A frequency response function change method for damage localization and quantification in a shear building under ground excitation

A frequency response function change (FRFC) method to detect damage location and extent based on the change in the frequency response functions of a shear building under the effects of ground excitation was proposed in this paper. The damage identification equation was derived from the motion equations of the system before and after the occurrence of the damage. Efforts to make the FRFC method less model‐dependent were made. Intact system matrices, which could be estimated using the measured data without the need for an analytical model, and the frequency response functions were required for the FRFC method. The effects of measurement noise and model parameter error in the FRFC method were studied numerically. The proposed FRFC method was validated by experimental studies of a six‐story steel building structure with single and multiple damage cases. Copyright © 2012 John Wiley & Sons, Ltd.     

Conditional spectrum‐based ground motion selection. Part I: Hazard consistency for risk‐based assessments

The conditional spectrum (CS, with mean and variability) is a target response spectrum that links nonlinear dynamic analysis back to probabilistic seismic hazard analysis for ground motion selection. The CS is computed on the basis of a specified conditioning period, whereas structures under consideration may be sensitive to response spectral amplitudes at multiple periods of excitation. Questions remain regarding the appropriate choice of conditioning period when utilizing the CS as the target spectrum. This paper focuses on risk‐based assessments, which estimate the annual rate of exceeding a specified structural response amplitude. Seismic hazard analysis, ground motion selection, and nonlinear dynamic analysis are performed, using the conditional spectra with varying conditioning periods, to assess the performance of a 20‐story reinforced concrete frame structure. It is shown here that risk‐based assessments are relatively insensitive to the choice of conditioning period when the ground motions are carefully selected to ensure hazard consistency. This observed insensitivity to the conditioning period comes from the fact that, when CS‐based ground motion selection is used, the distributions of response spectra of the selected ground motions are consistent with the site ground motion hazard curves at all relevant periods; this consistency with the site hazard curves is independent of the conditioning period. The importance of an exact CS (which incorporates multiple causal earthquakes and ground motion prediction models) to achieve the appropriate spectral variability at periods away from the conditioning period is also highlighted. The findings of this paper are expected theoretically but have not been empirically demonstrated previously. Copyright © 2013 John Wiley & Sons, Ltd.     

Conditional spectrum‐based ground motion selection. Part II: Intensity‐based assessments and evaluation of alternative target spectra

In a companion paper, an overview and problem definition was presented for ground motion selection on the basis of the conditional spectrum (CS), to perform risk‐based assessments (which estimate the annual rate of exceeding a specified structural response amplitude) for a 20‐story reinforced concrete frame structure. Here, the methodology is repeated for intensity‐based assessments (which estimate structural response for ground motions with a specified intensity level) to determine the effect of conditioning period. Additionally, intensity‐based and risk‐based assessments are evaluated for two other possible target spectra, specifically the uniform hazard spectrum (UHS) and the conditional mean spectrum (CMS, without variability).It is demonstrated for the structure considered that the choice of conditioning period in the CS can substantially impact structural response estimates in an intensity‐based assessment. When used for intensity‐based assessments, the UHS typically results in equal or higher median estimates of structural response than the CS; the CMS results in similar median estimates of structural response compared with the CS but exhibits lower dispersion because of the omission of variability. The choice of target spectrum is then evaluated for risk‐based assessments, showing that the UHS results in overestimation of structural response hazard, whereas the CMS results in underestimation. Additional analyses are completed for other structures to confirm the generality of the conclusions here. These findings have potentially important implications both for the intensity‐based seismic assessments using the CS in future building codes and the risk‐based seismic assessments typically used in performance‐based earthquake engineering applications. Copyright © 2013 John Wiley & Sons, Ltd.     

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

ABSTRACT

An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations.     

