首页|Reports from Shanghai Jiao Tong University Describe Recent Advances in Machine L earning (A Combined Machine Learning/search Algorithm-based Method for the Ident ification of Constitutive Parameters From Laboratory Tests and In-situ Tests)
Reports from Shanghai Jiao Tong University Describe Recent Advances in Machine L earning (A Combined Machine Learning/search Algorithm-based Method for the Ident ification of Constitutive Parameters From Laboratory Tests and In-situ Tests)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Machine Learning have been prese nted. According to news originating from Shanghai, People’s Republic of China, b y NewsRx correspondents, research stated, “Accurate numerical analysis in geotec hnical engineering heavily relies on the constitutive model and its parameters. The advanced constitutive model can describe the complex mechanical behaviors of soil that may involve a number of parameters.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Science & Technology Commission of Shanghai Mu nicipality (STCSM). Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “However, determining the values of constitutive parameters always r elies on manual trial-and-error, which can be a time-consuming process and not c onducive to widespread application. This paper presents an identification method that combines machine learning with search algorithm based on the laboratory an d in-situ testing. Initially, the sensitivity of constitutive parameters was ana lyzed by investigating the effects of variations in soil overconsolidation and s tructural parameters on the results of triaxial and pressuremeter tests. Subsequ ently, the initial state parameter values and material control parameter ranges of the soil can be identified from the triaxial tests, this is achieved by using the neural network model. In order to accurately determine the parameters value , the numerical model was established based on in-situ pressuremeter test, and t raversal algorithm was implemented to search for the optimal fit values within t he range of material control parameters. Finally, the proposed identification me thod was applied to layers 3 - 5 of Shanghai clay, and the inverted parameters e xhibited a good fit with the outcomes of triaxial tests and pressuremeter tests. ”
ShanghaiPeople’s Republic of ChinaAs iaAlgorithmsCyborgsEmerging TechnologiesMachine LearningSearch Algorit hmsShanghai Jiao Tong University
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