Axial Compression Performance and Residual Strength Calculation of Concrete-Encased CFST Composite Columns Exposure to High Temperature

Concrete-encased concrete-filled steel tube (CFST) composite columns provide high bearing capacity, good seismic performance and an easier connection with arbitrary angle beams, which are widely used in high-rise buildings. Considering the high frequency of building fires, experimental research investigated the axial compressive behavior of the composite columns’ exposure to high temperature in this paper. Fourteen specimens after exposure to high temperatures with different parameters, including the heating temperature, steel tube diameter and concrete cover thickness, were fabricated to test under axial compressive loading. The failure pattern, load-displacement curve, bearing capacity, initial stiffness, deformation performance and damage rule of the specimens were discussed. The test results showed obvious differences in damage of specimens subjected to various high temperatures. The failure of the specimens began with the spalling and crushing of the concrete at the edge and ends in a lantern shape. The load-displacement curves of the specimens were significantly affected by high temperature, while the influence the of steel tube diameter and concrete cover thickness was relatively weak. A method of calculating axially loaded capacity for the composite column exposure to high temperature is proposed considering the effects of the main parameters of heating temperature and steel tube position, and the calculated results are in good agreement with the experimental results.

An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks

This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program.

The Effect of Changing Stirrup Spacing and Hook Angle on RC Cantilever Beams with Industrial Iron Chips Waste

In this study, the usability of industrial iron chips waste was investigated in order to provide recycling in the production of reinforced concrete cantilever beams with different stirrup spacing and hook angle. In the concrete produced for cantilever beams, aggregates not larger than 4 mm in diameter were reduced by 20% and replaced with iron chips waste. Cantilever beams are manufactured with stirrup spaces of 50, 100 and 150 mm. The hook angles of the stirrups are differentiated to be 90 and 135 degrees. The experimental setup was prepared in such a way that one side of the samples was fixed, and the other side was free. The loading process was done from the end point of the released side. Load-Displacement curves of cantilever beams were obtained. In the research, it was observed that although 20% iron chips added cantilever beams experienced a decrease in their strength compared to the reference beams, they increased their ductility values at all three different stirrup spaces. As the stirrup spacing widened, the ductility values decreased. However, the effect of iron chips additive on ductility has increased. Samples with stirrup hook angle of 135 degrees increased both strength and ductility values compared to samples with 90 degrees.

NUMERICAL AND THEORETICAL STUDIES ON DOUBLE STEEL PLATE COMPOSITE WALLS UNDER COMPRESSION AT LOW TEMPERATURES

Double steel plate composite walls (DSCWs) with several unique types of connectors have been implemented to protect offshore oil exploration platforms from concentric forces caused by ice in the Arctic region. This paper investigates the compressive perfor-mance of DSCWs with interlocked J-hooks and overlapped headed studs at low temperatures ranging from 20 ℃ to -80 ℃ with nonlinear finite element models (FEMs). The intricate geometric size of the concrete, multiple interactions of the concrete with the connectors, and material nonlinearities of the concrete have been thoroughly simulated. The reasonable consistency between the results of the monotonic tests and finite element analysis (FEA) on nine DSCWs with interlocked J-hooks and seven DSCWs with overlapped headed studs indicates that the FEMs can effectively predict the compressive performance of the DSCWs at low temper-atures. On the basis of the validated FEMs, the effects of the horizontal and vertical spacing of the connectors on the compressive performance of the DSCWs are studied. Finally, theoretical models of the load-displacement curves are developed to reveal the compressive response of DSCWs at low temperatures with different types of connectors, taking into account the restraining effect of steel plates on the inner concrete and the local buckling of steel plates. Compared with previous tests and FEA, the developed theoretical models have reasonable consistency for the load-displacement curves of DSCWs at low temperatures.

Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms

High–speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load–displacement curves presenting pop-in, slope changes, or instabilities from curves exhibiting a typical loading path in large nanoindentation datasets. Classification of the curves was achieved with a deep learning model, specifically, a convolutional neural network (CNN) model implemented in Python using TensorFlow and Keras libraries. Load–displacement curves (with pop-in and without pop-in) from various materials were input to train and validate the model. The curves were converted into square matrices (50 × 50) and then used as inputs for the CNN model. The model successfully differentiated between pop-in and non-pop-in curves with approximately 93% accuracy in the training and validation datasets, indicating that the risk of overfitting the model was negligible. These results confirmed that artificial intelligence and computer vision models represent a powerful tool for analyzing nanoindentation data.

Research on Hysteretic Models for Prestressed and Non-Prestressed Steel Reinforced Concrete Frames

The beam-column fibre model is used to simulate the entire hysteretic process of the prestressed and non-prestressed steel reinforced concrete frame, and the results are compared with the test results. Based on the analysis of a large number of parameters, the hysteretic curve characteristics of this kind of composite frame are discussed, and the load-displacement hysteretic models of single-storey and single-span composite frame are established. The models can comprehensively consider the influence of axial compression ratio and column slenderness ratio and can predict the hysteretic behaviour of this kind of composite frame under horizontal loads. The load-displacement hysteretic models are consistent with the numerical simulation results. Relevant research can provide reference for simplifying the elastic-plastic dynamic analysis of structures.