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An accurate correlation model between thermal loading and deformation is required for facilitating a reliable deformation-based condition evaluation in bridge service periods. In this paper, a general approach for modeling closed-form thermal correlation of deformation based on monitoring data is proposed and applied in a long-span arch bridge. First, samples of all available thermal variables and deformation induced by thermal loading are prepared by averaging preprocessed monitoring records at a 10-min interval. Then these available thermal variables are reduced to several predominant thermal variables, each of which represents a cluster of thermal variables with statistical similarity and has the strongest relationship with the thermal deformation in concern. Finally, the model of thermal deformation is formulated as a weighted sum of fitted functions of predominant thermal variables. The weighted coefficients are calculated by the back-propagation neural network technique combined with the mean impact value method and the fitted functions are estimated by the nonlinear least-squares method. The proposed approach is applied to 1 year of monitoring data obtained from a sophisticated structural health monitoring system deployed on the Jiubao Bridge. The proposed method is validated and a closed-form thermal correlation model of vertical deformation in the Jiubao Bridge is established. [ABSTRACT FROM AUTHOR]

The negative stiffness (NS) phenomenon and additional supplemental damping (AD) were proposed and investigated in recent years. Despite the bulk of literature on the base shear reduction for structures with the NS at the base or on the first story, the application of the NS and AD in the stories of a superstructure was barely studied. Considering the implementation of the NS and AD in a passive way, this paper proposes an innovative approach to optimize the NS and AD values on the different stories of civil structures. First, the formulation is derived to convert the optimization of NS and AD to the static feedback decentralized controller design problem. Then, the controller is searched by homotopy path, which leads to a local optimal solution. Finally, the procedure is validated by a 20-story benchmark structural model. [ABSTRACT FROM AUTHOR]

The implementation and operation of health monitoring systems in structures is an effective method of evaluating structural serviceability, reliability, safety, and integrity and providing economical maintenance and repair planning. This paper reports on an innovative structural health monitoring (SHM) system designed and permanently deployed on a complicated long-span arch bridge during its construction for the purpose of continuous performance monitoring throughout the bridge's lifespan. The SHM system integrates several novel and practice-based concepts--i.e., life-cycle concerns for system design and implementation; newly designed high-performance sensors; flexible, modularized hardware and software; multilevel data management; multiscale condition evaluation; and reliable warning strategies. More than 300 sensors of 10 types are installed on the bridge with the aim of monitoring the environmental effects and structural responses. The innovative design and implementation of the monitoring system are presented together with a functional description of each subsystem. Selected preliminary monitoring results are outlined, including structural temperatures, main girder deformations, relationship between structural temperature and main girder deformation, and dynamic responses. The architecture of this SHM system is expected to provide a valuable reference for other structures. [ABSTRACT FROM AUTHOR]

Using structural monitoring data collected from a sensor network to assess the health condition of a monitored structure relies on the accurate operation of the sensors and therefore could be affected by various sensor faults. This paper presents a sensor-fault detection and isolation approach with application to structural health monitoring. Principal-component analysis (PCA) is first applied to model the fault-free history monitoring data to generate uncorrelated residuals, which can be seen as the projection of the additional measurement noise into the residual subspace of the PCA transform. Then, under the assumption that the measurement noise is Gaussian distributed, a statistical hypothesis test model is established for the subsequent sensor-fault detection procedure, after that two fault detectors are deduced through the rejection of the null hypothesis. Next, the missing variable approach is used to establish an isolation index to identify the specific faulty sensor. A benchmark structure developed for bridge health monitoring is adopted to validate and demonstrate the performance of the proposed method, and the analysis results indicate that the method is effective in detecting and isolating both bias and drift sensor faults. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented which discusses various papers published within the issue, including one on damage identification for structures under traffic-induced vibration, one on a structural health monitoring system for a high-rise building, and another on the construction process of the Shenzhen Vanke Center.