In order to accurately measure the internal stress-strain curve of plain concrete specimens and confined concrete specimens under compression, a new measurement method is proposed, which adopts conventional strain gauges to measure the internal strain data of the specimens, and the micro soil pressure box with ultra-large range is developed to measure the internal stress of the specimens. The uniaxial compression tests of 3 plain concrete specimens and 9 confined concrete specimens are completed, and the macroscopic failure process of the specimens and the stress-strain curves at different internal points are obtained. Combined with the experimental results, the accuracy of the calculation results of several classical confined concrete constitutive models is compared, and a modified constitutive model is proposed. Solid finite element analysis is used to analyze the stress-strain curves at different points inside the specimens, and the prediction accuracy of different constitutive models is compared. On this basis, nonlinear finite element analysis is used to verify the quasi-static test of RC columns, and the accuracy of different constitutive models in the nonlinear analysis at the component level is compared and analyzed. The results show that the measurement method proposed in this study can accurately measure the stress-strain data internal the concrete. The calculation results of the modified constitutive model proposed in this study are in the best agreement with the test results, and have a wide range of applications, which can be applied to the measurement of internal stress-strain curves of other different types of specimens.

This study presents a novel approach to forecasting the evolution of hysteresis stress-strain response of different types of soils under repeated loading-unloading cycles. The forecasting is made solely from the knowledge of soil properties and loading parameters. Our approach combines mathematical modeling, regression analysis, and Deep Neural Networks (DNNs) to overcome the limitations of traditional DNN training. As a novelty, we propose a hysteresis loop evolution equation and design a family of DNNs to determine the parameters of this equation. Knowing the nature of the phenomenon, we can impose certain solution types and narrow the range of values, enabling the use of a very simple and efficient DNN model. The experimental data used to develop and test the model was obtained through Torsional Shear (TS) tests on soil samples. The model demonstrated high accuracy, with an average R 2 value of 0.9788 for testing and 0.9944 for training.