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The structural performance improvement of concrete members is by far a crucial theoretical issue for engineers, and the development of modern smart and composite materials makes it possible to gradually enhance the durability design of the concrete structure. In this study, six beams of the same size and reinforcement ratio, the proposed composite beam (SMA-GFRP-ECC) and five comparative beams (RC, R-ECC, SS-ECC, GFRP-ECC, SMA-ECC), were designed and tested under low-cycle unidirectional cyclic loading and unloading conditions. The energy dissipation capacity, displacement ductility, residual deformation, and self-repairing performance of each concrete beam were evaluated. Afterward, a concise calculation model for the studied composite beam is deduced and developed based on the existing relevant constitutive models and concrete assumptions. The test results indicate that compared with RC beams, the composite reinforced ECC beams show obvious multi-cracking and smaller crack width during the loading process and have good bending ductility. The innovative SMA-GFRP-ECC beam is capable of a high bearing capacity, ductility, and damage self-repairing. The new proposed beam has more than 80% of the maximum crack width recovery capacity during unloading. Hence, the proposed SMA-GFRP-ECC beam is a rather good first attempt of strengthened beams, combining the advantages of SMA, GFRP, and ECC. [ABSTRACT FROM AUTHOR]

Structures such as long-span footbridges, floors, and long cantilevers are vulnerable to vibration serviceability problems under crowd walking, which should be taken into consideration during the structural design, operation, and maintenance stages. Standards have been developed to enable designers to assess the vibration serviceability of structures using simplified load models that simulate crowd-induced loading. To facilitate engineers in quickly selecting appropriate standards for vibration serviceability design, ten current standards were collected which deal with the assessment of structural vibration serviceability under walking loads, including the French "Assessment of vibrational behavior of footbridges under pedestrian loading" (2006), the German "Design of footbridges guideline" (2007), the Chinese "Technical standard for human comfort of the floor vibration" (2019), etc. The ten standards were reviewed and evaluated from three aspects including the crowd loading model, structural response calculation method, and vibration serviceability evaluation standard in this paper. Through summary and comparison between standards, three directions for future improvement and perfection of the standards were proposed: the challenges of the improvement of the standards focus on the establishment of the refined stochastic load model, the analysis of the crowd–structure coupling system, and the modelling of multifactor coupling serviceability evaluation indexes. [ABSTRACT FROM AUTHOR]

Offshore structures, such as oil and gas platforms and offshore wind turbines, are subjected to wind and wave loads simultaneously during their service lifetime. Since the wind and wave states are of significant randomness and dependence, the probabilistic modeling of joint wind and wave conditions plays an essential role in the safety design of offshore structures. Currently, three different methods can be adopted to establish the joint probabilistic model, which, however, are somewhat inconvenient in applications. The recently emerged generative adversarial networks has been demonstrated to be effective in dealing with high-dimensional random variables in several fields. In this study, the implicit joint probabilistic model of joint wind and wave load conditions is developed based on the Wasserstein generative adversarial network with gradient penalty. Long-term metocean reanalysis data of the site in the South China Sea is used to train and validate the model. After one million training steps, high-quality samples that are quite similar to the original data can be generated by the developed model. In addition, statistical comparisons of the generated samples obtained by the C-vine copula approach and the developed generative adversarial network model are performed as well, which demonstrates the effectiveness and superiority of the developed model. [ABSTRACT FROM AUTHOR]

Crowd jumping loads are often simplified to a single-point excitation in the existing Fourier series-based models, most of which lack the data support of crowd jumping experiments. A Fourier series-based multi-point excitation model for crowd jumping loads is herein developed, where two parameters, the jumping frequency, and the time lag shift, are selected to quantify the crowd synchronization. After the verification of 3D motion capture technology, the probability distributions of the jumping frequency and the time lag shift are modeled based on the crowd jumping experiment, in which the trajectories of the reflective markers of 48 test subjects were simultaneously recorded by 3D motion capture technology. Through repeated sampling, the jumping load of each person in a crowd is simulated. This model could offer a useful method for evaluating the vibration performance of assembly structures like grandstands, gymnasiums, and concert venues. [ABSTRACT FROM AUTHOR]

Existing dynamic load factors (DLF) for crowd jumping loads are modeled by identifying the peaks and energies from the corresponding Fourier amplitude spectrum, which may underestimate and overestimate structural responses, respectively. Based on the principle of equal structural responses, this study herein develops an equivalent DLF, using the frequency response function to weight and integrate the power spectral density (PSD) matrix of crowd jumping loads. Firstly, massive PSD matrices of different crowd sizes and different metronome frequencies are simulated using a random field model of crowd jumping loads. Thereafter, the equivalent DLF of different structural damping ratios, crowd sizes, metronome frequencies, and non-exceedance probabilities are investigated, and a model of the equivalent DLF is established accordingly. It is believed that this model could provide an efficient and accurate way to conduct reliability design for structures subjected to crowd jumping loads. [ABSTRACT FROM AUTHOR]

