Damage Identification in Beam-Type Structures Using Pseudo Strain Energy Density and Grey Relation Coefficient

Based on pseudo strain energy density (PSED) and grey relation coefficient (GRC), an index is proposed to locate the damage of beam-type structures in time-domain. The genetic algorithm (GA) is utilized to identify the structural damage severity of confirmed damaged locations. Furthermore, a systematic damage identification program based on GA is developed on MATLAB platform. ANSYS is employed to conduct the finite element analysis of complicated civil engineering structures, which is embedded with interface technique. The two-step damage identification is verified by a finite element model of Xinxingtang Highway Bridge and a laboratory beam model based on polyvinylidens fluoride (PVDF). The bridge model was constructed with 57 girder segments, and simulated with 58 measurement points. The damaged segments were located accurately by GRC index regardless of damage extents and noise levels. With stiffness reduction factors of detected segments as variables, the GA program evolved for 150 generations in 6 h and identified the damage extent with the maximum errors of 1% and 3% corresponding to the noise to signal ratios of 0 and 5%, respectively. In contrast, the common GA-based method without using GRC index evolved for 600 generations in 24 h, but failed to obtain satisfactory results. In the laboratory test, PVDF patches were used as dynamic strain sensors, and the damage locations were identified due to the fact that GRC indexes of points near damaged elements were smaller than 0.6 while those of others were larger than 0.6. The GA-based damage quantification was also consistent with the value of crack depth in the beam model.

Relationship of Remote Sensing Normalized Differential Vegetation Index to Anopheles Density and Malaria Incidence Rate

Objective To study the relationship of remote sensing normalized differential vegetation index （NDVI） to Anopheles density and malaria incidence rate. Methods Data of monthly average climate, environment, Anopheles density and malaria incidence rate, and remote sensing NDVI were collected from 27 townships of 10 counties in southeastern Yunnan Province from 1984 to 1993. The relationship of remote sensing ecological proxy index, NDVI, to Anopheles density and malaria incidence rate was studied by principal component analysis, factor analysis and grey correlation analysis. Results The correlation matrix showed that NDVI highly correlated with Anopheles density in 4 townships of Mengla, Jinghong, and Yuanjiang counties, but in other 23 townships the relationship was not clear. Principal component and factor analyses showed that remote sensing NDVI was the representative index of the first principal component and the first common factor of Anopheles density evaluation. Grey correlation analysis showed that in rainy season NDVI had a high grey correlation with Anopheles density and malaria incidence rate. The grey correlation analysis showed that in rainy season the grey degree of NDVI correlated with Anopheles. Minimus density was 0.730, and 0.713 with Anopheles sinensis density, and 0.800 with malarial incidence rate. Conclusion Remote sensing NDVI can serve as a sensitive evaluation index of Anopheles density and malaria incidence rate.

Characterization of alpine meadow surface crack and its correlation with root-soil properties

Quantifying surface cracks in alpine meadows is a prerequisite and a key aspect in the study of grassland crack development.Crack characterization indices are crucial for the quantitative characterization of complex cracks,serving as vital factors in assessing the degree of cracking and the development morphology.So far,research on evaluating the degree of grassland degradation through crack characterization indices is rare,especially the quantitative analysis of the development of surface cracks in alpine meadows is relatively scarce.Therefore,based on the phenomenon of surface cracking during the degradation of alpine meadows in some regions of the Qinghai-Tibet Plateau,we selected the alpine meadow in the Huangcheng Mongolian Township,Menyuan Hui Autonomous County,Qinghai Province,China as the study area,used unmanned aerial vehicle(UAV)sensing technology to acquire low-altitude images of alpine meadow surface cracks at different degrees of degradation(light,medium,and heavy degradation),and analyzed the representative metrics characterizing the degree of crack development by interpreting the crack length,length density,branch angle,and burrow(rat hole)distribution density and combining them with in situ crack width and depth measurements.Finally,the correlations between the crack characterization indices and the soil and root parameters of sample plots at different degrees of degradation in the study area were analyzed using the grey relation analysis.The results revealed that with the increase of degradation,the physical and chemical properties of soil and the mechanical properties of root-soil composite changed significantly,the vegetation coverage reduced,and the root system aggregated in the surface layer of alpine meadow.As the degree of degradation increased,the fracture morphology developed from"linear"to"dendritic",and eventually to a complex and irregular"polygonal"pattern.The crack length,width,depth,and length density were identified as the crack characterization indices via analysis of variance.The results of grey relation analysis also revealed that the crack length,width,depth,and length density were all highly correlated with root length density,and as the degradation of alpine meadows intensified,the underground biomass increased dramatically,forming a dense layer of grass felt,which has a significant impact on the formation and expansion of cracks.