This study focused on a multi-indicator assessment methodology for governmental environmental auditing of water protection programs. The environmental status of Wuli Lake in China was assessed using the global indicat...This study focused on a multi-indicator assessment methodology for governmental environmental auditing of water protection programs. The environmental status of Wuli Lake in China was assessed using the global indicators （driver-status-response） developed by the Commission on Sustainable Development, and four additional indicators proposed by the author： water quality, pollution load, aquatic ecosystem status, and lake sediment deposition. Various hydrological, chemical, biological and environmental parameters were used to estimate the values of the indicators for assessment of environmental status of the lake based on time series data sets for twenty years. The indicators proposed can be customized to meeting the needs for particular assessment of water protection programs. This method can be used to evaluate the performance of national environmental protection programs and provide technical support for environmental auditors.展开更多
Using the method of trophic state-composite index (TSI-CI ) and the 12 months of monitoring data in 2010,we carry out initial exploration of the status of ecosystem health in Wuli Lake. First,we select four indicators...Using the method of trophic state-composite index (TSI-CI ) and the 12 months of monitoring data in 2010,we carry out initial exploration of the status of ecosystem health in Wuli Lake. First,we select four indicators,Chla,SD,TP and TN,to conduct trophic state assessment using weighted index method; then after selecting physical,chemical and biological indicators to conduct nondimensionalization processing,we calculate the composite index and conduct comprehensive assessment. The results show that in 2010,the status of ecosystem health in Wuli Lake was the best in July,worst in August; when the composite trophic state indicators with Chla as the representative increase or decrease significantly and cross different nutritional grades,TSI will significantly deviate from CI,and the relationship between the two in the other time is not prominent.展开更多
This paper presents a new method of damage condition assessment that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness that are statistically nondescribable. In this method, ...This paper presents a new method of damage condition assessment that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness that are statistically nondescribable. In this method, healthy observations are used to construct a fury set representing sound performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed by using the state similarity matrix. Thus, an optimal group fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fitzzy pattern recognition based on an approximate principle . This method can be embedded into the system of Structural Health Monitoring （SHM） to give advice about structural maintenance and life predictio comes from Reference [ 9 ] for damage pattern recognition is presented n. Finally, a case and discussed. The study, which compared result illustrates our method is more effective and general, so it is very practical in engineering.展开更多
The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research...The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.展开更多
基金Project supported by the International Project between The Netherlands Royal Academy of Arts and Sciences and Chinese Academy of Sciences (No. 04CDP014) the National Natural Science Foundation of China (No. 40471130)
文摘This study focused on a multi-indicator assessment methodology for governmental environmental auditing of water protection programs. The environmental status of Wuli Lake in China was assessed using the global indicators （driver-status-response） developed by the Commission on Sustainable Development, and four additional indicators proposed by the author： water quality, pollution load, aquatic ecosystem status, and lake sediment deposition. Various hydrological, chemical, biological and environmental parameters were used to estimate the values of the indicators for assessment of environmental status of the lake based on time series data sets for twenty years. The indicators proposed can be customized to meeting the needs for particular assessment of water protection programs. This method can be used to evaluate the performance of national environmental protection programs and provide technical support for environmental auditors.
基金Supported by Project of Wuxi Municipal Development and Reform Commission (2115019)
文摘Using the method of trophic state-composite index (TSI-CI ) and the 12 months of monitoring data in 2010,we carry out initial exploration of the status of ecosystem health in Wuli Lake. First,we select four indicators,Chla,SD,TP and TN,to conduct trophic state assessment using weighted index method; then after selecting physical,chemical and biological indicators to conduct nondimensionalization processing,we calculate the composite index and conduct comprehensive assessment. The results show that in 2010,the status of ecosystem health in Wuli Lake was the best in July,worst in August; when the composite trophic state indicators with Chla as the representative increase or decrease significantly and cross different nutritional grades,TSI will significantly deviate from CI,and the relationship between the two in the other time is not prominent.
基金This paper is supported by the National High Technology Research and Development Program ("863" Program) of China under Grant No.2006AA04Z437
文摘This paper presents a new method of damage condition assessment that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness that are statistically nondescribable. In this method, healthy observations are used to construct a fury set representing sound performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed by using the state similarity matrix. Thus, an optimal group fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fitzzy pattern recognition based on an approximate principle . This method can be embedded into the system of Structural Health Monitoring （SHM） to give advice about structural maintenance and life predictio comes from Reference [ 9 ] for damage pattern recognition is presented n. Finally, a case and discussed. The study, which compared result illustrates our method is more effective and general, so it is very practical in engineering.
基金funded by the Natural Science Foundation of Fujian Province(Grant No.2020J05207)Fujian University Engineering Research Center for Disaster Prevention and Mitigation of Engineering Structures along the Southeast Coast(Grant No.JDGC03)+1 种基金Major Scientific Research Platform Project of Putian City(Grant No.2021ZP03)Talent Introduction Project of Putian University(Grant No.2018074).
文摘The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.
