海底隧道安全状态评价数据集

该数据集包含海底隧道结构状态演化试验的监测数据、安全状态评价指标数据、智能诊断算法及现场无人化巡检获取的安全状态评价数据。数据通过数值模拟/模型试验与实际工程监测相结合的方式产生，基于多维Copula函数和贝叶斯网络构建了智能诊断模型，并由专家知识优化模型结构，为海底隧道安全状态的智能评估和预警提供了系统性解决方案。该数据集形成了包含结构物理参数和视觉特征的多源异构数据融合体系，构建了针对典型隧道病害的安全状态评价阈值标准，建立了多层级安全预警机制，实现了从单点监测到整体安全状态评估的智能化转化，为海底隧道韧性增强提供了关键决策支持。

This dataset encompasses monitoring data from structural state evolution tests of undersea tunnels, safety status evaluation index data, intelligent diagnosis algorithms, and safety status evaluation data collected via on-site unmanned inspections. The data is generated through a combination of numerical simulation/model tests and actual engineering monitoring. An intelligent diagnosis model is constructed based on multi-dimensional Copula functions and Bayesian networks, with its structure optimized using expert knowledge, providing a systematic solution for intelligent assessment and early warning of undersea tunnel safety states. This dataset establishes a multi-source heterogeneous data fusion system integrating structural physical parameters and visual features, develops safety status evaluation threshold standards for typical tunnel defects, builds a multi-level safety early warning mechanism, realizes the intelligent transformation from single-point monitoring to overall safety status assessment, and provides key decision support for the resilience enhancement of undersea tunnels.

搜集汇总

背景与挑战

背景概述

该数据集包含海底隧道结构状态演化试验的监测数据、安全评价指标和智能诊断算法，通过数值模拟、模型试验与实际监测结合生成，基于多维Copula函数和贝叶斯网络构建智能诊断模型，并由专家知识优化。它形成了多源异构数据融合体系，建立了安全状态评价阈值标准和多层级预警机制，实现了从单点监测到整体安全状态智能评估的转化，为海底隧道韧性增强提供关键决策支持。