Mechanoluminescent (ML) materials featuring renewable mechanical‐to‐optical conversion have shown promising prospects in stress sensing, lighting, and display. However, the advancement in ML applications is being restrained by the obstacles in developing efficient ML materials and understanding the underlying ML mechanisms. Herein, a matrix evolution strategy to modulate the local microstructure and electronic environment around the luminescent activators is proposed, which not only supports the batch development of new ML materials but also provides a well‐connected platform for systematically revealing the mechanism of achieving efficient ML performance. The feasibility of the strategy is proved by constructing and evaluating a series of ML materials with matrix‐dependent luminescent properties in experimental‐theoretical collaboration. It is demonstrated that the construction of piezoluminescence is available in both non‐centrosymmetric and centrosymmetric matrices without being restricted by lattice symmetry. The inter‐electronic‐levels and shallow electron traps formed by activator doping enhance the electron recombination efficiency through tunneling and conduction band transfer pathways. The results are expected to accelerate the exploitation of ML material systems and to deepen the comprehensive apprehending of ML mechanisms, thereby guiding the rational design and widespread use of efficient ML materials.
Based on wavelet packet decomposition and conditions of the support vector kernel function, a non-linear wavelet basis is introduced to construct the kernel function of support vector machine (SVM), and a tighten wavelet support vector machine (WSVM), which has strong generalization ability is obtained. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction according to wavelet energy spectrum. The feature vectors are used for training and classification as the inputs of the tighten SVM. Hence, a new structural damage detection method called complete WSVM is established. This method is used to a single-layer spherical lattice dome for damage detection. The structural damage location and degree can be detected and classified, and the result is highly accurate. This approach has some advantages, such as engineering oriented, low cost and convenient. ensure the accuracy [6]. Flexibility matrix method is hard to reflect structural local damages [7]. Modal strain energy method can obtain different results using different test modals [8].Structural health monitoring (SHM) is a multi-disciplinary and integrated technology; hence, it is difficult to resolve many practical problems for large and complex structures based on merely vibration testing. Improving the finite element updating technology for health monitoring and state evaluation, combining modern signal analysis technology and soft computing theory, mining structural characteristic data deeply, realizing the real-time, online dynamic monitoring and control, is the developmental direction for SHM.Recently, many advanced technology and intelligent methods, such as digital filter technology, wavelet transform (WT) analysis [9][10][11][12], artificial neural networks (ANNs) [13,14] and genetic algorithm (GA), are studied to detect the structural global and local damage information, and to optimize the SHM system.Wavelet packet transform (WPT) is a new method of signal processing that has recently been applied to various science and engineering fields with great success. The specific local properties of wavelet packet can be particularly useful to describe signals with sharp spikes or discontinuities. It is effective for the structural damage signals process by wavelet packet because its multi-resolution analysis capability and time-frequency localization. As an effective time-frequency tool, wavelets could detect whether damage has occurred but it is not effective to detect different damage states. Intelligent tools are needed to solve the damage classification problem, which includes damage location and degree.ANNs have been applied in structural damage detection as generalization or classification problems based on learning pattern from examples or empirical data model [15]. The ANN method for structural damage detection has some advantages such as strong self-adaptive and fault-tolerant capabilities, and it has been approved to be effective for some elements and simple...
A spherical polymerized toner with an average size around 10 µm and a span (D 0.9 -D 0.1 )/D 0.5 of 1.5-2.0 is prepared directly by in situ suspension copolymerization of a mixture of styrene/ n-butyl acrylate/iron black. The particle size distribution (PSD) and morphological properties of the toner are investigated. On the basis of the analysis of droplet breakage, coalescence, and growth in the preparation of the polymerized toner, several factors affecting the droplet size, PSD, and morphology of the toner, i.e., agitation speed, ultrasonic introduction, and addition of different types and amounts of surfactants and pigment, are experimentally studied. Surface treatments of the pigment are conducted by modification with coupling agents and in situ prepolymerization. It is shown that surface modification of the pigment particles with coupling agents destroys the stabilization of the droplet, resulting in unexpectedly large particles. However, the in situ prepolymerization treatment is capable of maintaining the required PSD.
A prestress force identification method for externally prestressed concrete uniform beam based on the frequency equation and the measured frequencies is developed. For the purpose of the prestress force identification accuracy, we first look for the appropriate method to solve the free vibration equation of externally prestressed concrete beam and then combine the measured frequencies with frequency equation to identify the prestress force. To obtain the exact solution of the free vibration equation of multispan externally prestressed concrete beam, an analytical model of externally prestressed concrete beam is set up based on the Bernoulli-Euler beam theory and the function relation between prestress variation and vibration displacement is built. The multispan externally prestressed concrete beam is taken as the multiple single-span beams which must meet the bending moment and rotation angle boundary conditions, the free vibration equation is solved using sublevel simultaneous method and the semi-analytical solution of the free vibration equation which considered the influence of prestress on section rigidity and beam length is obtained. Taking simply supported concrete beam and two-span concrete beam with external tendons as examples, frequency function curves are obtained with the measured frequencies into it and the prestress force can be identified using the abscissa of the crosspoint of frequency functions. Identification value of the prestress force is in good agreement with the test results. The method can accurately identify prestress force of externally prestressed concrete beam and trace the trend of effective prestress force.
Traditional statistical pattern identification methods, such as artificial neural network and support vector machine, have limited ability to identify minor damage of bridges. Deep learning can mine the inherent law and representation level of sample data. As a typical algorithm of deep learning, convolutional neural network is a feedforward neural network with deep structure and convolution calculation, and its ability of image identification is very outstanding. The recurrence graph of structural response can reveal the internal structure, similarity, and damage information. The original structure response signal involves the coupling vibration of vehicle and bridge is filtered and reconstructed by wavelet packet, and then the recurrence graph of different damage cases is obtained, which is used as the input image of convolutional neural network as a new type of damage feature; thus, a damage identification method based on convolutional neural network and recurrence graph is established. The results of numerical simulation and model experiment show that the recurrence graph contains more damage information; compared with the traditional statistical pattern identification methods, convolutional neural network can achieve more accurate feature extraction and identification through intelligent learning layer by layer, so as to realize more accurate identification of damage location and damage degree.
The current methods of optimal sensor placement are majorly presented based on modal analysis theory, lacking the consideration of damage process of the structure. The effect of different minor damage cases acting on the total spatial structure is studied based on vulnerability theory in structural analysis. The concept of generalized equivalent stiffness is introduced and the importance coefficient of component is defined. For numerical simulation, the random characteristics for both structural parameters and loads are considered, and the random samples are established. The damage path of each sample is calculated and all the important members on the damage failure path are listed; therefore the sensor placement scheme is determined according to the statistical data. This method is extended to dynamic analysis. For every dynamic time-history analysis, time-varying responses of the structure are calculated by selecting appropriate calculating interval and considering the randomness of structural parameters and load. The time-varying response is analyzed and the importance coefficient of members is sorted; finally the dynamic sensor placement scheme is determined. The effectiveness of the method in this paper is certified by example.
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