A non-parametric system identification-based model is presented for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy wavelet neural network (WNN) model developed by the authors. The model does not require complete measurements of the dynamic responses of the whole structure. A large structure is divided into a series of sub-structures around a few pre-selected floors where sensors are placed and measurements are made. The new model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively, thus resulting in fast training convergence and high accuracy. A new damage evaluation method is proposed based on a power density spectrum method, called pseudospectrum. The multiple signal classification (MUSIC) method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using the data obtained for a 38-storey concrete test model. The results demonstrate the effectiveness of the WNN model together with the pseudospectrum method for damage detection of highrise buildings based on a small amount of sensed data. Copyright © 2007 John Wiley & Sons, Ltd.

Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings

Optimal sensor placement for spatial lattice structure based on genetic algorithms

Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale civil infrastructures. This paper outlines an overview of current research and development in the field of OSP problems in a perspective of both researchers and engineers. The paper begins with a definition of the model of sensor placement and provides the basic issues covering relevant methodologies. The primary evaluation criteria and main sensor placement methods are then discussed in details. Following that, the linkage between several influential sensor placement methods is described. Finally, existing problems and promising research efforts in the OSP problem of civil SHM are discussed.

https://journals.sagepub.com/doi/pdf/10.1155/2012/612726

Methodology Developments in Sensor Placement for Health Monitoring of Civil Infrastructures

Subject of Research. The paper presents the study of a graphite-epoxy composite plate strain detection possibility by the fiber-optic acoustic emission sensor mounted on its surface. Method. The proposed method consisted in additional lowfrequency phase-generated carrier implementation in the impulse Fabry-Perot interferometer and its amplitude evaluation. The phase-generated carrier amplitude depends on the interferometer optical path difference, therefore, its value change can be used for the studied composite strain estimation. VCSEL with a wavelength of 1550 nm was used as a light source. The phase carrier was generated by current modulation of the light source that caused center wavelength shift of the VCSEL. Main Results. The low-frequency phase-generated carrier signal amplitude dependence on the interferometer optical path difference and wavelength shift of the light source were obtained. According to simulation results the sensitivity of the proposed method is 1.6 urad×ppm, 5.3 urad×ppm and 13.3 urad×ppm at different values of the coefficient Kd 30, 100 and 250 pm, respectively. Experimental study of the proposed method and results analysis were performed. According to experimental results, the accuracy of the proposed method was about 110% that corresponds to the sensor relative stretch of 10 e, while the accuracy of the market available fiber optic systems based on fiber Bragg grating sensors equals to 4 ppm. Practical Relevance. The proposed method can be used for strain detection of the graphite-epoxy composite constructions along with its acoustic emission control by one fiber-optic sensor.

http://cyberleninka.ru/article/n/osobennosti-vremennyh-parametrov-transmitralnogo-diastolicheskogo-potoka-pri-serdechno-sosudistoy-patologii.pdf

Особенности временных параметров трансмитрального диастолического потока при сердечно-сосудистой патологии

Setting up a health monitoring system for large-scale civil engineering structures requires a large number of sensors and the placement of these sensors is of great significance for such spatially separated large structures. In this paper, we present an optimal sensor placement (OSP) algorithm by treating OSP as a combinatorial optimization problem which is solved using a swarm intelligence technique called particle swarm optimization (PSO). We propose a new hybrid PSO algorithm by combining a self-configurable PSO with the Nelder–Mead algorithm to solve this rather difficult combinatorial problem of OSP. The proposed algorithm aims precisely to achieve the best identification of modal frequencies and mode shapes. Numerical experiments have been carried out by considering civil engineering structures to evaluate the performance of the proposed swarm-intelligence-based OSP algorithm. Numerical studies indicate that the proposed hybrid PSO algorithm generates sensor configurations superior to the conventional iterative information-based approaches which have been popularly used for large structures. Further, the proposed hybrid PSO algorithm exhibits superior convergence characteristics when compared to other PSO counterparts.

Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique

The global optimization of sensor locations for structural health monitoring systems is studied in this paper. First, the performance function based on damage detection is presented. Then, genetic algorithms (GAs) are adopted to search for the optimal locations of sensors. However, the simple GAs can result in infeasible solutions to the problem. Some improved strategies are presented in this paper, such as crossover based on identification code, mutation based on two gene bits, and improved convergence. The analytical results from the improved genetic algorithm are compared with the penalty function method and the forced mutation method. It is concluded that the convergence speed with the proposed improved genetic algorithm is faster than that with the penalty function method and the forced mutation method, and the result of placement optimization is better.

http://iopscience.iop.org/article/10.1088/0964-1726/13/3/011/pdf

H Y Guo

Papers

Optimal placement of sensors for structural health monitoring using improved genetic algorithms