Seismic fragilities of single-column highway bridges with rocking column-footing

Rocking isolation has been increasingly studied as a promising design concept to limit the earthquake damage of civil structures. Despite the difficulties and uncertainties of predicting the rocking response under individual earthquake excitations (due to negative rotational stiffness and complex impact energy loss), in a statistical sense, the seismic performance of rocking structures has been shown to be generally consistent with the experimental outcomes. To this end, this study assesses, in a probabilistic manner, the effectiveness of using rocking isolation as a retrofit strategy for single-column concrete box-girder highway bridges in California. Under earthquake excitation, the rocking bridge could experience multi-class responses (eg, full contacted or uplifting foundation) and multi-mode damage (eg, overturning, uplift impact, and column nonlinearity). A multi-step machine learning framework is developed to estimate the damage probability associated with each damage scenario. The framework consists of the dimensionally consistent generalized linear model for regression of seismic demand, the logistic regression for classification of distinct response classes, and the stepwise regression for feature selection of significant ground motion and structural parameters. Fragility curves are derived to predict the response class probabilities of rocking uplift and overturning, and the conditional damage probabilities such as column vibrational damage and rocking uplift impact damage. The fragility estimates of rocking bridges are compared with those for as-built bridges, indicating that rocking isolation is capable of reducing column damage potential. Additionally, there exists an optimal slenderness angle range that enables the studied bridges to experience much lower overturning tendencies and significantly reduced column damage probabilities at the same time.     

Modelling groundwater level variations by learning from multiple models using fuzzy logic

Modelling time series of groundwater levels is investigated by three fuzzy logic (FL) models, Sugeno (SFL), Mamdani (MFL) and Larsen (LFL), using data from observation wells. One novelty in the study is the re-use of these three models as multiple models through the following strategies: (a) simple averaging, (b) weighted averaging and (c) committee machine techniques; these are implemented using artificial neural networks (ANN). These strategies provide some evidence that (i) multiple models improve on the performance of individual models and those using committee machines perform better than the other two options; and (ii) committee machine models produce defensible modelling results to develop management scenarios. The study investigates water table declines through management scenarios and shows that in this aquifer water use has higher impacts on water table variations than climatic variations. This provides evidence of the need for planned management in the study area.     

Forecasting monthly precipitation using sequential modelling

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.     

SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry

I describe a configurable machine-learning framework to estimate a suite of continuous and categorical sedimentological properties from photographic imagery of sediment, and to exemplify how machine learning can be a powerful and flexible tool for automated quantitative and qualitative measurements from remotely sensed imagery. The model is tested on a dataset consisting of 409 images and associated detailed label data. The data are from a much wider sedimentological spectrum than previous optical granulometry studies, consisting of both well- and poorly sorted sediment, terrigenous, carbonate, and volcaniclastic sands and gravels and their mixtures, and grain sizes spanning over two orders of magnitude. I demonstrate the model framework by configuring it in several ways, to estimate two categories (describing grain shape and population, respectively) and nine numeric grain size percentiles in pixels from a single input image. Grain size is then recovered using the physical size of a pixel. Finally, I demonstrate that the model can be configured and trained to estimate equivalent sieve diameters directly from image features, without the need for area-to-mass conversion formulas and without even knowing the scale of one pixel. Thus it is the only optical granulometry method proposed to date that does not necessarily require image scaling. The flexibility of the model framework should facilitate numerous application in the spatiotemporal monitoring of the grain size distribution, shape, mineralogy and other quantities of interest of sedimentary deposits as they evolve, as well as other texture-based proxies extracted from remotely sensed imagery. © 2019 John Wiley & Sons, Ltd.     

人工智能技术在数值天气预报中的应用

当前,人工智能迎来第3次发展浪潮并在多个领域大数据分析中取得巨大成功,这为人工智能技术与数值天气预报结合提供了契机.已有大量研究尝试将人工智能技术用于数值天气预报的初值生成、预报和产品应用过程中,涉及观测资料预处理、资料同化、模式积分、后处理以及高性能计算,通过误差估计、参数估计和局部代理等手段使预报结果,得到改进且计...     

南水北调中线工程环境影响与河南生态经济发展

南水北调工程是解决我国北方地区资源性缺水问题的战略措施,是关系国家经济布局的重点工程。文章概述了南水北调中线工程建设情况及其意义,并对中线工程实施后对主要水源区和受水区河南省生态环境与社会经济发展的影响进行了综合评估分析。在此基础上,应用生态经济学理论,提出基于可持续发展的生态农业、生态林业、生态工业、生态旅游业、生态产业及循环经济的河南生态经济发展对策。     

河南省东南边缘地区区域发展研究

由于历史原因,处于河南省边缘地区的区域经济发展落后,属于经济欠发达地区。该区地处黄淮海平原,历史文化底蕴丰厚,区位优势明显,交通条件便利,农业资源丰富。但是,这里公共基础设施建设滞后,产业结构性矛盾突出,农业发展水平低,农民增收困难。根据河南省委、省政府提出“加快工业化、城镇化,推进农业现代化是河南省全面建设小康社会的基本途径”这一发展思路,文章提出了做大做强中心城市、培育产业集群和加强城市分工协作的发展途径。     

我国区域循环经济的制度分析

区域循环经济是发展循环经济的重要层次,而制度则是实施区域循环经济的重要手段。文章从制度经济学的视角对区域循环经济进行剖析,首先讨论了发展区域循环经济对制度的需求,发展区域循环经济不仅有对正式制度的需求,而且还有对非正式制度的需求;然后进一步分析区域循环经济的制度功能,在此基础上重点论述和构建了区域循环经济的一般制度框架。     

新疆县域产业结构转换能力综合评价

产业结构的演进和转换是经济发展的本质特征,也是促进经济持续稳定协调发展的关键。文章从影响区域产业结构转换的技术创新能力、需求能力、供给能力、对外贸易发展、经济政策等因素出发,从5个方面构建了由13个指标组成的区域产业结构转换能力评价指标体系,并在此基础上采用主成分分析、层次分析和均方差3种方法的组合评价法对新疆县域产业结构转换能力进行研究,并提出相应建议。     

河南省农村经济发展的人文环境分析--对16个农村的调查

通过对河南省16个村的调查分析,指出：农村经济发展与劳动力结构、规模、文化素质等存在较强关联,与村域在年内耕地减少量呈现一定相关,尤其与基础设施建设用地面积有密切关系;河南省农产品的商品化程度对农村经济发展的贡献程度较低,而农户兼业化随着时间发展起到一定的带动作用;在农村发展的外部环境中。村域所在县（市）的影响程度大于较大范围的地（市）的影响程度,但地（市）影响程度正逐步加强;在不同的历史时期,影响农村经济发展的人文因子逐渐增多;农村经济发展水平与村域的历史基础也有一定关联。