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Suzhen Dang,Manfei Yao,Xiaoyan Liu,Guotao Dong 한국기상학회 2019 Asia-Pacific Journal of Atmospheric Sciences Vol.55 No.4
Under the impact of climate change, the variation in the magnitude and frequency of extreme precipitation events has become a major issue worldwide. Based on the daily precipitation data at 9 meteorological stations and 111 precipitation stations in the Hekouzhen-Longmen region (HLR) in the middle reaches of the Yellow River Basin during 1966–2015, the spatial and temporal variations in and statistical probability characteristics of extreme precipitation were investigated. The probability distribution of extreme precipitation in the HLR was simulated with the annual maximum (AM) series and peak over threshold (POT) series by three extreme distributions, namely, generalized extreme value (GEV) distribution, generalized Pareto (GP) distribution, and gamma distribution. The parameters of the GEV, GP and gamma distributions were estimated by the L-moments method, and the best fit model was selected by the Kolmogorov-Smirnov (K-S) test. The results showed that high value area of extreme precipitation was located in the southeast part of the HLR, while the lower value area was mainly distributed in the northwestern part of the HLR and the upper reaches of the Wuding River Basin. A positive trend of annual maximum precipitation was detected at half of the stations, and the other half of the stations were found to show a negative trend from 1966 to 2015, while the changing trends of extreme precipitation at almost all of the stations were nonsignificant. From the results of the K-S test, the GEV distribution can better fit the AM series, and the GP distribution can better fit the POT series. The estimated extreme precipitation of the AM series is larger than that of the POT series with the same return period at most stations.
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Formation and failure characteristics of a landslide induced by excavation in western Henan, China
Haining Liu,Suzhen Duan,Han Dong Liu,Weiguo Wang,Yujie Jia 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.7
Engineering construction in mountainous areas is a key factor inducing slope failure, whichposes severe threats to life and property safety during construction and operation. Thus, clearunderstanding of slope structure and potential failure mechanisms is of great importance forslope reinforcement. The Shangge landslide, located in a mountainous area of WesternHenan, China, was triggered by excavation along a toll station. The slope was still unstableafter a row of anti-sliding piles were constructed. To clarify the deformation characteristics andfailure mechanism of this landslide and provide reinforcement guidance for slopes with similargeological conditions, a combined method of field investigation, displacement monitoring,and numerical simulation was employed. The results indicate that the natural slope comprisesa dual structure of soil and rock, and that the soil, with poor permeability and high watermoisture, was prone to form a potential sliding surface. Secondly, slope excavation was thedirect triggering factor. Lastly, but most importantly, the preliminary investigation work failed toaccurately expose the potential position of the sliding surface, and the existing anti-slide pilesfailed to cross the potential sliding surface, which made them unable to effectively improveslope stability. The numerical simulation verified that the recommended piles installed on theplatform of level 2 could significantly improve slope stability. Therefore, when carrying outengineering slope design in similar geological environments, the slope structure and materialcomposition should be fully identified, and the influence of groundwater on the slope stabilitystate should be emphasized. These results can provide a reference for similar slopereinforcement designs.
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Liu Xin,Zhang Heng,Liu Qiang,Dong Suzhen,Xiao Changshi 대한조선학회 2021 International Journal of Naval Architecture and Oc Vol.13 No.1
Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the “curse of dimensionality” when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.
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