Multi-type Sensors Research Articles

Multi-type sensor placement and response reconstruction for structural health monitoring of long-span suspension bridges

This study is devoted to the experimental validation of the multi-type sensor placement and response reconstruction method for structural health monitoring of long-span suspension bridges. The method for multi-type sensor placement and response reconstruction is briefly described. A test bed, comprising of a physical model and an updated finite element (FE) model of a long-span suspension bridge is also concisely introduced. The proposed method is then applied to the test bed; the equation of motion of the test bed subject to ground motion, the objective function for sensor location optimization, the principles for mode selection and multi-type response reconstruction are established. A numerical study using the updated FE model is performed to select the sensor types, numbers, and locations. Subsequently, with the identified sensor locations and some practical considerations, fiber Bragg grating (FBG) sensors, laser displacement transducers, and accelerometers are installed on the physical bridge model. Finally, experimental investigations are conducted to validate the proposed method. The experimental results show that the reconstructed responses using the measured responses from the limited number of multi-type sensors agree well with the actual bridge responses. The proposed method is validated to be feasible and effective for the monitoring of structural behavior of long-span suspension bridges.

Optimal multi-type sensor placement for response and excitation reconstruction

The need to perform dynamic response reconstruction always arises as the measurement of structural response is often limited to a few locations, especially for a large civil structure. Besides, it is usually very difficult, if not impossible, to measure external excitations under the operation condition of a structure. This study presents an algorithm for optimal placement of multi-type sensors, including strain gauges, displacement transducers and accelerometers, for the best reconstruction of responses of key structural components where there are no sensors installed and the best estimation of external excitations acting on the structure at the same time. The algorithm is developed in the framework of Kalman filter with unknown excitation, in which minimum-variance unbiased estimates of the generalized state of the structure and the external excitations are obtained by virtue of limited sensor measurements. The structural responses of key locations without sensors can then be reconstructed with the estimated generalized state and excitation. The asymptotic stability feature of the filter is utilized for optimal sensor placement. The number and spatial location of the multi-type sensors are determined by adding the optimal sensor which gains the maximal reduction of the estimation error of reconstructed responses. For the given mode number in response reconstruction and the given locations of external excitations, the optimal multi-sensor placement achieved by the proposed method is independent of the type and time evolution of external excitation. A simply-supported overhanging steel beam under multiple types of excitation is numerically studied to demonstrate the feasibility and superiority of the proposed method, and the experimental work is then carried out to testify the effectiveness of the proposed method.

Estimation on structural responses using multi scale measurements

Different measurands from the different types of sensors can obtain different information regarding the structural behavior in a real structural health monitoring system. To enrich information and estimate the structural responses based on much more known information, the estimation on structural responses using multi scale measurements from multi-type sensors is proposed in this paper. Pattern identification is constructed with the pattern library given by strain measurements and deformation measurements. Considering the uncertainty of the measurements as well as to enhance the robustness of the proposed algorithm, more than one best pattern is selected to synthesize the finally estimated stress responses. To validate the capacity of the proposed acquisition method using multi scale measurements, finite element model analysis is conducted to estimate the structural stress response in Shenzhen Bay Stadium as an example. The performance of the pattern identifications, constructed by two kinds of pattern libraries captured by sole strain measurement, and multi scale measurements which are constructed by both kinds of strain measurements and deformation measurements, respectively, are compared in this paper to observe measurements constructed from strain measurements and deformation measurements outperformed others. Errors analysis for a series of parametric studies in which noise at different levels has also included in the measurements are further carried out, and robustness of the proposed information acquisition scheme under noisy measurement is demonstrated.

Damage identification for large span structure based on multiscale inputs to artificial neural networks.

In structural health monitoring system, little research on the damage identification from different types of sensors applied to large span structure has been done in the field. In fact, it is significant to estimate the whole structural safety if the multitype sensors or multiscale measurements are used in application of structural health monitoring and the damage identification for large span structure. A methodology to combine the local and global measurements in noisy environments based on artificial neural network is proposed in this paper. For a real large span structure, the capacity of the methodology is validated, including the decision on damage placement, the discussions on the number of the sensors, and the optimal parameters for artificial neural networks. Furthermore, the noisy environments in different levels are simulated to demonstrate the robustness and effectiveness of the proposed approach.

Development of a Multitype Wireless Sensor Network for the Large-Scale Structure of the National Stadium in China

A multitype wireless sensor network (WSN) for structural health monitoring is developed for the National Stadium in China (generally known as “Bird's Nest”). The stadium is a super large-scale building built for the 2008 Beijing Olympic Games and can house more than 90,000 occupants. The structure is very rigid and weighs more than 40,000 tons in total. Considering the structural features and on-site environment, the system takes multitype sensors as measurement components including stress, displacement, acceleration, wind, and temperature. The monitoring module design consists of four functions: sensing, processing, wireless communication, and energy management. The communication between each sensor node is realized by using an adjustable and artificial-control chain-type network. A total of 290 sensors were installed on the structure, and the data collection work has been carried out for more than one year. This paper mainly focuses on the system development and project application, while the data analysis work is briefly discussed as well. It can be concluded that the customized WSN is robust and durable, which well satisfies the requirement of plenty multitype sensors working in a large-area distribution. The data analysis results reveal that the super large-scale structure is very sensitive to the temperature effect.

Multi-Type Sensor Placement for Multi-Scale Response Reconstruction

Although various types of sensors are now available to monitor a structure, measurements are usually obtained in only a few locations numbering less than the total degrees of freedom (DOFs) of the structure. The lack of structural responses of the structure at all its critical areas may hamper the effectiveness of structural monitoring. Therefore, multi-scale response reconstruction at those key structural locations where sensors are not available is essential to fully achieving the structural monitoring objectives. This paper addresses a problem of placing multi-type sensors in a structure with the objective of best reconstruction of structural responses. The types of sensors include accelerometers, displacement and strain measurement sensors, all of which are widely used in civil engineering structures. The number and locations of the three types of sensors are determined aiming to best possibly reconstruct the strain, displacement and acceleration responses, in which the Kalman filter algorithm is employed. By minimizing the overall reconstruction error variance at the locations of interest and maintaining reconstruction errors to a desired target level, the initial set of candidate sensor locations is reduced to a smaller set. The key multi-scale structural responses are reconstructed from the fusion of limited multi-type sensor data information. A simply-supported overhanging steel beam is investigated as a sample case in numerical and experimental study to investigate the effectiveness and accuracy of the presented approach. The good response reconstruction results clearly demonstrate the effectiveness of the proposed optimal placement method for multi-type sensors.

Limitation of using multi-type sensors in combination for verifying atmospheric phenomena

The feasibility of combining two sensors for monitoring gas concentrations over a large area, is demonstrated via controlled simulation experiments. The network considered was a lidar combined with a point detector — the MIRAN spectrophotometer equipped with a flow-through gas cell. The first has a spatial scanning capacity and a very good time resolution, whereas the second has an inherent temporal memory effect. Algorithms for operating the two sensors coincidentally, taking their physical characteristics into account, were constructed. A good fit was found between measured concentrations in the MIRAN and reconstructed concentrations, using the lidar data for the same environment. Thus complementary operation can be achieved. The MIRAN temporal memory effect can be overcome by correlating the concentration calculated over different time intervals.