Damage Assessment of Reinforced Concrete Structures through Damage Indices:A State-of-the-Art Review

Due to the developments of computer science and technology in recent years,computer models and numerical simulations for large and complicated structures can be done.Among the vast information and results obtained from the analysis and simulations,the damage performance is of great importance since this damage might cause enormous losses for society and humanity,notably in cases of severe damage occurring.One of the most effective tools to handle the results about the damage performance of the structure is the damage index(DI)together with the damage states,which are used to correlate the damage indices with the damage that occurred in the actual structures.Numbers of damage indices proposed and developed rely on the fact that the damage causes noticeable changes in the structural and dynamic properties of the structural components or the whole structure.Therefore,this study presents a comprehensive review of the damage assessment of Reinforced Concrete(RC)structures.It presents step by step the development of the damage indices that are most widely used to estimate the performance of structural components in the structure and subsequently assess the damage degree of such these structures either based on the structural properties or dynamic properties of the structure.Also,several damage states have been introduced to estimate the performance level of the structure.Finally,case studies,methodologies,and applications on the damage assessment of RC structures are reviewed and presented.

Two Network Structure Indicators for Conventional Public Transit

The significance of network structure indicators for the planning and management of conventional public transit is widely acknowledged.In order to improve and enrich the conventional public transit assessment system,two network structure indicators are proposed.Firstly,according to the obvious defects lying in the traditional no-linear coefficient,the realistic no-linear coefficient γRNL,a modified no-linear coefficient indicator,is put forward,which takes into account the effects of barriers in a city.Secondly,to cover the gap of an indicator which can reflect the coverage homogeneity of a transit network,the length dimension LDis proposed on the basis of Fractal Theory.Finally,a case study is applied to verify the validity and practicability of the two indicators in problem diagnosis using regression analysis.The results validate that γRNLcan evaluate the detour of bus lines more reasonably than the previous no-linear coefficient because it reflects the layout of bus lines,and LDcan represent the rate of change of the network density,adding a new member to the scheme of network structure indicators for public transit.

A new image watermarking framework based on levels-directions decomposition in contourlet representation

With the development of digital information technologies,robust watermarking framework is taken into real consideration as a challenging issue in the area of image processing,due to the large applicabilities and its utilities in a number of academic and real environments.There are a wide range of solutions to provide image watermarking frameworks,while each one of them is attempted to address an efficient and applicable idea.In reality,the traditional techniques do not have sufficient merit to realize an accurate application.Due to the fact that the main idea behind the approach is organized based on contourlet representation,the only state-of-the-art materials that are investigated along with an integration of the aforementioned contourlet representation in line with watermarking framework are concentrated to be able to propose the novel and skilled technique.In a word,the main process of the proposed robust watermarking framework is organized to deal with both new embedding and de-embedding processes in the area of contourlet transform to generate watermarked image and the corresponding extracted logo image with high accuracy.In fact,the motivation of the approach is that the suggested complexity can be of novelty,which consists of the contourlet representation,the embedding and the corresponding de-embedding modules and the performance monitoring including an analysis of the watermarked image as well as the extracted logo image.There is also a scrambling module that is working in association with levels-directions decomposition in contourlet embedding mechanism,while a decision maker system is designed to deal with the appropriate number of sub-bands to be embedded in the presence of a series of simulated attacks.The required performance is tangibly considered through an integration of the peak signal-to-noise ratio and the structural similarity indices that are related to watermarked image.And the bit error rate and the normal correlation are considered that are related to the extracted logo analysis,as well.Subsequently,the outcomes are fully analyzed to be competitive with respect to the potential techniques in the image colour models including hue or tint in terms of their shade,saturation or amount of gray and their brightness via value or luminance and also hue,saturation and intensity representations,as long as the performance of the whole of channels are concentrated to be presented.The performance monitoring outcomes indicate that the proposed framework is of significance to be verified.

Structural Traits,Structural Indices and Body Weight Prediction of Arsi Cows

Structural measurements are indicators of animal performance,productivity and carcass characteristics.This study was conducted with the objectives of assessing structural measurements,developing body weight prediction and structural indices for cows of Arsi breed.The cows were purchased from highland and lowland agro-ecologies of Arsi and East Shoa zones of Oromia regional state,Ethiopia and kept in Adami Tulu Agricultural Research Center(ATARC)for the breed development purpose.Totally 222 cows were included in the structural traits measurement.Thirty four young heifers were also considered in the study.Twenty two structural traits were considered during observational survey.The structural index was calculated from the phenotypically correlated linear measurements.Structural traits were analyzed by T-test of SPSS version twenty four.The observed average values of height at wither,chest depth,heart girth,body length,pelvic width,cannon bone circumferences of the cows were 107,55.62,141.06,117.82,31.41 and 13.58cm,respectively.Heart girth(0.82),flank girth(0.73),hook circumferences(0.67),chest depth(0.65)and height at rump(0.64)were highly correlated(P<0.01)to body weight of the cows.Regression analysis indicated that hearth girth had the highest coefficient of determination for body weight of the cows and heifers.Accordingly,the simple linear equations were developed to predict the body weight of cows and heifers.Body weight of Arsi cow(y)=-221.005+3.1(heart girth)and Body weight of Arsi heifer(y)=-188.452+2.75(heart girth).Based on this,the measuring chart tape can be developed to estimate the body weight of Arsi cows and heifers at field condition where there is no access to weighing scales.

Cosmic-ray helium nuclei measurement with the calorimeter of the Alpha Magnetic Spectrometer

Purpose The distributions of energy responses of helium measured by the electromagnetic calorimeter(ECAL)of Alpha Magnetic Spectrometer(AMS-02)in Monte-Carlo do not agree with those in real data,thus using energy response matrix obtained from Monte-Carlo to measure the cosmic ray helium spectrum is not correct.Therefore,the spectrum measured with ECAL based on raw Monte-Carlo would be different from the real one.The purpose is to correct the response matrix to measure helium spectrum with ECAL.Methods The energy measured by ECAL of AMS-02 provides in-flight correction for the energy response matrix in Monte-Carlo.Results The spectrum based on corrected response matrix is consistent with the AMS collaboration published result.Conclusions It is feasible to measure helium spectrum with ECAL with corrected energy response matrix.