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CONTENTS
Volume 20, Number 1, July 2017
 

Abstract
This paper focuses on the mesh-size dependency in numerical simulations of reinforced concrete (RC) structures subjected to blast loading. A tensile failure criterion that can minimize the mesh-dependency of simulation results is introduced based on the fracture energy theory. In addition, conventional plasticity based damage models for concrete such as the CSC model and the HJC model, which are widely used for blast analyses of concrete structures, are compared with the orthotropic model that adopts the introduced tensile failure criterion in blast tests to verify the proposed criterion. The numerical predictions of the time-displacement relations at the mid-span of RC beams subjected to blast loading are compared with experimental results. The analytical results show that the numerical error according to the change in the finite element mesh size is substantially reduced and the accuracy of the numerical results is improved by applying a unique failure strain value determined by the proposed criterion.

Key Words
tensile criterion; high strain rate concrete; blast simulation; fracture energy; mesh-dependency

Address
HanGul Gang and Hyo-Gyoung Kwak: Department of Civil Engineering, Korean Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

Abstract
There are two methods to model the plastification of members comprising lumped and distributed plasticity. When a reinforced concrete member experiences inelastic deformations, cracks tend to spread from the joint interface resulting in a curvature distribution; therefore, the lumped plasticity methods assuming plasticity is concentrated at a zero-length plastic hinge section at the ends of the elements, cannot model the actual behavior of reinforced concrete members. Some spread plasticity models including uniform, linear and recently power have been developed to take extended inelastic zone into account. In the aforementioned models, the extended inelastic zones in proximity of critical sections assumed close to connections are considered. Although the mentioned assumption is proper for the buildings simply imposed lateral loads, it is not appropriate for the gravity load effects. The gravity load effects can influence the inelastic zones in structural elements; therefore, the plasticity models presenting the flexibility distribution along the member merely based on lateral loads apart from the gravity load effects can bring about incorrect stiffness matrix for structure. In this study, the linear flexibility distribution model is improved to account for the distributed plasticity of members subjected to both gravity and lateral load effects. To do so, a new model in which, each member is taken as one structural element into account is proposed. Some numerical examples from previous studies are assessed and outcomes confirm the accuracy of proposed model. Also comparing the results of the proposed model with other spread plasticity models illustrates glaring error produced due to neglecting the gravity load effects.

Key Words
distributed plasticity; reinforced concrete; inelastic deformations; linear flexibility distribution; gravity load

Address
AliReza Habibi: Department of Civil Engineering, Shahed University, Tehran, Iran
Mehdi Izadpanah: Department of Civil Engineering, University of Kurdistan, Sanandaj, Iran

Abstract
In this study engineered steel fibres used as reinforcement for concrete were characterized by number of key mechanical and spatial parameters, which are easy to measure and quantify. Such commonly used parameters as length, diameter, fibre intrinsic efficiency ratio (FIER), hook geometry, tensile strength and ductility were considered. Effective classification of various fibres was demonstrated using simple multivariate computations involving principal component analysis (PCA). Contrary to univariate data mining approach, the proposed analysis can be efficiently adapted for fast, robust and direct classification of engineered steel fibres. The results have revealed that in case of particular spatial/geometrical conditions of steel fibres investigated the FIER parameter can be efficiently replaced by a simple aspect ratio. There is also a need of finding new parameters describing properties of steel fibre more precisely.

Key Words
steel fibres; concrete; reinforcement; univariate measurements; multivariate classification; principal component analysis

Address
Pawel K. Zarzycki: Department of Environmental Technologies and Bioanalytics, Faculty of Civil Engineering, Environmental and Geodetic Sciences,
Koszalin University of Technology, Śniadeckich 2, 75-453 Koszalin, Poland
Jacek Katzer: Department of Construction and Building Materials, Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology, Śniadeckich 2, 75-453 Koszalin, Poland
Jacek Domski: Department of Concrete Structures and Technology of Concrete, Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology, Śniadeckich 2, 75-453 Koszalin, Poland

Abstract
In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN\'s are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.

