Ensuring the accuracy of free-field inversion is crucial in determining seismic excitation for soil-structure interaction (SSI) systems. Due to the spherical and cylindrical diffusion properties of body waves and surface waves, the near-fault zone presents distinct free-field responses compared to the far-fault zone. Consequently, existing far-fault free-field inversion techniques are insufficient for providing accurate seismic excitation for SSI systems within the near-fault zone. To address this limitation, a tailored near-fault free-field inversion method based on a multi-objective optimization algorithm is proposed in this study. The proposed method establishes an inversion framework for both spherical body waves and cylindrical surface waves and then transforms the overdetermined problem in inversion process into an optimization problem. Within the multi-objective optimization model, objective functions are formulated by minimizing the three-component waveform differences between the observation point and the delayed reference point. Additionally, constraint conditions are determined based on the attenuation property of propagating seismic waves. The accuracy of the proposed method is then verified through near-fault wave motion characteristics and validated against real downhole recordings. Finally, the application of the proposed method is investigated, with emphasis on examining the impulsive property of underground motions and analyzing the seismic responses of SSI systems. The results show that the proposed method refines the theoretical framework of near-fault inversion and accurately restores the free-field characteristics, particularly the impulsive features of near-fault motions, thereby providing reliable excitation for seismic response assessments of SSI systems.

To address the challenge of the complex and extensive seismic design elements of tunnels, which are difficult to be accurately described using mathematical functions, a novel model combining convolutional neural networks (CNN), gated recurrent units (GRU), and an attention mechanism is proposed. Firstly, based on actual engineering examples, the tunnel dimensions and site soil information are determined to establish a numerical model of tunnel seismic response and verify its reliability. Then, the soil parameters, seismic motion amplitude, tunnel depth, and overlying water depth are selected for systematic analysis of the displacement momentum (DM) and time of maximum damage occurrence (TMDO). The parameters with higher influence are chosen as input variables, while the calculated DM and TMDO from the reliable numerical model are selected as the output variables to be predicted. Next, integrating the GRU model to capture long-term dependencies in time series, the CNN model to extract spatial features, and the attention mechanism to handle complex relationships among multiple variables, the CNN-GRU-Attention prediction model was established. By generating dataset samples through numerical simulation, accurate predictions of DM and TMDO were achieved. Finally, using the proposed model to establish the objective function relationship between input and output parameters, employing the NonDominated Sorting Genetic Algorithm II (NSGA-II) to find the optimal input design features, achieving the optimal design of tunnel seismic performance. The results show that: (1) The calculation results of the numerical model for tunnel seismic response conform to general research findings, indicating sufficient reliability. (2) The error compensation and dynamic updating mechanisms improved prediction accuracy. The R2 values for the training set reach 0.973 and 0.982 respectively. (3) Optimizing DM and TMDO using the NSGA-II algorithm leads to a 23.42% reduction in DM and a 18.71% increase in TMDO. After optimization, tunnel displacement is reduced, damage is delayed, and seismic performance is significantly improved.

The discrete element method (DEM) is proving to be a reliable tool for studying the behavior of granular materials and has been increasingly used in recent years. The accuracy of a DEM model depends heavily on the accuracy of the particle property parameters chosen which is of vital importance for studying the mechanical properties of granular materials. However, the existing DEM parameter calibration methods are limited in terms of applicability, and the trial-and-error method remains the most common way for DEM parameter calibration. This paper presents a novel calibration method for DEM parameters using the multi-objective tree-structured parzen estimator algorithm based on prior physical information (MOTPE-PPI). The MOTPE-PPI does not rely on the training datasets and may optimize with every single test, significantly reducing the computational efforts for DEM simulation. Moreover, MOTPE-PPI is suitable for a variety of contact models and damping parameters in DEM simulation, showing robust applicability and practical feasibility. Taking an example, the DEM parameters of sandy gravel material collected from Dashixia rockfill dam in China are calibrated using MOTPE-PPI in the paper. The prior physical information is obtained through a series of triaxial loading-unloading tests, single-particle crushing tests, and literature research. Seven parameters in the rolling resistance linear contact model and breakage model are considered, and the optimization process takes only 25 iterations. Through quantitative comparison with existing parameter calibration methods, the high efficiency and wide applicability of the DEM parameter calibration method proposed in this study. The calibrated DEM parameters are used to investigate the hysteretic behavior and deformation characteristics of the granular material, revealing that the accumulation of plastic strain and resilient modulus is related to confining pressure, stress level, and the number of cycles.

Context: Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops. Objective: The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop. Methods: Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems. Results and conclusions: The results showed MOGA strategy performers good, the minimum power cost is only need 0.084*day(-1) in warm weather condition, as well as 3.74 *day(-1) in cold weather condition, the uninfected LAI (m(2)[Leaves](m(-2)[soils]*day(-1))) is the range of [0.14 0.20]. The yearly power cost at least [308 1365].These are able to embed within a control scheme for achieving optimization purpose. Significance: The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth.