提高安全检查可靠性的统计分析和人工智能模型研究

Sung Jong Lee, Joo Ha Lee
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引用次数: 0

摘要

本研究收集了地下结构和核电站结构的安全诊断结果,以评估现场结构安全评估的可靠性。对这些结果的分析表明,回弹硬度确定的抗压强度与核心抗压强度之间存在差异,前者的评估值通常高于后者。此外,现有的强度预测模型无法充分解释现场数据,而人工智能模型,特别是支持向量机模型,则提高了准确性并降低了错误率。这表明支持向量机模型在这种情况下具有卓越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on the Statistical Analysis and Artificial Intelligence Model to Improve the Reliability of Safety Inspection
In this study, safety diagnosis results from underground and nuclear power plant structures were collected to evaluate the reliability of on-site structural safety assessments. The analysis of these results revealed a discrepancy between the compressive strength determined by rebound hardness and the core compressive strength, with the former typically being evaluated higher than the latter. Additionally, existing strength prediction models did not adequately explain field data, whereas artificial intelligence models, particularly the support vector machine model, demonstrated improved accuracy and reduced error rates. This indicated the superior performance of support vector machine models in this context.
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