A multi-criteria decision framework to support measurement-system design for bridge load testing
A multi-criteria decision framework to support measurement-system design for bridge load testing
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DOI：
10.1016/j.aei.2019.01.004
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发表时间：
2019-01-01
影响因子：
8.8
通讯作者：
Smith, Ian F. C.
中科院分区：
文献类型：
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作者：
Bertola, Numa J.;Cinelli, Marco;Smith, Ian F. C.
由于保守的设计模型和安全的施工实践，基础设施通常具有超出规范要求的未知储备容量。通过避免不必要的更换和降低维护费用，这种储备能力的量化有可能导致更好的资产管理决策。然而，由于典型结构模型中存在的系统不确定性，这种量化是具有挑战性的。在载荷试验期间收集的现场测量数据，结合良好的结构识别方法，可以提高模型预测的准确性。在大多数结构识别任务中，工程师通常根据经验和高信噪比估计来选择和放置传感器。由于结构识别的成功取决于测量系统，因此对测量系统设计的研究已经进行了几十年。尽管该问题具有多标准的性质，但大多数研究人员只关注测量系统获得的信息。本研究提出了一个基于分层多标准策略的评估和排序可能的测量系统设计的框架。测量系统设计的性能标准包括监测成本、信息增益、检测异常值的能力以及传感器故障时信息丢失的影响。通过包括相互冲突的标准，如监控成本和信息获取，最优测量系统变成了一个类似帕累托的选择，最终取决于资产管理者的偏好层次。生成了几种潜在的首选方案，并通过埃克塞特大桥的全面测试研究对结果进行了比较。该框架通过提供广泛的备选方案成功地支持测量系统的知情设计，包括概率定义的最佳解决方案，以及在其他接近最优解决方案可能被首选的特定条件下的最佳解决方案。
Due to conservative design models and safe construction practices, infrastructure usually has unknown amounts of reserve capacity that exceed code requirements. Quantification of this reserve capacity has the potential to lead to better asset-management decisions by avoiding unnecessary replacement and by lowering maintenance expenses. However, such quantification is challenging due to systematic uncertainties that are present in typical structural models. Field measurements, collected during load tests, combined with good structural-identification methodologies may improve the accuracy of model predictions. In most structural-identification tasks, engineers usually select and place sensors based on experience and high signal-to-noise estimations. Since the success of structural identification depends on the measurement system, research into measurement system design has been carried out over several decades. Despite the multi-criteria nature of the problem, most researchers have focused only on the information gained by the measurement system. This study presents a framework to evaluate and rank possible measurement-system designs based on a tiered multi-criteria strategy. Performance criteria for the design of measurement systems include monitoring costs, information gain, ability to detect outliers and impact of loss of information in case of sensor failure. Through including conflicting criteria, such as cost of monitoring and information gain, the optimal measuring system becomes a Pareto-like choice that ultimately depends on asset-manager preference hierarchies. Several potential preference scenarios are generated and results are compared using a full-scale test study, the Exeter Bascule Bridge. The framework successfully supports an informed design of measurement systems by providing an extensive set of alternatives, including the best solution defined probabilistically and for specific conditions when other near-optimal solutions might be preferred.