Ashraf F. Ashour

Influence of Type and Replacement Level of Recycled Aggregates on Concrete Properties

The study reports results of tests, using only natural aggregates, of a control concrete and nine recycled aggregate concretes. The recycled aggregates were classified according to measured specific gravity and water absorption into three different types, namely: RS II for recycled fine aggregate having 2.36 specific gravity and 5.4% water absorption; RG III for recycled coarse aggregate having a 2.4 specific gravity and 6.2% water absorption; and RG I for recycled coarse aggregate having a 2.53 specific gravity and 1.9% water absorption. Both recycled coarse and fine aggregate replacement levels in separate mixtures were 30, 50, and 100%. For fresh concrete, slump loss and bleeding amount with time were recorded. For hardened concrete, there was also measurement of unrestrained shrinkage strain, moduli of rupture and elasticity, and compressive and tensile strengths. Fresh and hardened concrete properties tested, together with a literature-reported comprehensive database, were evaluated with respect to relative aggregate water absorption combined with recycled aggregate quality and volume. Hardened concrete properties, in addition, with different recycled aggregate replacement levels and quality were compared with ACI 318-05 design equation and empirical equality for natural aggregate concrete proposed by Oluokun, whenever possible. That the properties of recycled aggregate-containing fresh and hardened concrete were dependent on the aggregate relative water absorption was clearly shown in test results. In addition, the moduli of recycled aggregate concrete rupture and elasticity was lower than ACI 318-05-specified design equation, when relative aggregate water absorption is respectively above 2.5% and 3.0%.

Empirical modelling of shear strength of RC deep beams by genetic programming

Abstract This paper investigates the feasibility of using genetic programming (GP) to create an empirical model for the complicated non-linear relationship between various input parameters associated with reinforced concrete (RC) deep beams and their ultimate shear strength. GP is a relatively new form of artificial intelligence, and is based on the ideas of Darwinian theory of evolution and genetics. The size and structural complexity of the empirical model are not specified in advance, but these characteristics evolve as part of the prediction. The engineering knowledge on RC deep beams is also included in the search process through the use of appropriate mathematical functions. The model produced by GP is constructed directly from a set of experimental results available in the literature. The validity of the obtained model is examined by comparing its response with the shear strength of the training and other additional datasets. The developed model is then used to study the relationships between the shear strength and different influencing parameters. The predictions obtained from GP agree well with experimental observations.

Sagging and Hogging Strengthening of Continuous Reinforced Concrete Beams Using CarbonFiber-Reinforced Polymer Sheets

The testing of 11 reinforced concrete 2-span beams strengthened in flexure with externally bonded carbon fiber-reinforced polymer (CFRP) sheets is reported. The beams were classified into 2 groups according to the arrangement of the internal steel reinforcement. Each group included 1 unstrengthened control beam. The main parameters studied were the position, length, and number of CFRP layers. External strengthening using CFRP sheets was found to increase the beam load capacity. All strengthened beams exhibited less ductility compared with the unstrengthened control beams, however, and showed undesirable sudden failure modes. There was an optimum number of CFRP layers beyond which there was no further enhancement in the beam capacity. Extending the CFRP sheet length to cover the entire hogging/sagging zones did not prevent peeling failure of the CFRP sheets, which was the dominant factor mode of beams tested.

TESTS OF REINFORCED CONCRETE CONTINUOUS DEEP BEAMS WITH WEB OPENINGS

This paper presents test results of 16 reinforced concrete two-span continuous deep beams with web openings. All test specimens had the same geometry and main longitudinal top and bottom reinforcement. The main parameters considered were the size and position of the web openings and web reinforcement arrangement. Two modes of failure were observed, depending on the position of the web openings. For beams having web openings within interior shear spans, the failure is developed by diagonal cracks between the web opening corners and the edges of the load and central support plates. For beams having web openings within exterior shear spans, the mode of failure is characterized by major diagonal cracks within interior and exterior shear spans. The diagonal cracks that occurred in the interior shear span extended to join the edges of the load and central support plates, and at the same time, the diagonal cracks that formed at the web opening corners propagated both ways toward the edges of the load and end support plates. Web openings within interior shear spans caused more reduction on the beam capacity than those within exterior shear spans. The vertical web reinforcement had more influence on the beam capacity than the horizontal web reinforcement. An upper-bound analysis of the two failure mechanisms that occurred in the experiments is introduced, and design equations are developed.

