In order to solve the problem of low computational efficiency of adaptive beamforming algorithms in ultrasonic imaging, an adaptive beamforming algorithm for ultrasonic array with the combination of spatial sampling and coherence factor was proposed. The maximum decimation factor with different numbers of array elements was deduced according to the beam pattern. The sparse echo data was obtained by spatially sampling the whole array element data using the maximum decimation factor. Therefore, the amount of data used for beamforming was greatly reduced. Taking the spatial sampling data as the input of a beamformer and constructing the covariance matrix as Toeplitz matrix, the adaptive weights of the sampling data were obtained according to the principle of minimum variance. Then, the adaptive weights were modified by introducing the coherence factor to highlight the effective information of the sampling data. Under the case of unequal data and spatial sampling data, the proposed algorithm, minimum variance algorithm and minimum variance algorithm combined with coherence factor were used to simulate the imaging of cracks and cross-drilled holes respectively. The results show that: for unequal data, the imaging quality of the proposed algorithm is between the other two algorithms; in terms of imaging time, compared with the other two algorithms, the average imaging time of the proposed algorithm is reduced by more than 85%. For the same spatial sampling data, the imaging quality of the proposed method is better than the other algorithms; in terms of imaging time, compared with the other two algorithms, the average imaging time of the proposed algorithm is reduced by more than 65%.

The stress state of the cable is related to the safety of the cable system bridge, and the cable force value is an important index to measure the mechanical states of the cable. At present, the difficulty of determining the cable boundary conditions is an important factor affecting the accuracy of the cable force identification results. The ANSYS was used to numerically simulate the cable vibration, and the reliability of the modeling method was verified by the existing cable force calculation formula and the simulation data was generated. Then taken cable length, line density, bending stiffness, first-order frequency, second-order frequency, and third-order frequency as the input parameters, and used cable force as output parameter combined with vibration simulation data to establish BP neural network and generalized regression neural network cable force prediction model. Two neural network cable force prediction models and the existing cable force calculation formula were applied to actual projects for comparison and verification. The results showed that the neural network structure of the BP neural network cable force prediction model was 6–13–13–1, the activation functions between the input layer and the hidden layer 1, the hidden layer 1 and the hidden layer 2, the hidden layer 2 and the output layer were tansig, tansig, purelin, the training algorithm was the L–M optimization algorithm trainlm, the learning rate was 0.1, the number of network iterations was 1 000, the display interval was 100, the mean square error was 0.001, the prediction effect of the cable force prediction model was good, but there was room for further optimization. The best spread value of the generalized regression neural network cable force prediction model was 0.002 15, the prediction effect of the cable force prediction model was better than that of the BP neural network and the existing cable force calculation formula, and the forecast error was basically controlled within 5%. Utilizing the generalized regression neural network to predict the cable force of the bridge can avoid the influence of the judgment error of the cable boundary condition on the accuracy of the cable force recognition result, and improve the accuracy of the cable force recognition, which has a good engineering application value.

Surface roughness of structures is a primary factor that affects the mechanical properties of soil-structure interface. To further study the effect of roughness on shear strength of interface, large-scale direct shear tests were performed on clay-concrete interface under different roughness conditions and the influence mechanism of roughness on peak shear strength of interface was revealed. The results showed that the shear stress-displacement curves of clay-concrete interface exhibited strain-softening under different roughness conditions, and the greater roughness, the more obvious peak point of curve. Increasing roughness could obviously increase the peak shear strength of interface and there existed a critical roughness in terms of its influence on peak shear strength of interface. Morphological characteristics of the shear failure plane of different rough interfaces indicated that the smooth interface mainly occurred shear slip failure during the shearing process, and the friction and occlusion between clay particles and concrete were strengthened with increasing roughness, which resulted in the internal shear failure of clay. The shear strength of interface can be approximately divided into two parts: the shear strength of smooth interface and the shear strength of soil in rough parts. A new peak shear strength model of interface considering roughness was established by introducing a roughness-related coefficient into Jewell’s model and proposing a function to describe the relationship between the coefficient and roughness. Finally, comparison results between calculated value and test value showed that maximum relative error was 11.01% and mean relative error was 4.74%, which verified the accuracy and rationality of the proposed model.

In order to improve the denitration performance of La-Mn perovskite catalyst, a series of Ce modified perovskite La-Mn composite oxide catalysts were synthesized by citric acid sol-gel method. The structure, morphology, composition and surface physicochemical properties of the catalysts were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), N2 adsorption-desorption (BET) and temperature programmed technology (H2-TPR/NH3-TPD).The results of the activity test showed that the denitration performance of Ce modified perovskite type La-Mn composite oxide catalysts are improved. When the Ce/Mn molar ratio is 0.2, the catalyst has the best denitration activity. The NOx conversion rate could reach 90% at 135 ℃, and maintaining more than 90% NOx conversion in the temperature window range of 135～260℃. XRD results showed that the perovskite type La-Mn composite oxide modified by Ce has porous structure and could maintain the perovskite structure of LaMnO3.15. However, Ce ions do not completely enter the perovskite structure, and some of them cover the catalyst surface in the form of oxides. At the same time, part of Mn ions in the lattice overflow from the perovskite structure in the form of Mn3O4, thus maintaining the structural stability and charge balance. SEM and BET results showed that the specific surface area of the catalyst increases and more active sites are provided after the introduction of Ce, which promotes the denitration reaction. XPS results showed that Ce modified catalyst produces more Mn4+ and chemically adsorbed oxygen, which promotes the oxidation of NO. The results of temperature programmed technology showed that the catalyst modified by Ce has better redox performance and more acidic sites, which is conducive to the denitration reaction. Therefore, Ce modified La-Mn composite oxide could improve the denitration performance by promoting NO oxidation and NH3 adsorption.