Liangtao Bu

This article proposes a prefabricated steel reactive powder concrete (PSRPC) component. Axial compression tests are conducted with five 3m full-scale column specimens to investigate the specimen damage and failure process and to measure the axial displacement, strain and ultimate bearing capacity in order to study the influences of the RPC strength and the section steel flange thickness on the mechanical properties of the PSRPC. The tests show the following. The ultimate failure mode of the PSRPC is column end splitting. During the loading process, the RPC and the section steel work compatibly, and their strengths are given full play. The lateral effect of the RPC on the section steel and the composite confinement effect of the stirrups and section steel on the RPC core significantly increase the ultimate bearing capacity of the PSRPC, and these effects increase with an increase in the RPC strength. The confinement effect on the RPC core in the enclosed region increases with an increase in the section steel flange thickness, that is, the thicker the section steel flange, the larger the portion of the load taken by the RPC core and the higher the ultimate bearing capacity of the PSRPC. The finite element analysis of PSRPC specimen is carried out by ANSYS software, and the simulation results are in good agreement with the test results. A method for calculating the axial bearing capacity of a full-scale column is proposed, taking the confinement effect of the stirrups and section steel on the RPC core into account.

In order to study the effect of ultra-high performance concrete on steel columns, we propose to use reactive powder concrete (RPC) to outsource and reinforce 6 full-scale eccentric steel columns under initial load. Considering the influence of initial load, eccentricity, and RPC strength on the mechanical performance of eccentrically loaded steel columns, we carry out the secondary eccentrically loaded test. The test results show that the use of RPC to reinforce the eccentrically compressed steel column under secondary loading can significantly improve the ultimate bearing capacity, stability and stiffness. RPC and section steel work well together, and the section of the reinforced members basically conforms to the assumption of a flat section. The ultimate bearing capacity of the specimen increases with the increase of RPC strength and decreases with the increase of initial load and eccentricity. When the ratio of the initial load to the ultimate load reaches 70%, the load capacity of the original member can still increase by about 5 times. Based on the failure mechanism and considering the RPC strength reduction, we derive the ultimate bearing capacity calculation formula, which can be used as a reference for practical reinforcement projects.

To address the problem of low accuracy and poor robustness of in situ testing of the compressive strength of high-performance self-compacting concrete (SCC), a genetic algorithm (GA)-optimized backpropagation neural network (BPNN) model was established to predict the compressive strength of SCC. Experiments based on two concrete nondestructive testing methods, i.e., ultrasonic pulse velocity and Schmidt rebound hammer, were designed and test sample data were obtained. A neural network topology with two input nodes, 19 hidden nodes, and one output node was constructed, and the initial weights and thresholds of the resulting traditional BPNN model were optimized using GA. The results showed a correlation coefficient of 0.967 between the values predicted by the established BPNN model and the test values, with an RMSE of 3.703, compared to a correlation coefficient of 0.979 between the values predicted by the GA-optimized BPNN model and the test values, with an RMSE of 2.972. The excellent agreement between the predicted and test values demonstrates the model can accurately predict the compressive strength of SCC and hence reduce the cost and time for SCC compressive strength testing.