Elasto-plastic Cracking Analysis of Reinforced Concrete

A new elasto-plastic cracking constitutive model for reinforced concrete is presented. The nonlinear effects considered cover almost all the nonlinearities exhibited by reinforced concrete under short term monotonic loading. They include concrete cracking in tension, plasticity in compression, aggregate interlock, tension softening, elasto-plastic behavior of steel, bond-slip between concrete, and steel reinforcement and tension stiffening. A new procedure for incorporating bondslip in smeared steel elements is described. A modified Huber-Hencky-Mises failure criterion for plastic deformation of concrete, which fits the experimental results under biaxial stresses better, is proposed. Multiple cracking at Gauss points and their opening and closing are considered. Matrix expressions are developed and are incorporated in a nonlinear finite element program. After the objectivity of the model is demonstrated, the model is used to analyze two different types of problems: one, a set of four shear panels, and the other, a reinforced concrete beam without shear reinforcement. The results of the analysis agree favorably with the experimental results.

Inelastic dynamic response of R.C. beam column joints by using B.E.M.

A BEM formulation to obtain the inelastic response of R.C. Beam-Column joints subjected to sinusoidal loading along the boundary is presented. The equations of motion are written along with kinematical and constitutive equations. The dynamic reciprocal theorem is presented and the temporal dependence is removed by assuming steady state response.

Graphical method to determine the parameters of the two-parameter fracture model for concrete

To evaluate the parameters in the two-parameter fracture model, i.e. the critical stress intensity factor and critical crack tip opening displacement for the fracture of plain concrete in Mode 1 for the given test configuration and geometry, considerable computational effort is necessary. A simple graphical method has been proposed using normalized fracture parameters for the three-point bend (3PB) notched specimen and the double-edged notched (DEN) specimen. A similar graphical method is proposed to compute the maximum load carrying capacity of a specimen, using the critical fracture parameters both for 3PB and DEN configurations.

Time dependent deformations in concrete: A multi-scale approach

Estimation of creep and shrinkage are critical in order to compute loss of prestress with time in order to compute leak tightness and assess safety margins available in containment structures of nuclear power plants. Short-term creep and shrinkage experiments have been conducted using in-house test facilities developed specifically for the present research program on 35 and 45 MPa normal concrete and 25 MPa heavy density concrete. The extensive experimental program for creep, has cylinders subject to sustained levels of load typically for several days duration (till negligible strain increase with time is observed in the creep specimen), to provide the total creep strain versus time curves for the two normal density concrete grades and one heavy density concrete grade at different load levels, different ages at loading, and at different relative humidity’s. Shrinkage studies on prism specimen for concrete of the same mix grades are also being studied. In the first instance, creep and shrinkage prediction models reported in the literature has been used to predict the creep and shrinkage levels in subsequent experimental data with acceptable accuracy. While macro-scale short experiments and analytical model development to estimate time dependent deformation under sustained loads over long term, accounting for the composite rheology through the influence of parameters such as the characteristic strength, age of concrete at loading, relative humidity, temperature, mix proportion (cement: fine aggregate: coarse aggregate: water) and volume to surface ratio and the associated uncertainties in these variables form one part of the study, it is widely believed that strength, early age rheology, creep and shrinkage are affected by the material properties at the nano-scale that are not well established. In order to understand and improve cement and concrete properties, investigation of the nanostructure of the composite and how it relates to the local mechanical properties is being undertaken. While results of creep and shrinkage obtained at macro-scale and their predictions through rheological modeling are satisfactory, the nano and micro indenting experimental and analytical studies are presently underway. Computational mechanics based models for creep and shrinkage in concrete must necessarily account for numerous parameters that impact their short and long term response. A Kelvin type model with several elements representing the influence of various factors that impact the behaviour is under development. The immediate short term deformation (elastic response), effects of relative humidity and temperature, volume to surface ratio, water cement ratio and aggregate cement ratio, load levels and age of concrete at loading are parameters accounted for in this model. Inputs to this model, such as the pore structure and mechanical properties at micro/nano scale have been taken from scanning electron microscopy and micro/nano-indenting of the sample specimen.

Short- and long-term temporal variations in salinity and the oxygen, carbon and hydrogen isotopic compositions of the Hooghly Estuary water, India

The Hooghly River estuary provides a unique experimental site to understand the effect of monsoonal river discharge on freshwater and seawater mixing. Water samples collected bi-weekly for a duration of 17 months were analyzed for salinity, delta O-18,delta C-13(DIC), as well as delta D to investigate the differential mixing of freshwater and seawater. The differences in salinity and delta O-18 of samples collected during low and high tides on the same day are strongly correlated suggesting a well mixed water column at our sampling site. Low salinity and depleted delta O-18 during monsoon is consistent with increased river discharge as well as high rainfall. We identified different slopes in a delta O-18 versus salinity plot for the estuary water samples collected during monsoon and non-monsoon seasons. This is driven by composition of the freshwater source which is dominated by rainwater during monsoon and rivers during non-monsoon months. Selected delta D analyses of samples indicate that groundwater contributes significantly to the Hooghly Estuary during low rainfall times of the year. delta C-13(DIC) measured in the water recorded low values towards the end of monsoon indicating low productivity (i.e. increased organic respiration) while progressively increasing delta C-13(DIC) values from October till January as well as during some of the pre-monsoon months can be explained by increasing productivity. Very low delta C-13(DIC) (similar to-20%0) suggests involvement of carbon derived from anaerobic oxidation of organics and/or methane with potential contribution from increased anthropogenic water supply. An estimate of seawater incursion into the Hooghly Estuary at different times of the year is obtained by using salinity data in a two-component mixing model. Presence of seawater was found maximum (31-37%) during February till July and lowest (less than or equal to 6%) from September till November. We notice a temporal offset between Ganges River discharge farther upstream at Farakka and salinity variation at the Hooghly Estuary. We believe that this time lag is a result of the physical distance between Farakka and Kakdweep (our sampling location) and put constraints on the travel time of river water during early monsoon. (c) 2012 Published by Elsevier B.V.

