientific and effective safety management on the construction site when a subway crosses under a river.

show abstract

Section: Introductionmentioning

confidence: 99%

Safety risk assessment of subway shield construction under-crossing a river using CFA and FER

He,

Cui,

Cheng

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…To study Multi-modal multiobjective optimization problem (MMOPs), the nondominated solution sorting genetic algorithm (NSGA-II) has poor PS distribution and convergence, so an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, namely ASDNSGA-II is proposed [14]. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgement in order to automatically obtain the number of clusters for the given data and initialise the centre cluster, and a novel K-means clustering algorithm based on a noise algorithm is developed [15]. Of course, situation assessment has been studied by different scholars with different methods from different angles; some achievements [16][17][18][19][20] have been achieved.…”

Section: Introductionmentioning

confidence: 99%

A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed‐wing patrol aircraft

Sun¹,

Ma²,

Dai³

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

The battlefield situation changes rapidly because underwater targets' are concealment and the sea environment is uncertain. So, a great number of situation information greatly increase, which need to be dealt with in the course of scouting underwater targets. Situation assessment in sea battlefield with a lot of uncertain information is studied, and a new situation assessment method of scouting underwater targets with fixed-wing patrol aircraft is proposed based on the cloud Bayesian network, which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation. Moreover, in the method, the cloud model knowledge deal with the input data of Bayesian network reasoning, and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied; also, the fuzziness and stochasticity of cloud theory in knowledge expression, the reasoning ability of Bayesian network, are combined. Then, the situation assessment model of scouting underwater targets with fixed-wing patrol aircraft is established. Hence, the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined. Next, the cloud model is used to deal with Bayesian network, and the discrete Bayesian network is obtained. Moreover, after CPT of each node and the transformation between certainty degree and probability are accomplished; the final situation level is obtained through a probability synthesis formula. Therefore, the target type and the operational intention of the other side are deduced to form the battlefield situation. Finally, simulations are carried out, and the rationality and validity of the proposed method are testified by simulation results. By this method, the battlefield situation can be gained. And this method has a wider application range, especially for large sample data processing, and it has better practicability. K E Y W O R D S certainty degree, cloudy bayesian network(CBN), conditional probability table(CPT), fixed-wing patrol aircraft, scouting underwater targets, situation assessmentThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Safety Situation Assessment of Underwater Nodes Based on BP Neural Network

Cited by 2 publications

References 7 publications

Safety risk assessment of subway shield construction under-crossing a river using CFA and FER

Safety risk assessment of subway shield construction under-crossing a river using CFA and FER

A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed‐wing patrol aircraft

Contact Info

Product

Resources

About