Abrasion resistance of sustainable green concrete containing waste tire rubber particles

The development of new environmental friendly concretes such as rubberized concrete is being promoted due to the environmental problems created by the waste tire rubber. Every year millions of tires are discarded, thrown away or buried all over the world, representing a very serious threat to the ecology. In this study, we analyse the potential of waste tire rubber particles as a partial substitute for fine aggregates in normal strength and high strength cement concrete and the resistance to abrasion has been measured. Statistical Analysis was carried out to strengthen the results obtained from experiments. The results show that the use of tire rubber particles can improve the abrasion resistance of concrete, and this can ensure its applications in pavements, floors and concrete highways, or in places where there are abrasive forces between surfaces and moving objects. (C) 2016 Elsevier Ltd. All rights reserved

Effect of wet curing duration on long-term performance of concrete in tidal zone of marine environment

A proper initial curing is a very simple and inexpensive alternative to improve concrete cover quality and accordingly extend the service life of reinforced concrete structures exposed to aggressive species. A current study investigates the effect of wet curing duration on chloride penetration in plain and blended cement concretes which subjected to tidal exposure condition in south of Iran for 5 years. The results show that wet curing extension preserves concrete against high rate of chloride penetration at early ages and decreases the difference between initial and long-term diffusion coefficients due to improvement of concrete cover quality. But, as the length of exposure period to marine environment increased the effects of initial wet curing became less pronounced. Furthermore, a relationship is developed between wet curing time and diffusion coefficient at early ages and the effect of curing length on time-to-corrosion initiation of concrete is addressed.Peer reviewedCivil and Environmental Engineerin

Numerical modeling of time to corrosion induced cover cracking in reinforced concrete using soft-computing based methods

Reinforced concrete (RC) is one of the most commonly used composite materials in construction industry. Corrosion of reinforcing steel embedded in concrete is a crucial issue leading deterioration of RC structure. In this study, mathematical formulations that predict the time from corrosion initiation to corrosion cracking in RC elements subjected to accelerated corrosion test are presented. For this, the time to corrosion cracking t(cr) in RC elements is evaluated and developed using soft-computing techniques, namely, genetic algorithms and artificial neural networks. In the models, nine critical estimation parameters were considered. The experimental inputs are mix design properties of the concretes, curing conditions, testing age, mechanical properties of concrete, and cover thickness of RC elements. The dataset was formed by collecting 126 experimental data samples reported in the technical literature. The dataset was randomly separated into three parts for training, testing, and validating the models. Through the numerical study, the influences of various experimental factors on the duration and extent of RC corrosion-induced cracking were shown. It was found that the soft-computing based models gave reasonable predictions of t(cr) in RC elements. However, the neural network model performed better and the highest correlation coefficient (R) between predicted and experimental t(cr) values was computed as 0.998

Modeling and analysis of the shear capacity of adhesive anchors post-installed into uncracked concrete

This paper reports the results of an analytical study to predict the edge breakout shear capacity of single adhesive anchors post-installed into uncracked hardened concrete. For this purpose, an experimental database for the adhesive anchors compiled by the ACI Committee 355 was obtained and utilized to construct training and test sets so as to derive the closed-form solution by means of gene expression programming (GEP). The independent variables used for development of the prediction model were anchor diameter, type of anchor, edge distance, embedment depth, clear clearance of the anchor, type of chemical adhesive, method of injection of the chemical, and compressive strength of the concrete. The generated prediction model yielded correlation coefficients of 0.98 and 0.92 for training and testing data sets, respectively. Moreover, the performance of the proposed model was compared with the existing models proposed by American Concrete Institute (ACI) and Prestressed/Precast Concrete Institute (PCI). The analyses showed that the proposed GEP model provided much more accurate estimation of the observed values as compared to the other models. (C) 2014 Elsevier Ltd. All rights reserved

Evaluation and modeling of ultimate bond strength of corroded reinforcement in reinforced concrete elements

Bond deterioration between the reinforcement and surrounding concrete is one of the crucial reasons for the structural degradation in the steel-concrete composite structures. The assessment of bond deterioration due to corrosion is of prime importance on this issue. This study aims to present the derivation of analytical formulation of ultimate bond strength su at the corroded reinforcement-concrete interface for the reinforced concrete (RC) elements subjected to various levels of corrosion. The modeling technique dealt in this work is gene-expression programming and artificial neural network. The data used for the development of the models are thoroughly selected from the available experimental studies reported in the technical literature. A total of 218 experimental data samples were arranged to obtain training and testing data sets. The critical predictive factors were compressive strength of concrete, concrete cover, steel type, diameter of the steel bar, bond length, and corrosion level. The performances of the proposed empirical models were also evaluated statistically. The results indicated that the soft-computing based models had a satisfactory performance to predict the ultimate bond strength of corroded steel bars in RC elements

Predictive models of the flexural overstrength factor for steel thin-walled circular hollow section beams

Circular hollow section (CHS) steel beams are widely used in both mechanical and civil applications. CHS members are mainly subjected to bending. The flexural overstrength factor (namely the ratio between the ultimate bending strength over the plastic bending moment) characterizes the flexural behaviour of steel CHS beams. This paper describes an analytical study aiming to develop a new explicit formulation for predicting the flexural overstrength factor of steel CHS beams. The proposed models were derived from soft-computing techniques based on both neural networks (NNs) and gene expression programming (GEP), respectively. To this aim, experimental data available from scientific literature were analysed and collected to form a comprehensive dataset for developing the prediction models. A total number of 128 samples was considered in order to cover different geometric and mechanical properties. The input variables accounted for the modelling were the external diameter (D), wall thickness (t), shear length (L-v), and steel yield strength (f(y)). The database was arbitrarily divided into two subsets to obtain both training and testing databases for the generation of the models. The prediction capability of the proposed formulations was assessed with respect to the experimental data and the levels of accuracy and performance were also compared with an existing analytical formulation available previously developed for cold-formed sections. The results showed that the novel proposed models derived from NN and GEP methods provide better prediction performances than those obtained by the existing analytical model. (C) 2015 Elsevier Ltd. All rights reserved