This article takes the doubly curved shell as research object, the material properties of graphene platelets reinforced metal foam (GPLRMF) are temperature-dependent, the dynamic model is built by using Newton's second law to study the lowvelocity impact of imperfect GPLRMF doubly curved shells. The partial differential equations are obtained by Euler-Lagrange principle and dispersed by using Galerkin method. Subsequently, the effects of various parameters (including temperature, porosity coefficient, damping, prestress, initial geometric imperfections, etc.) are discussed in sequence. The results indicate that the contact force, contact time and central displacement of GPLRMF doubly curved shells during the impact process are closely related to the above parameters, and an increase in temperature and geometric imperfections will simultaneously increase the contact force and central displacement. By analyzing the low-velocity impact performance of GPLRMF doubly curved shells, an effective analysis method is provided for the design and practical engineering applications of GPLRMF material structures.

Flexural strength is an indirect indicator for measuring the concrete's resistance to tensile stress caused by bending, shrinkage and temperature changes. This study aims to study the flexural strength of crumb rubber concrete (CRC) through mesoscale simulation and experimental testing. The internal structure of CRC was regarded as a five-phase material consisting of rubber, coarse aggregate, mortar, coarse aggregate-mortar interfacial transition zone (A-M ITZ), and rubber-mortar interfacial transition zone (R-M ITZ). The flexural strength of CRC specimens containing rubber particles of different contents, shapes, and sizes was calculated and compared. Mesoscale simulation showed that the addition of rubber reduces the flexural strength of concrete, and the reduction rate is mainly controlled by the content rather than the size, shape, and distribution of rubber particles. The thickness of R-M ITZ is around 0.05 mm, and its effect on the flexural strength of CRC is as low as 1.15% which can be ignored. The incorporation of rubber particles increases the heterogeneity of the internal structure of concrete, which increases the discreteness of the concrete's flexural strength. Numerical simulation also verified that treating the rubber particles as pores did not change the damage pattern of the CRC specimens and resulted in negligible differences in flexural strength. Rubber particles can be simulated through pores when analyze their effect on the flexural strength of concrete.

The ultra-high performance concrete-reinforced concrete(UHPC-RC) composite beam leverages the superior material attributes of both concrete and UHPC, offering enhanced performance in structures susceptible to erosion or cracking. This paper explores the shear behavior of UHPC-RC composite beams through an integrated experimental and numerical simulation approach. The study commences by establishing and validating meso-structures for concrete and UHPC based on lattice principle and material test. It then introduces an experimental setup with five UHPC-RC specimens to scrutinize the effects of UHPC layer, longitudinal reinforcement, and specimen dimensions on shear resistance. Numerical simulations utilizing Finite Element (FE) model and lattice models are executed, with both models demonstrating their capability to accurately predict the bearing capacity of beam and to simulate their mechanical behavior, cracking patterns, and failure processes. This paper innovatively employs a discrete lattice model to overcome the limitations of traditional continuum models in depicting UHPC post-cracking actions, offering a detailed analysis at a finer scale. This study contributes to the body of knowledge by providing a perspective on the failure mechanisms of UHPC-RC composite structures and providing a solid foundation for future research in this field.

Mechanical performance is crucial for 3D printed concrete, as it directly influences the structural load-bearing capacity and safety. However, the inherent complexities and variability of the material pose significant challenges in achieving accurate predictions of its mechanical performance. This study introduces a novel approach to predict the compressive strength (CS) and flexural strength (FS) of 3D printed concrete using machine learning (ML) methods. A comprehensive database containing 254 CS tests and 210 FS tests was established to train four ML models: artificial neural networks, random forest, extreme gradient boosting, and categorical boosting algorithms (CatBoost). The CatBoost model demonstrated superior performance, with R² values of 0.929 for CS and 0.967 for FS on the test set. To provide insights into the model's predictions, partial dependence plots and Shapley Additive Explanations were employed, revealing that the water-to-binder ratio (n(W/B)) and the content of ordinary Portland cement are critical factors influencing CS, while n(W/B) and the content of ground granulated blast furnace slag significantly affect FS. This innovative ML-driven approach offers a robust framework for accurately predicting the mechanical properties of 3D printed concrete, thereby enhancing its application in structural engineering.

