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Rapid Evaluation and Decision-Making for Concrete Bridges

目视检查 桥(图论) 无损检测 热成像 计算机科学 人工智能 计算机视觉 结构工程 工程类 红外线的 光学 医学 物理 内科学 放射科
作者
Marwan Debees,F. Necati Çatbaş
出处
期刊:Transportation Research Record [SAGE Publishing]
卷期号:2679 (7): 898-911
标识
DOI:10.1177/03611981251330892
摘要

Incorporating non-destructive evaluation (NDE) techniques into bridge assessment decision-making offers significant advantages to bridge owners. One promising method, infrared thermography (IRT), has been investigated for detecting concrete anomalies in civil engineering. When combined with high-definition image scans, IRT shows potential for rapid and efficient bridge scanning applications. This paper presents a comparative analysis of two rapid bridge scans conducted using a combination of IRT and high-definition imaging (IRT+Image). These scans were performed in 2021 and 2022 on a deteriorating bridge (built in 1964). The results of the scans are then compared with the findings from the official biennial visual inspection of the bridge. Additionally, a decision-making model based on a perceptron neural network is developed and demonstrated, which leverages the scan results. This model can be expanded to incorporate other NDE techniques or visual inspection data within an element-level inspection framework. The comparative evaluation showed that the majority of the concrete defects identified in the inspection report were also identified in the IRT+Image scans. Furthermore, the IRT+Image scan identified additional areas of concrete defects that were not identified in the inspection report. However, these additional locations were not verified to represent defects by means other than the scan. For example, IRT+Image scan identified 42.5% more piles with delamination than the inspection did. For the deck underside, 99% of the defect locations identified in the visual inspection were also detected by the IRT+Image scan. For the superstructure beams, the correspondence was 79%, and for the scanned bents, all substructure piles with defects in the biennial inspection were similarly identified by the IRT+Image scan. The perceptron neural network developed was applied to two case studies, assisting in decision-making for further evaluation. This practical NDE application using IRT+Image and the decision-making model can be extended to other transportation assets and integrate various NDE technologies.
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