Key Words
self-compacting concrete; compressive strength; artificial neural network; multivariable regression analysis; mean absolute error

Address
Prasenjit Saha and Prasad M.L.V.: Department of Civil Engineering, NIT, Silchar-788010, Cachar District, Assam, India
P. Rathish Kumar: Department of Civil Engineering, National Institute of Technology, Warangal, 506004, India

Abstract
In this paper, mode I fracture toughness of rock was determined by direct and indirect methods using Particle Flow Code simulation. Direct methods are compaction tension (CT) test and hollow centre cracked quadratic sample (HCCQS). Indirect methods are notched Brazilian disk (NBD) specimen, the semi-circular bend (SCB) specimen, hollow centre cracked disc (HCCD), the single edge-notched round bar in bending (SENRBB) specimen and edge notched disk (END). It was determined that which one of indirect fracture toughness values is close to direct one. For this purpose, initially calibration of PFC was undertaken with respect to data obtained from Brazilian laboratory tests to ensure the conformity of the simulated numerical models response. Furthermore, the simulated models in five introduced indirect tests were cross checked with the results from direct tests. By using numerical testing, the failure process was visually observed. Discrete element simulations demonstrated that the macro fractures in models are caused by microscopic tensile breakages on large numbers of bonded discs. Mode I fracture toughness of rock in direct test was less than other tests results. Fracture toughness resulted from semi-circular bend specimen test was close to direct test results. Therefore semi-circular bend specimen can be a proper test for determination of Mode I fracture toughness of rock in absence of direct test.

Key Words
tensile strength; direct test; flexural test; double punch tensile test and ring test

Address
Vahab Sarfarazi: Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
Hadi Haeri: Department of Civil Engineering, Aria University of Sciences and Sustainability, Tehran, Iran
Alireza Bagher Shemirani:
1) Department of Civil Engineering, SADRA Institute of Higher Education, Tehran, Iran
2) Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

Abstract
A clear correlation exists between the compressive strength and elastic modulus of concrete. Unfortunately, determining the static elastic modulus requires destructive methods and determining the dynamic elastic modulus is greatly complicated by the shape and size of the specimens. This paper reports on a novel approach to the prediction of compressive strength in concrete cylinders using numerical calculations in conjunction with the impact-echo method. This non-destructive technique involves obtaining the speeds of P-waves and S-waves using correction factors through numerical calculation based on frequencies measured using the impact-echo method. This approach makes it possible to calculate the dynamic elastic modulus with relative ease, thereby enabling the prediction of compressive strength. Experiment results demonstrate the speed, convenience, and efficacy of the proposed method.

Key Words
compressive strength prediction; dynamic elastic modulus; P-wave speed; S-wave speed

Address
Chi-Che Hung and Kuang-Chih Pai: Institute of Nuclear Energy Research, Atomic Energy Council, 1000 Wenhua Rd. Jiaan Village, Longtan District, Taoyuan City 32546, Taiwan
Wei-Ting Lin and An Cheng: Department of Civil Engineering, National ILan University, No.1, Sec. 1, Shennong Rd., Yilan City, Yilan County 26047, Taiwan

Abstract
A coupled experimental-numerical study on shear fracture in concrete specimens with different geometries is carried out. The crack initiation, propagation and final breakage of concrete specimens are experimentally studied under compression loading. The load-strain and the strength of the specimens are experimentally measured, indicating decreasing effects of the shear behavior on the failure load of the specimen. The effects of specimen geometries on the shear fracturing path in the concrete specimens are also investigate. Numerical models using an indirect boundary element method are made to evaluate the crack propagation paths of concrete specimens. These numerical results are compared with the performed experiments and are validated experimentally.

Key Words
shear behavior; concrete specimens; crack propagation; load-strain analysis

Address
Hadi Haeri: Young Researchers and Elite Club, Bafgh Branch, Islamic Azad University, Bafgh, Iran
Vahab Sarfarazi: Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
Alireza Bagher Shemirani:
1) Department of Civil Engineering, SADRA Institute of Higher Education, Tehran, Iran
2) Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

Abstract
In this study, a novel application of symbolic regression (SR) is employed for the prediction of ultimate shear strength of steel fiber reinforced (SFRC) and glass fiber reinforced (GFRC) corbels without stirrups, for the first time in the literature. A database is created using the test results (42 tests) conducted by the authors of current paper as well as the previous studies available in the literature. A symbolic regression based empirical formulation is proposed using this database. The formulation is unique in a way that it has the capability to predict the shear strength of both SFRC and GFRC corbels. The performance of proposed model is tested against randomly selected testing set. Additionally, a parametric study with a wide range of variables is carried out to test the effect of each parameter on the shear strength. The results confirm the high prediction capacity of proposed model.