Concrete breakout strength of single anchors in tension using neural networks

A feed forward neural network model for evaluating the concrete breakout strength of single cast-in and post-installed mechanical anchors in tension is presented. The nodes of the neural network input layer represent the embedment depth, anchor head diameter, concrete strength and anchor installation system, and the neural network output is the tensile capacity of anchors as governed by the concrete breakout. Three different techniques have been adopted to represent the anchor installation system in the neural network input layer. The training, validation and testing of the developed networks were based on a database of 451 experimental tests obtained from previous laboratory anchor tests. Testing of the trained neural network indicates good predictions of the concrete breakout strength of cast-in and post-installed mechanical anchors in tension.The relationships between the concrete breakout strength of anchors and different influencing parameters obtained from the trained neural networks were in general agreement with those of the ACI 318-02 for cast-in and post-installed mechanical anchors. It has been shown that the concrete breakout strength of anchors in tension is approximately proportional to the embedment depth of 1.5 power and marginally affected by changing the anchor head diameter.

Tests of Reinforced Concrete Continuous Deep Beams

Test results of eight reinforced concrete continuous deep beams are reported. The main parameters considered were shear span-to-depth ration, amount and type of web reinforcement, and amount of main longitudinal reinforcement. Vertical web reinforcement had more influence on shear capacity than horizontal web reinforcement. Failure is initiated by a major diagonal crack in the intermediate shear span between the edges of the load and intermediate support plates. Comparisons between test results and current codes of practice, namely the ACI Building Code (318-89) and CIRIA Guide 2, show little agreement.

Flow and Compressive Strength of Alkali-Activated Mortars

The authors present results of testing performed on 18 fly ash (FA)-based mortars and 36 slag-based mortars activated by sodium hydroxide and/or sodium silicate powders. Activator sodium oxide mixing ratio to source materials, fine aggregate-binder ratio (s/b), and water-binder ratio (w/b) are the main variables investigated. That much higher compressive strength and slightly less flow were exhibited by slag-based alkali-activated (AA) mortars when compared to FA-based AA mortars for the same mixing condition was shown by test results. For AA mortar initial flow and 28-day compressive strength evaluation, nonlinear multiple regression analysis developed feed-forward neural networks and simplified equations were developed. A comprehensive database of the results of 82 tests of sodium silicate activated mortars was used for neural network training and testing and simplified equation calibration. Estimation of slag-based AA mortar compressive strength development was also done using the ACI 209R specified formula calibrated against the collected database. Test results were in good agreement with predictions obtained from developed simplified equations and trained neural network, although there was slight overestimation of slag-based AA mortar early strength by the proposed simplified equations.

Shear Capacity of Reinforced Concrete Beams Using Neural Network

Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed.

Influence of Shear Reinforcement on Reinforced Concrete Continuous Deep Beams

This paper presents results from tests of 24 two-span reinforced concrete deep beams that were performed to study the influence of shear reinforcement on structural behavior. The main variables studied were concrete strength, shear span-to-overall depth ratio (a/h) and the amount and configuration of shear reinforcement. The results show that the load transfer capacity of shear reinforcement was much more prominent in continuous deep beams than in simply supported deep beams. The ratio of the load capacity measured and that predicted by the strut-and-tie model recommended by American Concrete Institute 318-05 dropped against the increase of a/h. This decrease rate was more remarkable in continuous deep beams than that in simple deep beams. The strut-and-tie model recommended by ACI 318-05 overestimated the strength of continuous deep beams having an a/h of more than 1.0. Horizontal shear reinforcement was always more effective than vertical shear reinforcement for beams having an a/h of 0.5. However, vertical shear reinforcement was more effective for an a/h higher than 1.0.

Modified ACI Drop-Weight Impact Test for Concrete

This article reports on a study of a modified ACI drop-weight impact test for concrete. The industry standard, the ACI Committee 544’s repeated drop-weight impact test, is often criticized for large variations within the results. The authors of this paper identifed the sources of these large variations and developed modifications to the ACI test. The proposed modifications were evaluated by conducting impact resistance tests on 40 specimens from two batches of polypropylene fiber-reinforced concrete (PPFRC). The impact resistance of PPFRC specimens tested with the current ACI test exhibited large coefficients of variation (COV) of 58.6% and 50.2% for the first-crack and the ultimate impact resistance, respectively. The corresponding COV for PPFRC specimens tested according to the modified technique were 39.4% and 35.2%, indicating that the reliability of the results was significantly improved. Using the current ACI test, the minimum number of replications needed per each concrete mixture to obtain an error below 10% was 41, compared to 20 specimens for the modified test. The authors conclude that although such a large number of specimens is still not good enough for practical and economical reasons, the reduction presents a good step forward on the development of a standard impact test.