This study investigates the effects of cement content (CC) and loading rate on the unconfined compressive strength (UCS) of cemented sand. Three CCs (12%, 14%, and 16%) were tested at loading rates ranging from 0.5%-5%/min. In addition, a novel camera vision-based crack detection method was developed to monitor crack-related parameters-crack size and angle-in real-time during testing. The results revealed significant variations in UCS with changes in CC and loading rate. For example, at a loading rate of 0.5%/min, UCS increased by 31% and 102% in the specimens with 12% and 16% CC, respectively. Moreover, UCS consistently increased with increasing loading rate, with increases of 28%, 36%, and 31% observed for CCs of 12%, 14%, and 16%, respectively, within the 0.5%-1%/min range. However, as the loading rate increased above 1%/min, the rate of UCS increase stabilized or decreased, highlighting the importance of selecting appropriate loading rates for accurate strength evaluation. A correlation between normalized UCS and loading rate is proposed based on this study's findings and previous research. Furthermore, the camera-based crack detection method provides detailed insights into the failure behavior of cemented sand and reveals a clear correlation between loading rate, CC, and the angle of the fracture surface. This study highlights the need to adopt a standardized loading rate of 1%/min in UCS testing of cemented sand and demonstrates the potential of camera vision technology to enhance geotechnical monitoring practices.

This study proposed a new semantic-based library and operator to improve the convergence of genetic programming (GP) in symbolic regression. The suggested library (hierarchical semantic cluster library, HSCL) is a program set in which programs form hierarchical clusters based on their semantics, through which the proposed operator (hierarchical semantic cluster operator, HSCO) performs a hierarchical search to derive an offspring. The validity of HSCO was verified at both the operator and algorithm levels. The percentile rank of HSCO's offspring was in the top 0.3% when compared to exhaustive search (EX)'s offspring, and the computation time of HSCO was only approximately 5% of EX. In a benchmark test using 11 types of algorithms, the algorithm employing HSCO (Iterated local search using HSCO, ILSH) showed the third, second, and fourth best performance in training error, testing error, and program size, respectively.

Considering the limit research and data deficiency of dynamic characteristics of RAC, the variability of dynamic characteristics of RAC from Bayesian updating perspective is of much value. This paper presents an analysis of variability of dynamic characteristics of RAC based on Bayesian approach at the material level. Firstly, for both NAC and RAC, 27 specimens were tested for statistical distribution of dynamic properties by the forced resonance method. The statistical parameters and appropriate probability density functions of the dynamic characteristics were assessed with classical statistical inference. Then, based on Bayesian updating, probabilistic distribution models for dynamic characteristics were proposed with vague-informative priors. Finally, the estimation models of fundamental frequency and damping ratio of RAC based on the Bayesian linear regression were presented. It is found that the variability of both the fundamental frequency and damping ratio of RAC are 12% and 16.2% times higher than that of NAC from classical statistical analysis. The normal distribution and lognormal distribution are the most suitable to describe the dynamic characteristics for NAC and RAC. The variability of dynamic characteristics is also a random variable with normal distribution. The standardized fundamental frequency is positively correlated with generalized modulus of elasticity, while standardized damping ratio is negatively correlated with standardized fundamental frequency.

Ultra-High Strength Concrete (UHSC) is known for its brittleness compared to traditional concrete, which can lead to sudden collapses. When it comes to columns, failures are particularly serious and require the use of confinement models to accurately predict the strength and strain of confined UHSC columns. While previous confinement models exist, many equations either underestimate or overestimate the confinement of concrete due to idealized assumptions and the exclusion of significant variables. This study employs a hybrid machine learning approach to capture the complex interactions in confinement behavior and accommodate a broader range of peak strength and axial strain parameters in UHSC. Statistical performance measures indicate the superiority of the proposed models over existing equations. Through causal inference, the study assesses the effects and relative importance of each parameter on peak strength and axial strain. The visualizations provided by the performance plots helped identify patterns and correlations that would have been difficult to discern through numerical analysis alone. The developed NN-PSO models are proven effective in reasonably predicting the peak strength and axial strain of UHSC columns.