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The center of polarization induced by hybrid elements and the size effect have been proven to be effective ways to enhance microwave absorption. The effects of specific surface area ratio and doping of elements (P, S, N) on microwave absorption characteristics were studied using almond shells as the raw material, and the particle size effect was evaluated using ball milling. The modified material displayed a doped conjugated porous structure and red phosphorus implanted carbon nanotubes onto the bio-based surface. Interestingly, cement was used as an absorbing substrate to make the absorbing composites, showing the fascinating electromagnetic interference shielding effectivenesses (EMI SEs) and microwave absorption properties. For example, S-BPAS with a thickness of 3.15 mm obtained a reflection loss (RL) of 99.42 dB, whereas BPAS with an ultra-low thickness of 1.20 mm obtained a wide effective bandwidth of 5.38 GHz. In particular, the total SE of BPAS and P-BPAS in the x and ku bands is ≥90%. The research results open up new prospects for the development of advanced functional materials that can save energy and purify electromagnetic pollution. [ABSTRACT FROM AUTHOR]

This study aimed to rigorously evaluate the impact of tuned mass dampers (TMDs) on structural response under seismic excitation. By strategically placing TMDs at various levels within the structures, the research sought to determine their effectiveness in mitigating structural movement. A single-degree-of-freedom (SDOF) system incorporating TMDs was utilized to model structures of 10, 13, and 16 stories, each configured with TMDs at different heights. The structures were subjected to near-fault earthquakes to assess the efficacy of TMDs in reducing structural response. The findings revealed significant enhancements in structural performance when TMDs were optimally positioned. Specifically, a 50% reduction in both acceleration and displacement, alongside a 65% decrease in maximum drift, underscored the effectiveness of TMD deployment. Furthermore, the study demonstrated that distributing multiple TMDs along the height of the structure provided superior drift control. Notably, positioning TMDs within the upper one-third of the structure yielded the most pronounced improvements in acceleration, displacement, and maximum drift. Finally, the research indicates that the strategic incorporation of TMDs can significantly enhance the seismic resilience of structures. The results highlight the substantial benefits of TMDs in optimizing acceleration, displacement, and drift, thereby affirming their critical role in seismic design and retrofitting strategies. [ABSTRACT FROM AUTHOR]

The uniaxial compressive strength (UCS) of rock is an essential property of rock material in different relevant applications, such as rock slope, tunnel construction, and foundation. It takes enormous time and effort to obtain the UCS values directly in the laboratory. Accordingly, an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance. This study presents powerful boosting trees evaluation framework, i.e., adaptive boosting machine, extreme gradient boosting machine (XGBoost), and category gradient boosting machine, for estimating the UCS of sandstone. Schmidt hammer rebound number, P-wave velocity, and point load index were chosen as considered factors to forecast UCS values of sandstone samples. Taylor diagrams and five regression metrics, including coefficient of determination (R²), root mean square error, mean absolute error, variance account for, and A-20 index, were used to evaluate and compare the performance of these boosting trees. The results showed that the proposed boosting trees are able to provide a high level of prediction capacity for the prepared database. In particular, it was worth noting that XGBoost is the best model to predict sandstone strength and it achieved 0.999 training R² and 0.958 testing R². The proposed model had more outstanding capability than neural network with optimization techniques during training and testing phases. The performed variable importance analysis reveals that the point load index has a significant influence on predicting UCS of sandstone. [ABSTRACT FROM AUTHOR]

To provide lateral resistance in structures as well as buildings, there are some types of structural systems such as shear walls. The utilization of lateral loads occurs on a plate on the wall's vertical dimension. Conventionally, these sorts of loads are transferred to the wall collectors. There is a significant resistance between concrete shear walls and lateral seismic loading. To guarantee the building's seismic security, the shear strength of the walls has to be prognosticated by using models. This paper aims to predict shear strength by using Artificial Neural Network (ANN), Neural Network-Based Group Method of Data Handling (GMDH-NN), and Gene Expression Programming (GEP). The concrete's compressive strength, the yield strength of transverse reinforcement, the yield strength of vertical reinforcement, the axial load, the aspect ratio of the dimensions, the wall length, the thickness of the reinforced concrete shear wall, the transverse reinforcement ratio, and the vertical reinforcement ratio are the input parameters for the neural network model. And the shear strength of the reinforced concrete shear wall is considered as the target parameter of the ANN model. The results validate the capability of the models predicted by ANN, GMDH-NN, and GEP, which are suitable for use as a tool for predicting the shear strength of concrete shear walls with high accuracy. [ABSTRACT FROM AUTHOR]

AbstractFunctionally graded triply periodic minimal surface (FG-TPMS) structures are known as bio-inspired structures. They possess some remarkable advantages such as porous structures with high inter-connectivity, mathematically controllable geometry features and smooth surfaces. As another advantage, FG-TPMS structures can be fast and numerously manufactured by 3D printing technology. Nevertheless, modeling these structures is a challenging task. This paper investigates static bending and free vibration behaviors of FG-TPMS shells. The proposed formulation is established upon isogeometric analysis (IGA) and first-order shear deformation shell theory (FSDT). The governing equations are discretized by a Galerkin weak form and numerically solved by using non-uniform rational B-Spline (NURBS) basis functions. Exact geometries of structures are described via NURBS basis functions. A fitting technique is used to compute the mechanical characteristics of FG-TPMS materials. We investigate behaviors of FG-TPMS shells considering three types of cell geometries which are primitive (P), gyroid (G), I-graph and wrapped package-graph (IWP), and six porosity distribution patterns. The present solutions are verified with the reference ones in the literature. Effects of boundary, type of cell geometry, porosity distribution pattern, length-to-radius and thickness ratios on behaviors of FG-TPMS shells are rigorously studied. Especially, many numerical results of FG-TPMS shells are first proposed in this paper. [ABSTRACT FROM AUTHOR]