首页|Researchers’ Work from Xi’an University of Technology Focuses on Machine Learnin g (Application of an Improved Method Combining Machine Learning-Principal Compon ent Analysis for the Fragility Analysis of Cross-Fault Hydraulic Tunnels)
Researchers’ Work from Xi’an University of Technology Focuses on Machine Learnin g (Application of an Improved Method Combining Machine Learning-Principal Compon ent Analysis for the Fragility Analysis of Cross-Fault Hydraulic Tunnels)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “Machine learning (ML) approa ches, widely used in civil engineering, have the potential to reduce computing c osts and enhance predictive capabilities. However, many ML methods have yet to b e applied to develop models that accurately analyze the nonlinear dynamic respon se of cross-fault hydraulic tunnels (CFHTs).” Financial supporters for this research include National Natural Science Foundati on of China. Our news reporters obtained a quote from the research from Xi’an University of T echnology: “To predict CFHT models and fragility curves effectively, we identify the most effective ML techniques and improve prediction capacity and accuracy b y initially creating an integrated multivariate earthquake intensity measure (IM ) from nine univariate earthquake IMs using principal component analysis. Struct ural reactions are then performed using incremental dynamic analysis by a multim edium-coupled interaction system. Four techniques are used to test ML-principal component analysis (PCA) feasibility. Meanwhile, mathematical statistical parame ters are compared to standard probabilistic seismic demand models of expected an d computed values using ML-PCA. Eventually, multiple stripe analysis-maximum lik elihood estimation (MSA-MLE) is applied to assess the seismic performance of CFH Ts. This study highlights that the Gaussian process regression and integrated IM can improve reliable probability and reduce uncertainties in evaluating the str uctural response.”
Xi’an University of TechnologyXi’anP eople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesEngineeringMa chine Learning
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