The mechanical characteristics and mechanisms of rock failure involve complex rock mass mechanics problems involving parameters such as energy concentration, storage, dissipation, and release. Therefore, it is important to select appropriate monitoring technologies to carry out relevant research. Fortunately, infrared thermal imaging monitoring technology has obvious advantages in the experimental study of rock failure processes and energy dissipation and release characteristics under load damage. Therefore, it is necessary to establish the theoretical relationship between the strain energy and infrared radiation information of sandstone and to reveal its fracture energy dissipation and disaster mechanism. In this study, an MTS electro-hydraulic servo press was used to carry out uniaxial loading experiments on sandstone. The characteristics of dissipated energy, elastic energy, and infrared radiation during the damage process of sandstone were studied using infrared thermal imaging technology. The results show that (1) the transition of sandstone loading from one stable state to another occurs in the form of an abrupt change. This sudden change is characterized by the simultaneous occurrence of elastic energy release, dissipative energy surging, and infrared radiation count (IRC) surging, and it has the characteristics of a short duration and large amplitude variation. (2) With the increase in the elastic energy variation, the surge in the IRC of sandstone samples presents three different development stages, namely fluctuation (stage Ⅰ), steady rise (stage Ⅱ), and rapid rise (stage Ⅲ). (3) The more obvious the surge in the IRC, the greater the degree of local damage of the sandstone and the greater the range of the corresponding elastic energy change (or dissipation energy change). (4) A method of sandstone microcrack location and propagation pattern recognition based on infrared thermal imaging technology is proposed. This method can dynamically generate the distribution nephograph of tension-shear microcracks of the bearing rock and accurately evaluate the real-time process of rock damage evolution. Finally, this study can provide a theoretical basis for rock stability, safety monitoring, and early warning.

This article presents a hybrid method of structural modal parameter identification, based on improved empirical mode decomposition (EMD) and autoregressive and moving average (ARMA). Special attention is given to some implementation issues, such as the modal mixing, false modes, the judgment of the real intrinsic mode function (IMF) of classical EMD, and the difficulty of fixing the order of ARMA. To resolve the existing defects of EMD, an improved EMD (IEMD) that combines frequency band filtering and cluster analysis is proposed in this paper, where frequency band filtering divides the signal into several narrowband signals before the EMD process, and cluster analysis is used to determine the real IMFs. Euclidean distance is used to cluster the decomposition results, with no need to adjust any indexes or thresholds, and only by means of using the nearest distance to efficiently determine the real IMF. Moreover, IEMD is used as a pre-processing tool for ARMA, to resolve the difficulty of fixing its order. The capabilities of the proposed method were compared and assessed using a numerical simulation and an experimental model. The numerical simulation and experimental results showed that the improved method could resolve the modal mixing and false modal problems in the classical EMD process and could automatically identified the real IMFs, while the proposed IEMD was combined with ARMA to successfully identify the frequency and mode shape of the structure. Additionally, since each IMF is a single component signal, it is easy to determine the order of the ARMA model.

Recently, a variety of intelligent structural damage identification algorithms have been developed and have obtained considerable attention worldwide due to the advantages of reliable analysis and high efficiency. However, the performances of existing intelligent damage identification methods are heavily dependent on the extracted signatures from raw signals. This will lead to the intelligent damage identification method becoming the optimal solution for actual application. Furthermore, the feature extraction and neural network training are time-consuming tasks, which affect the real-time performance in identification results directly. To address these problems, this paper proposes a new intelligent data fusion system for damage detection, combining the probabilistic neural network (PNN), data fusion technology with correlation fractal dimension (CFD). The intelligent system consists of three modules (models): the eigen-level fusion model, the decision-level fusion model and a PNN classifier model. The highlight points of this system are these three intelligent models specialized in certain situations. The eigen-level model is specialized in the case of measured data with enormous samples and uncertainties, and for the case of confidence level of each sensor is determined ahead, the decision-level model is the best choice. The single PNN model is considered only when the data collected is somehow limited, or few sensors have been installed. Numerical simulations of a two-span concrete-filled steel tubular arch bridge in service and a seven-storey steel frame in laboratory were used to validate the hybrid system by identifying both single- and multi-damage patterns. The results show that the hybrid data-fusion system has excellent performance of damage identification, and also has superior capability of anti-noise and robustness.

Using waste rubber to partially replace fine aggregate to make rubber concrete can not only reduce black pollution to alleviate the dilemma of natural sand resource depletion, but also improve the frost resistance of concrete, which is undoubtedly a win–win solution. Aim to promote the application of rubber concrete seasonal cold regions, it is of great significance to evaluate and predict its frost-resistance. Different from ordinary concrete, the existence of rubber changes the inherent characteristics of concrete to varying degrees, which makes the durability of rubber concrete more complicated and the establishment of prediction models more challenging. In this paper, an artificial neural network (ANN) model was proposed to predict the frost-resistance of rubberized concrete. Using water-cement ratio, cement, sand, sand rate, rubber content and the number of freeze–thaw cycles as input variables and relative dynamic elastic modulus as output variables, a three-layer BP neural network (BPNN) prediction model with a hidden layer was established on the basis of a large number of experimental data of another author. The prediction results show that the proposed BPNN model has a strong ability to predict the frost resistance of rubberized concrete with satisfactory accuracy (R² = 0.9825, MAPE = 1.5609%), which opens up a new way to improve the prediction accuracy of frost-resistance for rubberized concrete.

In order to improve the identification accuracy of damage detection and evaluation based on the vibration response, this paper presents a structural damage identification method based on the fractal dimension, data fusion and a revised counter-propagation network (RCPN). Firstly, the fractal dimensions of the original signal response are extracted through data preprocessing. Secondly, the first-time fusion of data (i.e., the feature-level fusion) is carried out, after which these data are used as the input for the RCPN, to identify and decide the initial damage. Finally, the second-time data fusion (i.e., based on the decision results of the feature-level fusion) is carried out, leading to decision-level fusion. In order to verify the validity of the proposed method, a four-storey benchmark structure of ASCE is used for damage identification and comparison, using a single RCPN decision and the data fusion damage identification method, respectively. The results show that the proposed method is more accurate and reliable than the results of single RCPN decision and feature-level fusion decision, and has good noise resistance and robustness.

Ferroelectric is a great potential material as new solar cells, light driver and optical sensor because of its anomalous photovoltaic effect. Bismuth ferrite and Nd-doped barium titanate thin films were prepared via sol–gel spin-coating method in the present study. The experimental results show that substitution of Nd³⁺ ions for Ba²⁺ on A sites leads to the decrease of band gap, and the short circuit photocurrent density, open circuit photovoltage and power conversion efficiency of Nd-doped barium titanate thin films begin to increase and reach the maximum and then decrease as Nd content increases. It has been found that the band gap of bismuth ferrite thin films annealed at 550–650^oC was between 2.306 eV and 2.453 eV. The short circuit photocurrent density decreased with the rise of annealing temperature, and the open circuit photovoltage and the power conversion efficiency of bismuth ferrite thin films annealed at 550^oC were higher than the thin films annealed at higher temperature.