The timely analysis of deformation monitoring data and reasonable diagnosis of the structural health are key tasks in dam health monitoring studies. This article presents a spatio-temporal clustering and health diagnosis method for super-high concrete arch dams that uses deformation monitoring data obtained from plumb meters. The spatio-temporal expression of the deformation monitoring data is proposed first by upgrading a punctuated time series to a curved panel time series, including cross-sectional, dam axial, and temporal changing directions. Second, a comprehensive similarity indicator on three aspects, namely, the absolute distance, incremental distance, and growth rate distance, is constructed after a deep discussion on deformation similarity characteristics both temporally and spatially. Next, the temporal clustering method is proposed by keeping the key features, namely, extreme points and turning points, while eliminating extraneous details, namely, noise points. Finally, the optimal spatio-temporal clustering of dam deformation is achieved by designing a multi-scale fuzzy C-means method of data mining and its iterative algorithm. The proposed method is applied to the Jinping-I hydraulic structure, which is the highest concrete arch dam in the world. The clustering results is quite sensitive in different weight coefficients of the comprehensive similarity indicator and clustering numbers of fuzzy C-means method. The dam deformation behaviors on high-water-level, water-falling, and low-water-level periods are analyzed and diagnosed. The advanced version of proposed methods is verified by comparative analysis on dam health diagnosis results obtained from ordinary deformation distribution figures and the spatio-temporal clustering figures. The proposed method will facilitate the recognition of abnormal deformation areas and associated safety diagnoses.

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Real-time monitoring of the actual elastic modulus is essential and necessary to ensure the safe operation of arch dams. The zoning elastic modulus of a high arch dam is inversed by using deformation safety monitoring data in the operation period, based on the particle swarm optimization with gravitation search algorithm for support vector machine (PSOGSA-SVM) method. Firstly, the measured data of multipoints with a pendulum are separated to construct the initial sample training set; then, an optimal inversion model is established to reflect the complex nonlinear relationship between the mechanical parameters of the high arch dam and the deformation of measured points; finally, the PSOGSA-SVM method is used to train and dynamically update the training set so as to realize the optimization solution of the inversion model. The proposed inversion method is successfully applied to a high arch dam in China to verify its feasibility and validity. The results show that the actual elastic modulus of the dam body is much larger than the initial elastic modulus, which is beneficial to structural stability.

Vesicle-inducing protein in plastids 1 (Vipp1) is thought to play an important role both in thylakoid biogenesis and chloroplast envelope maintenance during stress. Vipp1 is conserved in photosynthetic organisms and forms a high homo-oligomer complex structure that may help sustain the membrane integrity of chloroplasts. This study cloned two novel VIPP1 genes from Triticum urartu and named them TuVipp1 and TuVipp2. Both proteins shared high identity with the homologous proteins AtVipp1 and CrVipp1. TuVipp1 and TuVipp2 were expressed in various organs of common wheat, and both genes were induced by light and various stress treatments. Purified TuVipp1 and TuVipp2 proteins showed secondary and advanced structures similar to those of the homologous proteins. Similar to AtVipp1, TuVipp1 is a chloroplast target protein. Additionally, TuVipp1 was able to rescue the phenotypes of pale leaves, lethality, and disordered chloroplast structures of AtVipp1 (-/-) mutant lines. Collectively, our data demonstrate that TuVipp1 and TuVipp2 are functional proteins in chloroplasts in wheat and may be critical for maintaining the chloroplast envelope under stress and membrane biogenesis upon photosynthesis.

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Abstract:In this study, efforts are focused on the comparison and validation of standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products-Version-7 3B42RT estimates before and after assimilation by using a Kalman filter with independent rain gauge networks located within the Jinghe basin of China. Generally, the direct comparison of TMPA precipitation estimates to 200 collocated rain gauges from 2006 to 2008 demonstrate that the spatial and temporal rainfall characteristics over the region are well captured by the assimilation estimates. Especially, results also show that using Kalman filter to assimilate TRMM-based multi-satellite real-time precipitation estimates tends to perform well over regions, where gauge network is rather sparse. Last, this study highlights that accurate detection and estimation of precipitation in the summer season by Kalman filter, particularly for nonlinear convective precipitation events, is still a challenging task for the future development of assimilation technique for improving the satellite-based precipitation accuracy.

Structural health monitoring (SHM) has been widely employed to reveal the hidden safety information and to diagnose the safety status in dam engineering fields. As one of the most important parameters of SHM, crack opening displacement (COD) is often used to evaluate the cracks or joints of concrete dams. In this paper, a new dam health analytic perspective is introduced by integrating the data mining method into SHM field, focusing on revealing the association rules in COD monitoring data. The association rules are investigated systematically, considering the cause-effect relations between external loads and structural response, the temporal characteristics of time series for a single sensor, the spatial characteristics of monitoring data for multisensors, and the abnormal characteristics for different items of structural responses. The association relation is quantified by proposing the quantitative indexes, including support degree, confidence degree, and promotion degree. The methods are used in the COD monitoring data of the Baishan concrete gravity-arch dam, which is located in a severely cold area in northeastern China. Results show that 4 out of 24 cause-effect association rules are extracted by calculating the association degree of monitored COD values, and 21 out of 24 crack sensors present a temporal association relationship, among which the confidence degree of two sensors reaches 100%. The variation trend of COD values is relevant with the locations of the crack sensors. These results are consistent with the dam safety monitoring theories and models, which would be very useful for extracting the SHM information between different sensors, predicting the trend of COD value and repairing the monitoring data series of COD sensors, or even for discovering an abnormal signal for the operation safety of dams.

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