Probabilistic calibration of a distributed hydrological model for flood forecasting

Abstract

The complexity of distributed hydrological models has led to improvements in calibration methodologies in recent years. There are various manual, automatic and hybrid methods of calibration. Most use a single objective function to calculate estimation errors. The use of multi-objective calibration improves results, since different aspects of the hydrograph may be considered simultaneously. However, the uncertainty of estimates from a hydrological model can only be taken into account by using a probabilistic approach. This paper presents a calibration method of probabilistic nature, based on the determination of probability functions that best characterize different parameters of the model. The method was applied to the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model using the Manzanares River basin in Spain as a case study. The proposed method allows us to consider the uncertainty in the model estimates by obtaining the probability distributions of flows in the flood hydrograph.

Citation Mediero, L., Garrote, L. & Martín-Carrasco, F. J. (2011) Probabilistic calibration of a distributed hydrological model for flood forecasting. Hydrol. Sci. J. 56(7), 1129–1149.     

面向预警的台风灾害预评估指标体系探讨

台风灾害预评估对防灾减灾具有重要意义,其指标体系的构建是一个理论性强的基础工作,目前尚无成熟的标准．对灾害预评估的概念从灾害预警的视角做出了进一步的界定;从灾害系统论出发,借鉴自然灾害风险评估与损失评估的指标体系及其评估方法,以台风灾害为研究对象,分析了现有预评估指标体系存在的问题,并提出了一个台风灾害预评估的指标体系,对该指标体系的适用性进行了讨论．     

Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model

Abstract

The accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time.     

Spatial distribution of cadmium and lead in the sediments of the western Anzali wetlands on the coast of the Caspian Sea (Iran)

Spatial distribution patterns of total cadmium (Cd) and lead (Pb), their bioavailable fractions and total organic matter in sediment from Anzali wetlands are provided. Total sediment Pb was higher than Cd (34.95 versus 0.024 μg/g dry weight). The geoaccumulation index indicated that the sediment was “uncontaminated”, but some stations were categorized as “unpolluted” to “moderately polluted”. Less than 0.01 of Pb existed in exchangeable and carbonate fractions. The sum of exchangeable and carbonate-bound fractions of Cd was 42%, suggesting that Cd poses high risk to the aquatic ecosystems. Total Cd and Pb exhibited positive relationships with total organic matter. Considering spatial distribution maps of total and bioavailable fractions of metals suggested that high concentrations of metals does not necessarily indicate high bioavailable fraction. The methodologies we used in this study can be in more effective management of aquatic ecosystems, as well as ecological risk assessment of metals, and remediation programs.     

Evaluation of equivalence between different methods for enumeration of fecal indicator bacteria before and after adoption of the new Bathing Water Directive and risk assessment of pollution

The quality of bathing water is of considerable public importance due to the possibility of fecal contamination. In 2009, Croatia implemented the new European Bathing Water Directive (BWD, 2006/7/EC) establishing stricter microbiological standards for new parameters with new reference methods. This study aims to evaluate the equivalence of different methods according to the old and revised BWD and to provide the possibility of data comparison. Furthermore, the directive requires the establishment of the bathing water profile (BWP) for pollution risk assessment. The estimation of consistency of pollution risk assessment with obtained microbiological results was also performed.     

Heavy metal pollution and assessment in the tidal flat sediments of Haizhou Bay,China

The heavy metal inventory and the ecological risk of the tidal flat sediments in Haizhou Bay were investigated. Results show that the average concentrations of heavy metals in the surface sediments exceeded the environment background values of Jiangsu Province coastal soil, suggesting that the surface sediments were mainly polluted by heavy metals (Cd, Cr, Cu, Mn, Pb and Zn). In addition, the profiles of heavy metals fluxes can reflect the socio-economic development of Lianyungang City, and heavy metals inputs were attributed to anthropogenic activities. Cr, Cu, Pb and Zn were mainly present in the non-bioavailable residual form in surface sediments, whereas Cd and Mn were predominantly in the highly mobile acid soluble and reducible fractions. The ecological risk of the polluted sediments stemmed mainly from Cd and Pb. According to the Sediment quality guidelines (SQGs), however, the adverse biological effects caused by the heavy metals occasionally occurred in tidal flat.