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.

Sensitivity analysis of influencing factors on frost resistance is carried out in this paper, and a two-stage neural network model based on grey theory and Back Propagation Neural Networks (BPNNs) is established for the sake of predicting the frost resistance of active-admixture recycled concrete quickly and accurately. Firstly, the influence degree of cement, water, sand, natural aggregate, recycled aggregate, mineral powder, fly ash, fiber and air-entraining agent on the frost resistance of active-admixture recycled-aggregate concrete was analyzed based on the grey system theory, and the primary and secondary relationships of various factors were effectively distinguished. Then, the input layer of the model was determined as cement, water, sand, recycled aggregate and air-entraining agent, and the output layer was the relative dynamic elastic modulus. A total of 120 datasets were collected from the experimental data of another author, and the relative dynamic elastic modulus was predicted using the two-stage BPNN prediction model proposed in this paper and compared with the BPNN prediction results. The results show that the proposed two-stage BPNN model, after removing less-sensitive parameters from the input layer, has better prediction accuracy and shorter run time than the BPNN model.

The load-bearing capacity of a bidirectional laminated slab with shear keys arranged in a rectangular grid (BCSWSK) was investigated to determine its mechanical properties, using load tests on a fabricated laminated slab and finite element models. The shear force distribution across the shear keys was measured and analysed, and the effects of different parameters were identified. The interfaces between the cast bottom slab and the shear keys were strongly bonded, as was the interface between the precast bottom slab and the cast-in-place upper slab, despite it being a secondary concrete pouring. The shear force was distributed similarly along the X direction (columns) and the Y direction (rows) of the shear key arrangement. The shear forces along the Y direction were greater and reached a maximum value sooner, but the differences between columns were mostly less than 10%. A square cross-section is recommended for the shear keys. The number of rows, number of columns, and the cross-sectional area of the shear keys are the main factors influencing the mechanical properties of the composite slab, but as they individually increased, they reached a point at which further increase had little effect. Similarly, after a certain number of shear keys were used, the concrete grade cast in situ, the shear key concrete grade, and the friction coefficient between the precast bottom slab and the cast-in-place upper slab had little influence on the BCSWSK.

This study proposes a new type of method for the connection of the prefabricated coupling beam and shear wall to solve the problems in the construction of assembled frame‐shear wall structures. To investigate the seismic properties of this new type of assembled coupling beam, experiments with 2 groups of test pieces under low‐period cycling loads were completed. Furthermore, this study studied some important influence factors. Results show that this novel coupling beam has a similar performance to the cast‐in‐place coupling beam. The bearing capacity and energy dissipation capacity of cast‐in‐place structures increase with the increase in end block thickness and the ductility inversely, yet the bearing capacity, seismic energy dissipation capacity, and ductility of assembled coupling beam structure decrease with the increase in end block thickness. Numerical simulation shows that increasing the area of equivalent steel bars will increase its yield load but will also reduce the seismic energy dissipation capacity and ductility. The thicker the shear wall thickness, the higher specimen’s bearing capacity, the stronger the seismic energy dissipation capacity, and the worse the ductility is.