摘要：Compared with the inherent brittleness of bulk silicon(Si)at ambient temperature,the nanosized Si materials with very high strength,plasticity,and anelasticity due to size effect,are all ***,the ultimate stretchability of Si nanostructure has not yet been demonstrated due to the difficulties in experimental ***,directly performing in-situ tensile tests in a scanning electron microscope after developing a protocol for sample transfer,shaping and straining,we report the customized nanosized Si mechanical metamaterial which overcomes brittle limitations and achieves an ultra-large tensile strain of up to 95%using the maskless focused ion beam(FIB)*** unprecedented characteristic is achieved synergistically through FIB-induced size-softening effect and engineering modification of mechanical metamaterials,revealed through analyses of finite element analysis,atomic-scale transmission electron microscope characterization and molecular dynamics *** work is not only instructive for tailoring the strength and deformation behavior of nanosized Si mechanical metamaterials or other bulk materials,but also of practical relevance to the application of Si nanomaterials in nanoelectromechanical system and nanoscale strain engineering.

摘要：A general initial water penetration(seepage) fracture criterion for concrete is proposed to predict whether or not harmful water penetration(hydraulic fracturing),other than microcracking,will occur in concrete structures in a severe high water pressure *** final regression,of the different macroscopic failure types in concrete to microscopic ModeⅠ c racking,allows the use of only one universal criterion to indicate the ***,a general initial water penetration fracture criterion is approximately defined as a strain magnitude of 1000×10-6,based on the concept of tensile strain derived from experimental results in the relevant ***,the locations of harmful water penetration fracture(hydraulic fracture) in the high arch dam mass of the Jinping first class hydropower project are analyzed using the nonlinear finite element method(FEM) according to the proposed *** proposed criterion also holds promise for other concrete structures in high water pressure environments.

摘要：In recent years,extensive research has focused on applying machine learning(ML)techniques to predict the properties of engineered cementitious composites(ECCs).ECCs exhibit crucial characteristics such as compressive strength(CS),tensile strength(TS),and tensile strain(TSt).Accurate forecasting of these critical properties can reduce material waste,lower construction expenses,and expedite project timelines for engineers and *** study investigates mixture design components and corresponding strengths of ECCs based on only polyethylene fiber drawing from existing *** neural network(ANN)models are developed to predict CS,TS,and TSt using a dataset of 339 experimental results with twelve input *** ANN models,implemented in MATLAB,consider various hidden layers and neurons to optimize accuracy and validation metrics demonstrate the model’s high *** analysis explores individual parameter *** inspiration from this study,it would be advantageous to enhance the predictive modeling toolkit by leveraging the progress made in existing technologies,thereby driving the green and low-carbon development of civil *** approach not only improves the efficiency and sustainability of construction practices but also aligns with global environmental goals by reducing the carbon footprint associated with civil engineering projects.