報(bào)告題目：Reliability analysis using hybrid advanced machine learning models

報(bào)告人：Behrooz Keshtegar教授/博導(dǎo)/國際杰青

報(bào)告時(shí)間：2025年1月3日（周二），上午9：30

報(bào)告地點(diǎn)：明志樓B504

報(bào)告人簡介：Behrooz Keshtegar is a staff member in Department of Civil Engineering at University of Zabol. He received his Ph.D. from Sistan and Baluchestan, Zahedan, Iran in 2013. His research fields focused on the multidisciplinary topics such as; structural reliability analysis, reliability–based design optimization, machine learning methods and optimization -based population approaches. He has published more than 150 peer-reviewed papers in scientific journal. He received a prize of university of Zabol for four most cited papers published in JOF and CMAME journals in 2022. He got distinguished top researcher form university of Zabol at 2018 to 2020. He received young researcher of civil engineering from the academy of Science of Iran, IRAN at 2022 and he cited in list of world’s top 2% scientists since 2019。

報(bào)告內(nèi)容摘要：The accuracy, efficiency, stable results are major issues of the computational reliability methods. The performance-based accuracy and efficiency with robust searching scheme for determining the most probable point (MPP) is proposed by hybrid strategies given by first order reliability method (FORM) and hybrid support vector regression (SVR) coupled by population-based optimization method of global best particle swarm optimization (GPSO). The PSO and GPSO combined with SVR and also applied for MPP search are compared using traditional FORM iterative algorithms for accuracy, robustness and efficiency through several engineering problems.

主辦單位：機(jī)電工程學(xué)院

石油天然氣裝備教育部重點(diǎn)實(shí)驗(yàn)室

四川省深層地?zé)崮苎b備技術(shù)工程研究中心

四川省地?zé)崮荛_發(fā)與利用工程技術(shù)研究中心

石油天然氣裝備技術(shù)四川省科技資源共享服務(wù)平臺

成都市機(jī)械工程學(xué)會

科學(xué)技術(shù)發(fā)展研究院