Comparative study of three short-chain neurotoxins from the venom of Naja kaouthia (Yunnan, China)

Three short-chain neurotoxins named NT-I, NT-II, and NT-III were purified from the venom of Naja kaouthia, a snake distributed throughout the south of Yunnan province, China, by a series of chromatographic steps, including an FPLC Resource S column. Their molecular weights, determined by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MS, were 6952.19 Da, 6854.92 Da, and 6828.80 Da, respectively. NT-I consisted of 62 amino acid residues, and the other two consisted of 61 amino acid residues, including 8 cysteines. After hydrolysis by endoproteinase Glu-C, their primary sequences were determined. A test of their activities demonstrated that they effectively inhibited muscle contractions induced by electric stimulation. Furthermore, the extent of inhibition caused by NT-II and NT-III was less than that of NT-I. The IC(50)s were 0.04 mug/ml, 0.20 mug/ml, and 0.23 mug/ml for NT-I, NT-II, and NT-III, respectively. Compared with NT-II and NT-III, the higher activity of NT-I may be a result of the amino acid residue substitution Ile36 to Arg36.

Detection of large-scale concrete columns for automated bridge inspection

There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame. This is because manually inspecting bridges is a time-consuming and costly task, and some state Departments of Transportation (DOT) cannot afford the essential costs and manpower. In this paper, a novel method that can detect large-scale bridge concrete columns is proposed for the purpose of eventually creating an automated bridge condition assessment system. The method employs image stitching techniques (feature detection and matching, image affine transformation and blending) to combine images containing different segments of one column into a single image. Following that, bridge columns are detected by locating their boundaries and classifying the material within each boundary in the stitched image. Preliminary test results of 114 concrete bridge columns stitched from 373 close-up, partial images of the columns indicate that the method can correctly detect 89.7% of these elements, and thus, the viability of the application of this research.

Real Time Concrete Damage Visual Assessment for First Responders

First responders are in danger when they perform tasks in damaged buildings after earthquakes. Structural collapse due to the failure of critical load bearing structural members (e.g. columns) during a post-earthquake event such as an aftershock can make first responders victims, considering they are unable to assess the impact of the damage inflicted in load bearing members. The writers here propose a method that can provide first responders with a crude but quick estimate of the damage inflicted in load bearing members. Under the proposed method, critical structural members (reinforced concrete columns in this study) are identified from digital visual data and the damage superimposed on these structural members is detected with the help of Visual Pattern Recognition techniques. The correlation of the two (e.g. the position, orientation and size of a crack on the surface of a column) is used to query a case-based reasoning knowledge base, which contains apriori classified states of columns according to the damage inflicted on them. When query results indicate the column's damage state is severe, the method assumes that a structural collapse is likely and first responders are warned to evacuate.

Automated Detection of Concrete Columns from Visual Data

After earthquakes, licensed inspectors use the established codes to assess the impact of damage on structural elements. It always takes them days to weeks. However, emergency responders (e.g. firefighters) must act within hours of a disaster event to enter damaged structures to save lives, and therefore cannot wait till an official assessment completes. This is a risk that firefighters have to take. Although Search and Rescue Organizations offer training seminars to familiarize firefighters with structural damage assessment, its effectiveness is hard to guarantee when firefighters perform life rescue and damage assessment operations together. Also, the training is not available to every firefighter. The authors therefore proposed a novel framework that can provide firefighters with a quick but crude assessment of damaged buildings through evaluating the visible damage on their critical structural elements (i.e. concrete columns in the study). This paper presents the first step of the framework. It aims to automate the detection of concrete columns from visual data. To achieve this, the typical shape of columns (long vertical lines) is recognized using edge detection and the Hough transform. The bounding rectangle for each pair of long vertical lines is then formed. When the resulting rectangle resembles a column and the material contained in the region of two long vertical lines is recognized as concrete, the region is marked as a concrete column surface. Real video/image data are used to test the method. The preliminary results indicate that concrete columns can be detected when they are not distant and have at least one surface visible.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.

Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments

The current procedures in post-earthquake safety and structural assessment are performed manually by a skilled triage team of structural engineers/certified inspectors. These procedures, and particularly the physical measurement of the damage properties, are time-consuming and qualitative in nature. This paper proposes a novel method that automatically detects spalled regions on the surface of reinforced concrete columns and measures their properties in image data. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the exposure of longitudinal reinforcement (depth of spalling into the column) and length of spalling along the column are measured using a novel global adaptive thresholding algorithm in conjunction with image processing methods in template matching and morphological operations. The method was tested on a database of damaged RC column images collected after the 2010 Haiti earthquake, and comparison of the results with manual measurements indicate the validity of the method.