公路路基沉降灰色预测方法之探讨

根据公路路基沉降规律和GM(1,1)模型单调指数变化的特性,提出了对原始数列进行分析,合理选择GM(1,1)模型或灰色Verhulst模型。若原始数列处于快速沉降阶段,宜选用GM(1,1)模型;若原始数列处于沉降饱和阶段,宜选用灰色Verhulst模型,并用数学方法证明了灰色Verhulst模型的合理性。     

灰色系统理论在建筑物变形分析中的应用

介绍了灰色系统理论的原理以及计算过程,并应用其对建筑物的变形进行定量分析与变形预报,取得较好的效果。     

Evaluation method for Green jack-up drilling platform design scheme based on improved grey correlation analysis

This paper presents an evaluation method for green jack-up drilling platform(GJDP) design scheme based on the improved grey correlation analysis(IGCA). Under the situation of the low-carbon economy it is important and complicated for jack-up drilling platform design to select the optimum design scheme from the feasible schemes. In this paper, the evaluation index system for GJDP has been proposed which includes 3 factors: advanced technology, economic feasibility and environmental coordination, as well as 13 sub-indices. At the same time, a multi-objective combinatorial optimization model is established by using IGCA, in which the analysis hierarchy process (AHP) is used to obtain the weight values of the indices. The IGCA method is fully considered the connection of the indices reflecting the objective true nature of the things, and the subjective interest of the platform owner and the needs of the designer. This research can not only conduct to green design of jack-up drilling platform, but also can contribute to the development, popularization and application of low-carbon technology on offshore platform. Finally, the case study results demonstrate the feasibility and efficiency of the proposed evaluation method.     

利用端点延拓提高LS + NN模型的UT1-UTC预报精度

现有UT1-UTC预报模式在进行周期项与残差项拟合分离时,通常没有考虑最小二乘拟合序列的端部效应,预报精度难以取得较大提高。针对最小二乘拟合存在的端部效应,首先采用灰色模型在UT1-UTC序列的两端进行数据延拓,形成一个新序列,然后对新序列进行最小二乘拟合,最后再联合最小二乘和神经网络(LS+NN)模型对UT1-UTC原始序列进行外推。结果表明,对UT1-UTC序列进行端点数据延拓再进行最小二乘拟合,能够有效地改善最小二乘拟合序列的端部效应;相对于常规LS+NN模型,端部效应改善的LS+NN模型的UT1-UTC预报精度有一定提高,尤其对中长期预报精度提高更为明显。     

ENTRAINMENT FUNCTION OF SUSPENDED SEDIMENT IN OPEN CHANNELS

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Grey wolf optimizer: a new strategy to invert geophysical data sets

There is no meta‐heuristic approach best suited for solving all optimization problems making this field of study highly active. This results in enhancing current approaches and proposing new meta‐heuristic algorithms. Out of all meta‐heuristic algorithms, swarm intelligence is preferred as it can preserve information about the search space over the course of iterations and usually has fewer tuning parameters. Grey Wolves, considered as apex predators, motivated us to simulate Grey Wolves in the optimization of geophysical data sets. The grey wolf optimizer is a swarm‐based meta‐heuristic algorithm, inspired by mimicking the social leadership hierarchy and hunting behaviour of Grey Wolves. The leadership hierarchy is simulated by alpha, beta, delta and omega types of wolves. The three main phases of hunting, that is searching, encircling and attacking prey, is implemented to perform the optimization. To evaluate the efficacy of the grey wolf optimizer, we performed inversion on the total gradient of magnetic, gravity and self‐potential anomalies. The results have been compared with the particle swarm optimization technique. Global minimum for all the examples from grey wolf optimizer was obtained with seven wolves in a pack and 2000 iterations. Inversion was initially performed on thin dykes for noise‐free and noise‐corrupted (up to 20% random noise) synthetic data sets. The inversion on a single thin dyke was performed with a different search space. The results demonstrate that, compared with particle swarm optimization, the grey wolf optimizer is less sensitive to search space variations. Inversion of noise‐corrupted data shows that grey wolf optimizer has a better capability in handling noisy data as compared to particle swarm optimization. Practical applicability of the grey wolf optimizer has been demonstrated by adopting four profiles (i.e. surface magnetic, airborne magnetic, gravity and self‐potential) from the published literature. The grey wolf optimizer results show better data fit than the particle swarm optimizer results and match well with borehole data.     

