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Fang G.-H.,Hohai University | Wen X.,Hohai University | Yu F.-C.,Anhui and Huaihe River Institute of Hydraulic Research
Fresenius Environmental Bulletin

Gucheng Lake has been suffering from eutrophication due to increased pollution and nutrient loads discharged into the watershed. Based on artificial neural networks (ANNs) and a 4-year record of water quality data (from 2006 to 2009), this study proposes an early-warning model for eutrophication aiming to predict the concentration of total nitrogen (TN) and total phosphorus (TP) of Gucheng Lake with a lead time of one week. To develop such data-driven models efficiently, a comprehensive sampling strategy is adopted to ensure that most relevant predictors for TN and TP are retained. Factor correlation analysis is then employed to further eliminate noisy predictors. The preferable selecting ranges of correlation coefficient values are proven to be [-1, -0.5] and [0.5, 1]. As a result, 6 and 18 input variables are filtered from 75 potential input variables to develop the TN and TP prediction models, respectively. The prediction models can achieve high performance. The validation results of TN (TP) showed that the correlation coefficient of 0.9915 (0.9945) and the RMSE of 0.0684 (0.0015), which have demonstrated the potential of ANN models to predict TN and TP conditions at Gu-cheng Lake. Source

Li R.-Z.,Hefei University of Technology | Huang Q.-F.,Hefei University of Technology | Yang J.-W.,Anhui and Huaihe River Institute of Hydraulic Research | Zhang R.-G.,Hefei University of Technology | Jin J.-L.,Hefei University of Technology
Zhongguo Huanjing Kexue/China Environmental Science

A typical agricultural headwater stream was chosen as the representative to investigate the dynamic characteristics of effective flow for nutrient retention over a longer time scale, based on the change of regional hydrology, from the perspective of coupling the discharge probability density function and nutrient retention efficiency. Through the Monte Carlo simulation for discharge probability density function, the overall level of nutrient retention for the target stream was quantitatively evaluated as well as the most effective flow and the functionally equivalent discharge were calculated, according to the nutrient uptake velocity derived from field tracer experiments. The overall levels of retention capability for NH4 + and PO4 3- were quite low. The expected values of the retention efficiency of NH4 + and PO4 3- were 0.0671 (6.71%) and 0.0541 (5.41%), respectively. The most effective flow for NH4 + and PO4 3- were 0.0051m3/s and 0.0049m3/s, and the functionally equivalent discharge for them were 0.044m3/s and 0.043m3/s, respectively. In view of the fact of low nutrient uptake velocity in the stream, it is necessary to improve the nutrient retention efficiency of the target stream by reconstructing stream morphology and streambed geomorphology. © 2016, Editorial Board of China Environmental Science. All right reserved. Source

Zhang J.,Anhui and Huaihe River Institute of Hydraulic Research | Zhang L.,Hohai University
Earthquake Engineering and Engineering Vibration

Based on a Chinese national high arch dam located in a meizoseismal region, a nonlinear numerical analysis model of the damage and failure process of a dam-foundation system is established by employing a 3-D deformable distinct element code (3DEC) and its re-development functions. The proposed analysis model considers the dam-foundation-reservoir coupling effect, influence of nonlinear contact in the opening and closing of the dam seam surface and abutment rock joints during strong earthquakes, and radiation damping of far field energy dissipation according to the actual workability state of an arch dam. A safety assessment method and safety evaluation criteria is developed to better understand the arch dam system disaster process from local damage to ultimate failure. The dynamic characteristics, disaster mechanism, limit bearing capacity and the entire failure process of a high arch dam under a strong earthquake are then analyzed. Further, the seismic safety of the arch dam is evaluated according to the proposed evaluation criteria and safety assessment method. As a result, some useful conclusions are obtained for some aspects of the disaster mechanism and failure process of an arch dam. The analysis method and conclusions may be useful in engineering practice. © 2014 Institute of Engineering Mechanics, China Earthquake Administration and Springer-Verlag Berlin Heidelberg. Source

Yu F.-C.,Anhui and Huaihe River Institute of Hydraulic Research | Fang G.-H.,Hohai University | Shen R.,Anhui and Huaihe River Institute of Hydraulic Research
Environmental Earth Sciences

Comprehensive early warning of drinking water sources is a multi-target, multi-level and multi-factor system. The complex nonlinear relationship between early-warning indicators and warning limit levels has not been founded. In the present study, the combination weight of early-warning indicators was first determined by combining Delphi–Analytic Hierarchy Process subjective weight and entropy objective weight. For uncertain characteristics of qualitative indicators, the grey weight matrix is obtained through grey evaluation theories. The grey-fuzzy comprehensive early-warning model was established based on advantages of combination weights, grey evaluation and fuzzy comprehensive evaluation theories. Then, a multi-factor and multi-level warning system, of which results were represented by ambiguity warning level, was put forward and applied to Gucheng Lake. The results showed that the comprehensive security degree of Gucheng Lake is 5.43, which is between warning level (degree is 6) and heavy warning level (degree is 4). This indicated that Gucheng Lake needs to improve protection measures and enhance the safety of drinking water source. © 2014, Springer-Verlag Berlin Heidelberg. Source

Zhang J.-K.,Anhui and Huaihe River Institute of Hydraulic Research | Yan W.,General Electric | Cui D.-M.,Anhui and Huaihe River Institute of Hydraulic Research
Sensors (Switzerland)

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. © 2016 by the authors; licensee MDPI, Basel, Switzerland. Source

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