Features of jumping loads are essentially high-dimensional random variables but have been simplistically modeled owing to the lack of proper mathematical tools. Generative adversarial networks (GANs) in conjunction with deep learning technology are adopted herein for modeling the jumping loads. Conditional GANs (CGANs) combined with Wasserstein GANs (WGANs) with gradient penalty (WGANs-GP) are adopted in the impulse modeling, where a multi-layer perceptron and a convolutional neural network are employed for the discriminator and generator, respectively. As for the impulse amplitude sequence and interval sequence modeling, similar CGANs combined with WGANs-GP are adopted, where recurrent neural networks are employed for both the generator and discriminator. A large amount of measured individual jumping loads are utilized in training GANs to ensure the generated samples can simulate the real ones well. After training, the individual jumping loads are simulated by connecting the generated impulse samples, based on the generated impulse amplitude sequence samples and interval sequence samples. The simulated jumping loads can be used to assess the vibration performance of assembly structures, such as grandstands, concert halls, and gym floors. Moreover, the established GANs can be extended to the modeling of other stochastic dynamic excitations. [ABSTRACT FROM AUTHOR]

This paper reports a dataset that provides force time histories of various types of human-induced loads, including walking, jumping, and bouncing activities guided by metronome or music. The dataset contains 3,518 records collected from six experimental campaigns. Ground reaction forces of test subjects were measured using high-precision force plates for individuals and small groups, and the displacements of certain body parts of the test subjects were measured using three-dimensional motion capture technology for large crowds. All data records were converted to a unified format and uploaded to a published dataset. An accompanying website of the human-induced load dataset has also been designed and put online. The website provides a publicly available and type-classified data source of human-induced loads. The dataset will be updated by adding new measurements from both the authors and other researchers. To the author's knowledge, this dataset is the first open access dataset for research on human-induced loads, which is of great significance to future studies on new and advanced load models to understand the crowd load mechanisms under different circumstances and to spark broader data sharing in this area. Moreover, this dataset helps design engineers select real human-induced loads for a time history analysis of structures and fosters future collaboration among researchers and engineers. [ABSTRACT FROM AUTHOR]

Severe vibrations may occur on slender structures like footbridges and cantilever stands due to human-induced loads such as walking, jumping or bouncing. Currently, to develop a load model for structural design, the main features, such as periodicity and stationarity of experimental load records, are artificially extracted and then mathematically modeled. Different physical features have been included in different load models, i.e. no unified load model exists for different individual activities. The recently emerged generative adversarial networks can be used to model high-dimensional random variables. The probability distribution of these variables learned from real samples can be used to generate new samples, avoiding extracting features artificially. In this paper, a new model is proposed which combines the conditional generative adversarial networks and Wasserstein generative adversarial networks with gradient penalty to generate individual walking, jumping and bouncing loads. The generator of the model has five fully connected layers and a one-dimensional convolutional layer, and the discriminator has five fully connected layers. After one million training steps using the experimental records, the generator can generate high-quality samples similar to real samples in waveform. Finally, by comparing the power spectral densities and single degree of freedom system's responses of the generated samples with real samples, it is further proved that the proposed generative adversarial network model can be used to simulate various human-induced loads. Source code of the model along with its trained weights is provided to the readers to further analysis and application. [ABSTRACT FROM AUTHOR]

Modern slender structures such as long-span floors and cantilever stands are sensitive to jumping-induced vibrations. A conventional deterministic Fourier series model for the human jumping load may overestimate a structure's responses in resonance condition. This paper suggests a power spectral density (PSD) function for the individual jumping load, which was treated as a narrowband stationary stochastic process. Experiments were conducted on individual jumping loads resulting in 334 records from 73 subjects. Statistical analysis of the records led to experimental PSD curves on which a symmetrical bilinear function was suggested. The proposed PSD function is centered on the given jumping frequency and its integer multiples. The function's parameters were determined by equating the total energy of the proposed PSD with that of the experimental PSD. Application of the proposed PSD for predicting a floor's peak response via the stochastic vibration theory was then presented. The predictions for an experimental floor model subjected to individual jumping were compared with the measured peak responses. The comparisons demonstrated that the proposed PSD function was applicable for predicting the floor's response to individual jumping. Finally, the framework for calculating crowd-induced structure vibrations using the suggested PSD was also discussed. [ABSTRACT FROM AUTHOR]