An Intelligent Method for Geochemical Discrimination of Tectonic Settings of Basalt Based on Olivine Composition: GWO-SVM Method and its VerificationEI北大核心CSCD

Geochemical discrimination of tectonic settings of basalts has been an important research direction of geochemistry for decades. Olivine is one of the earliest crystallized minerals of basaltic magma, which records a lot of hidden information of the formation and evolution of the magma. Therefore, basic elements in olivine are used to discriminate three tectonic settings, including the mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB). However, it is still difficult to accurately discriminate the tectonic settings by using these diagrams. The machine learning algorithm is introduced to solve the aforementioned problem. The classification performance of the machine learning discrimination method largely depends on the rationality of parameter determination. To this end, the paper proposes a coupling intelligent method for geochemical discrimination of tectonic settings using olivine composition of the basalts based on the grey wolf optimizer (GWO)-optimized support vector machine (SVM), or GWO-SVM for short. GWO is used to seek the optimal parameter combination of SVM to form the optimal mapping relationship between basic elements in olivine and basalt tectonic settings, so as to realize the accurate discrimination of MORB, OIB and IAB. In addition, according to the published geochemical data of basalt samples, the discrimination performance of GWO-SVM is evaluated by means of the simulation experiment, hold-out validation and k-fold cross-validation. The evaluation results are represented by the confusion matrix and its derived evaluation indicators. The results show that GWO-SVM can discriminate the tectonic settings of the basalts based on olivine compositions with overall classification accuracy of up to 85%. Thus, in comparison with the traditional discrimination diagram method, the machine learning discrimination method based on multi-algorithm fusion can significantly improve the discrimination accuracy of basalt tectonic settings. © 2020, Science Press. All right reserved.     

考虑高低温影响因素的金属密封总成性能分析

水下井口系统环空金属密封总成设计是水下油气装备国产化核心技术之一。前期国内相关研究人员已在密封设计、密封机理等方面形成共识,但密封性能评价的全面性和准确性依旧存在不足。鉴于此,提出考虑高低温影响的金属密封总成性能评价方法,构建了“有限元分析+室内测试+无损探伤”的评价流程。评价结果显示:温度是金属密封设计及性能评价过程中不可忽略的重要影响因素;设计温度范围内,该金属密封总成的密封性能及主体结构强度满足设计要求,但仍不同程度地受到温度变化影响;相对于常规方法,该评价方法更加全面准确,可为该领域其它密封设计提供借鉴和参考。     

Multi-stakeholder perspectives on the use and influence of “grey” scientific information in fisheries management

Scientific information on the shrimp and groundfish resources of the Brazil–Guianas continental shelf has been produced by the United Nations Food and Agriculture Organization (FAO) and the Caribbean Regional Fisheries Mechanism (CRFM), in collaboration with the national governments exploiting the resources, since the early 1970s. In spite of the availability of such information, largely as grey literature, there is limited evidence as to the extent it is being used in fisheries management in Trinidad and Tobago. The flow of information between multiple stakeholders − fishing industry, scientists, fisheries managers, policy makers, and fisheries advisory bodies − was studied based on responses to a survey of key individuals to document each of their roles in the creation, distribution, and use of fisheries information. Content analysis of responses was completed to determine the opportunities and barriers for using scientific information in fisheries management. Salience, credibility, and legitimacy of the information were shadowed by barriers that decreased these attributes. Knowledge about the fishery has increased and technical capabilities have been strengthened through research. At the same time, advances in digital technology have made information more accessible. Yet, the high technical content of fisheries information reduced its usefulness to some stakeholders and formal systems do not exist for distributing or measuring the use and influence of such information in decision making. Communication strategies to promote awareness of the scientific information and aligning scientific information with fisheries policy could increase its use and influence. Institutional support for partnerships and education to encourage stakeholder involvement could also facilitate increased influence of scientific information.