Key Words
symbolic regression; reinforced concrete corbel; fiber reinforced concrete; shear strength; glass fiber; steel fiber

Address
Ahmet E Kurtoglu: Department of Civil Engineering, Istanbul Gedik University, 34876, Kartal, Istanbul, Turkey
Mehmet E Gulsan, Hussein A Abdi, Mohammed A Kamil and Abdulkadir Cevik: Department of Civil Engineering, University of Gaziantep, 27310, Gaziantep, Turkey

Abstract
Shirasu, a pyroclastic flow deposit, showed considerable performance as aluminosilicate source in geopolymer, based on past research. However, the polymerization reactivity was somewhat lower compared to the traditional fly ash based geopolymer even though the long-term strength was fairly good. The present study concentrates on the development of higher initial strength performance of Shirasu based geopolymer by utilizing ground granulated blast furnace slag as an admixture. Mortars with various mix proportions were adopted to study the effect of parametric changes on strength development along with the addition of slag in different percentages. A combination of sodium hydroxide and sodium silicate was used as alkaline activators considering parameters like molar ratios of alkali to geopolymer water and silica to alkali molar ratio. The mortars were cured at elevated temperatures under different curing conditions to analyze the effect on strength development. Compressive strength test, mercury intrusion porosimetry and X-ray powder diffraction were carried out to assess the strength performance and microstructure of slag-Shirasu based geopolymer. Based on the experimental study, it was observed that the initial and long-term strength development of Slag-Shirasu geopolymer were improved by the addition of slag.

Key Words
Shirasu; pyroclastic flow; geopolymer; alkali activation; polymerization; ground granulated blast furnace slag

Address
Dhruva Narayana Katpady, Koji Takewaka and Toshinobu Yamaguchi: Department of Ocean Civil Engineering, Kagoshima University, 1-21-40, 8900065, Korimoto, Kagoshima, Japan

Abstract
With a special attention to the different stages of a typical loading path travelled in a fluid confined concrete test, this paper introduces a physically consistent model for the stress-strain curve of actively confined normal-strength concrete in the axial direction. The model comprises of the five elements of: (1) a criterion for the peak or failure strength, (2) an equation for the peak strain, (3) a backbone hydrostatic curve, (4) a transient hardening curve linking the point of departure from the hydrostatic curve to the failure point, and finally (5) a set of formulas for the post-peak region. Alongside, relevant details and shortcomings of existing models will be discussed in each part. Finally, the accuracy and efficiency of the proposed model have been verified in a set of simulations which compare well with the experimental results from the literature.

Key Words
active confinement; concrete; failure strength; stress-strain model; hydrostatic response; numerical modeling

Address
Sharif Shahbeyk, Mahshid Z. Moghaddam and Mohammad Safarnejad: Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box 14115-143, Tehran, Iran

Abstract
Service life assessments which do not include the synergy between mechanical and environmental loading are neglecting a factor that can have a significant impact on structural safety and durability assessment. The degradation of concrete structure is a result of the combined effect of environmental and mechanical factors. In order to make service life design realistic it is necessary to consider both of these factors acting simultaneously. This paper deals with the advanced modelling of concrete carbonation and chloride ingress into concrete using stochastic 1D and 2D models. Widely accepted models incorporated into the new fib Model Code 2010 are extended to include factors that reflect the coupled effects of mechanical and environmental loads on the durability and reliability of reinforced concrete structures. An example of cooling tower degradation by carbonation and an example of a bended reinforced concrete beam kept for several years in salt fog are numerically studied to show the capability of the stochastic approach. The modelled degradation measures are compared with experimental results, leading to good agreement.

Key Words
concrete structures; synergy; mechanical loading; environmental loading; modelling; service life; reliability

Address
Dita Vořechovská, Martina Šomodíková, Jan Podroužek and David Lehký: Institute of Structural Mechanics, Faculty of Civil Engineering, Brno University of Technology, Veveří 331/95, 602 00 Brno, Czech Republic
Břetislav Teplý: Institute of Chemistry, Faculty of Civil Engineering, Brno University of Technology, Veveří 331/95, 602 00 Brno, Czech Republic

Abstract
The present study established prediction models based on multiple nonlinear regressions (MNLRs) and back-propagation neural networks (BPNs) for the expansion of cement mortar caused by oxidization slag that was used as a replacement of the aggregate. The data used for the models were obtained from actual laboratory tests on specimens that were produced with water/cement ratios of 0.485 or 1.5, within which 0%, 10%, 20%, 30%, 40%, or 50% of the cement had been replaced by oxidization slag from electric-arc furnaces; the samples underwent high-temperature curing at either 80oC or 100oC for 1-4 days. The varied mixing ratios, curing conditions, and water/cement ratios were all used as input parameters for the expansion prediction models, which were subsequently evaluated based on their performance levels. Models of both the MNLR and BPN groups exhibited R2 values greater than 0.8, indicating the effectiveness of both models. However, the BPN models were found to be the most accurate models.

Key Words
electric arc furnace oxidizing slag (EOS); back-propagation neural network (BPN); multiple linear regression (MLR)

Address
Wen-Ten Kuo and Chuen-Ul Juang: Department of Civil Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien-Kung Rd., Sanmin District, Kaohsiung 80778, Taiwan, R.O.C.



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