Ya Ge

This study investigates the stochastic vibration response of a laboratory steel-framed glass-deck pedestrian bridge subjected to random pedestrian loads and structural damping ratio uncertainty. A probabilistic pedestrian load model was established by considering walking frequency, body mass and step length as random variables. The probability density evolution method (PDEM) was then adopted to evaluate the time-varying probability density function of the midspan acceleration response. The dynamic responses under single-person walking, four-person simultaneous entry, and four-person interval walking conditions were first analyzed and compared with measured acceleration responses acquired from the laboratory test bridge. In addition, a single-pedestrian high-frequency walking load test with a target cadence of approximately 4.2 Hz was conducted to investigate the bridge response under near-resonant excitation. Subsequently, damping-ratio uncertainty was introduced together with pedestrian-load randomness to investigate the coupled influence of pedestrian excitation and structural damping on the stochastic response. The results show that the acceleration response under four-person group walking is higher and more probabilistically dispersed than that under single-person walking. When structural parameter uncertainties are considered, the peak midspan acceleration increases from 0.1978 m/s² under pedestrian-load randomness alone to 0.2487 m/s², corresponding to an increase of approximately 25.7%. The response mean and standard deviation predicted by PDEM agree well with those obtained from Monte Carlo simulation under the same numerical settings. Overall, the PDEM-based framework provides a probabilistic description of the transient bridge response and quantifies the influence of pedestrian-load randomness and structural damping uncertainty on response variability.

To investigate the effects of incorporating nanomaterials—carbon nanotubes (CNTs) and graphene oxide (GO)—on the axial compressive mechanical properties of alkali-activated recycled aggregate concrete (AARAC) after high-temperature exposure, this study designed 51 sets of specimens with recycled coarse aggregate replacement rate, nanomaterial content, and temperature as the main parameters. Compression tests were conducted to analyze the failure mode and strength variation in AARAC specimens after heating. In addition, microscopic tests, including X-ray diffraction, scanning electron microscopy, and computed tomography (CT scanning), were performed to analyze the microstructural characteristics of the post-heated AARAC specimens. The results indicate that as the replacement rate of recycled coarse aggregate increased from 0% to 100%, the residual compressive strength after exposure to 600 °C decreased from 33.6 MPa to 19 MPa. When 0.1 wt% of CNTs is added, the compressive strength of AARAC after exposure to a high temperature of 600 °C increases by approximately 30.4% compared to that of AARAC without nanomaterial addition. When 0.1 wt% of CNTs and 0.05 wt% of GO are added, the compressive strength after exposure to a high temperature of 600 °C increases by approximately 44.3%, while the size of scattered fragments upon failure increased, and the failure mode appeared more complete. Microscopic test results indicate that the high-temperature treatment did not cause significant changes in the main phase composition of AARAC. The synergistic effect of the nanomaterials CNTs and GO can fully utilize their functions as nucleation sites, pore fillers, and crack bridging agents. By strengthening the Interfacial Transition Zone between the recycled coarse aggregate and the cement paste, refining the Matrix Pore Structure, dispersing local thermal stress, and suppressing the propagation of high-temperature cracks, the mechanical properties of AARAC after high-temperature exposure can be effectively maintained.

To investigate the synergistic reinforcement mechanism of single and combined incorporation of carbon nanotubes (CNTs) and graphene oxide (GO) on the dynamic mechanical properties of alkali-activated recycled aggregate concrete (AARAC), 81 cylindrical specimens were designed with varying dimensions, recycled coarse aggregate (RCA) replacement ratios, and single/combined nanomaterial incorporation schemes. Uniaxial compression tests were conducted to obtain the stress–strain curves of AARAC under different strain rates (10⁻⁵ s⁻¹, 10⁻³ s⁻¹, and 10⁻¹ s⁻¹), and a dynamic constitutive model for AARAC was established. The results indicate that under static conditions (strain rates of 10⁻⁵ s⁻¹ and 10⁻³ s⁻¹), the coupling law between the RCA replacement ratio and nanomaterial dosage is determined by the balance between the defect degree of recycled aggregates and the improvement effect of nanomaterials. Specifically, at a 50% RCA replacement ratio, the single incorporation of 0.1% CNTs can enhance the mechanical properties of AARAC; at a 100% RCA replacement ratio, the synergistic effect of the combined incorporation of 0.1% CNTs and 0.05% GO can mitigate the defects of fully recycled aggregates. In contrast, under dynamic conditions (strain rate of 10⁻¹ s⁻¹), Nanomaterials (CNTs and GO) optimize load transfer efficiency and slow down the process of crack propagation, leading to a much greater improvement in the mechanical properties of AARAC compared to static conditions, with the combined incorporation achieving better performance at a 100% RCA replacement ratio. As the specimen size increases from 75 mm to 150 mm, the increase in static peak strain is relatively small, which is attributed to the more uniform deformation distribution and stronger deformation coordination capability of larger specimens under static loading. Under dynamic loading, the influence law of peak strain and elastic modulus is consistent with that of peak stress. Based on these findings, a dynamic constitutive model for AARAC modified by single and combined incorporation of CNTs/GO was established. The predicted curves of the model are in good agreement with the experimental curves, with an error range of 2.3–7.1%, which can well describe the constitutive relationship of the tested material.

Ground penetrating radar (GPR) is a mature and important research method in the field of structural non-destructive testing. However, when the detection target scale is small and the amount of data collected is limited, it poses a serious challenge for this research method. In order to verify the applicability of typical one-dimensional radar signals combined with convolutional neural networks (CNN) in the non-destructive testing of concrete structures, this study created concrete specimens with embedded defects (voids, non-dense solids, and cracks) commonly found in concrete structures in a laboratory setting. High-frequency GPR equipment is used for data acquisition, A-scan data corresponding to different defects is extracted as a training set, and appropriate labeling is carried out. The extracted original radar signals were taken as the input of the CNN model. At the same time, in order to improve the sensitivity of the CNN models to specific damage types, the spectrums of A-scan are also used as part of the training datasets of the CNN models. In this paper, two CNN models with different dimensions are used to train the datasets and evaluate the classification results; one is the traditional one-dimensional CNN model, and the other is the classical two-dimensional CNN architecture AlexNet. In addition, the finite difference time domain (FDTD) model of three-dimensional complex media is established by gprMax, and the propagation characteristics of GPR in concrete media are simulated. The results of applying this method to both simulated and experimental data show that combining the A-scan data of ground penetrating radar and their spectrums as input with the CNN model can effectively identify different types of damage and defects inside the concrete structure. Compared with the one-dimensional CNN model, AlexNet has obvious advantages in extracting complex signal features and processing high-dimensional data. The feasibility of this method in the research field of damage detection of concrete structures has been verified.