J-BMS在既有RC桥梁性能评估与剩余寿命预测中的应用

IF 1 Q4 ENGINEERING, CIVIL
A. Miyamoto, Hiroyoshi Asano
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引用次数: 1

摘要

本文介绍了基于J-BMS RC版近距离目测数据对某老旧钢筋混凝土t梁桥进行性能评估和剩余寿命预测的方法,并对J-BMS的一个子系统桥梁等级专家系统RC- brex的评估结果进行了验证,以评价该系统的有效性。利用作者先前开发的能够预测既有桥梁构件劣化过程的桥梁管理系统(J-BMS),根据这些试验结果对目标桥梁的安全指标(稳健性评分)和剩余寿命进行评价。利用这些方法,采用J-BMS RC版本的一个子系统桥梁等级专家系统(BREX),结合现场检查数据,可以定量估计RC- t梁桥(SK-bridge)的剩余寿命。在本研究中,由专业的视觉检查员对老桥进行近距离的视觉检查,在此过程中,使用五步问卷对检查结果的所有变化进行评估。结果发现,稳健性评分(安全指数)和剩余寿命预测受到学习(监督)数据选择的影响。此外,通过从主梁和甲板板中提取混凝土芯试验验证了预测的剩余寿命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of J-BMS to Performance Evaluation and Remaining Life Prediction of an Existing RC Bridge
This paper describes a method of performance evaluation and remaining life prediction for an aged reinforced concrete (RC) T-girder bridge by J-BMS RC version via close visual inspection data, and also verifies the assessment results obtained as outputs from the Bridge Rating Expert System (RC-BREX) which is a subsystem of the J-BMS, to evaluate the effectiveness of the system. The Bridge Management System (J-BMS) that was previously developed by the authors, and which is capable of forecasting the deterioration process of existing bridge members, was applied to evaluate the safety indices (soundness score) and remaining life of the target bridge based on these test results. Using these methods, the remaining life of an aged RC-T girder bridge (SK-bridge) can be quantitatively estimated by applying the bridge rating expert (BREX) system, which is a subsystem of the J-BMS RC version that incorporates with the field inspection data. In this study, close visual inspection was carried out on the aged bridge by professional visual inspectors, during which all variations of the inspection results were evaluated using a five-step questionnaire. As a result, it was found that the soundness score (safety index) and remaining life predictions were influenced by the learning (supervised) data selection. Additionally, the predicted remaining lives were verified through concrete core tests extracted from main girders and deck slabs.
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来源期刊
CiteScore
1.30
自引率
60.00%
发文量
0
审稿时长
47 weeks
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