Bertola Numa Joy, Papadopoulou Maria, Vernay Didier, Smith Ian F C
ETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, 1 CREATE Way, CREATE Tower, Singapore 138602, Singapore.
Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland.
Sensors (Basel). 2017 Dec 14;17(12):2904. doi: 10.3390/s17122904.
Assessing ageing infrastructure is a critical challenge for civil engineers due to the difficulty in the estimation and integration of uncertainties in structural models. Field measurements are increasingly used to improve knowledge of the real behavior of a structure; this activity is called structural identification. Error-domain model falsification (EDMF) is an easy-to-use model-based structural-identification methodology which robustly accommodates systematic uncertainties originating from sources such as boundary conditions, numerical modelling and model fidelity, as well as aleatory uncertainties from sources such as measurement error and material parameter-value estimations. In most practical applications of structural identification, sensors are placed using engineering judgment and experience. However, since sensor placement is fundamental to the success of structural identification, a more rational and systematic method is justified. This study presents a measurement system design methodology to identify the best sensor locations and sensor types using information from static-load tests. More specifically, three static-load tests were studied for the sensor system design using three types of sensors for a performance evaluation of a full-scale bridge in Singapore. Several sensor placement strategies are compared using joint entropy as an information-gain metric. A modified version of the hierarchical algorithm for sensor placement is proposed to take into account mutual information between load tests. It is shown that a carefully-configured measurement strategy that includes multiple sensor types and several load tests maximizes information gain.
由于在结构模型中估计和整合不确定性存在困难，评估老化基础设施对土木工程师来说是一项严峻挑战。现场测量越来越多地用于增进对结构实际性能的了解；这项活动被称为结构识别。误差域模型证伪（EDMF）是一种易于使用的基于模型的结构识别方法，它能稳健地处理源自边界条件、数值建模和模型保真度等因素的系统不确定性，以及源自测量误差和材料参数值估计等因素的偶然不确定性。在结构识别的大多数实际应用中，传感器是根据工程判断和经验布置的。然而，由于传感器布置是结构识别成功的基础，因此需要一种更合理、系统的方法。本研究提出一种测量系统设计方法，利用静载试验信息确定最佳传感器位置和传感器类型。具体而言，针对新加坡一座全尺寸桥梁的性能评估，使用三种类型的传感器对三个静载试验进行了传感器系统设计研究。使用联合熵作为信息增益指标比较了几种传感器布置策略。提出了一种改进版的传感器布置分层算法，以考虑荷载试验之间的互信息。结果表明，精心配置的测量策略，包括多种传感器类型和多次荷载试验，可使信息增益最大化。
