Accurate wind power assesses are fundamental to maximizing the use of wind power and defend a safe and reliable electricity grid. gradientboosting algorithm with a limit learning machine optimized using particle swar...
详细信息
During the construction of a shield tunnel, it will disturb the surrounding ground and affect the use and structural safety of buildings around the tunnel. The geometric parameters of the tunnel, the operating paramet...
详细信息
During the construction of a shield tunnel, it will disturb the surrounding ground and affect the use and structural safety of buildings around the tunnel. The geometric parameters of the tunnel, the operating parameters of the shield machine, and the geological parameters will affect the degree of disturbance. However, the existing theories and models are difficult to comprehensively consider the interaction of these factors, and it is difficult to accurately predict the response of the formation to solve the above problems. The research is based on the machine learning algorithm to establish a prediction model of stratum settlement caused by shield tunneling, which provides a new idea for real time prediction of the ground response caused by shield tunneling and risk reduction. The main results of this research are as follows: (1) propose a novel quantification method for geological parameters that can comprehensively consider the physical and mechanical properties of the rock,soil layers, and the geometric characteristics of depth and thickness and (2) establish a more robust proxy model and use the k-fold cross-validation method to enhance its performance.
The growth in the interest and research on high-entropy alloys (HEAs) over the last decade is due to their unique material phases responsible for their remarkable structural properties. A conventional approach to disc...
详细信息
The growth in the interest and research on high-entropy alloys (HEAs) over the last decade is due to their unique material phases responsible for their remarkable structural properties. A conventional approach to discovering new HEAs requires scavenging an enormous search space consisting of over half a trillion new material compositions comprising of three to six principal elements. Machine learning has emerged as a potential tool to rapidly accelerate the search for and design of new materials, due to its rapidity, scalability, and now, reasonably accurate material property predictions. Here, we implement machine learning tools, to predict the crystallographic phase and Young's modulus of low-, medium- and high-entropy alloys composed of a family of 5 refractory elements. Our results, in conjunction with experimental validation, reveal that the mean melting point and electronegativity difference exert the strongest contributions to the phase formation in these alloys, while the melting temperature and the enthalpy of mixing are the key features impacting the Young's modulus of these materials. Additionally, and more importantly, we find that the entropy of mixing only negligibly influences the phase or the Young's modulus, reigniting the issue of its actual impact on the material phase and properties of HEAs. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
暂无评论