The present research is conducted to investigate long term (more than two years) free and confined drying shrinkage magnitude and behaviour of self-compacting concrete (SCC) and compare with normal concrete (NC). For all SCCs mixes, Portland cement was replaced with 0-60% of fly ash (FA), fine and coarse aggregates were kept constant at 890 kg/m3 and 780 kg/m3, respectively. Two different water binder ratios of 0.44 and 0.33 were examined for both SCCs and NCs. Fresh properties of SCCs such as filling ability, passing ability, viscosity and resistance to segregation and hardened properties such as compressive and flexural strengths, water absorption and density of SCCs and NCs were also determined. Experimental results of free drying shrinkage obtained from this study together with collected comprehensive database from different sources available in the literature were compared to five existing models, namely the ACI 209R-92 model, BSEN-92 model, ACI 209R-92 (Huo) model, B3 model, and GL2000 model. To assess the quality of predictive models, the influence of various parameters (compressive strength, cement content, water content and relative humidity) on the drying shrinkage strain are studied. An artificial neural network models (ANNM) for prediction of drying shrinkage strains of SCC was developed using the same data used in the existing models. Two ANNM sets namely ANNM1 and ANNM2 with different numbers of hidden layer neurones were constructed. Comparison between the results given by the ANNM1 model and the results obtained by the five existing predicted models were presented. The results showed that, using up to 60% of FA as cement replacement can produce SCC with a compressive strength as high as 30 MPa and low drying shrinkage strain. SCCs long-term drying shrinkage from 356 to 1000 days was higher than NCs. Concrete filled elliptical tubes (CFET) with self-compacting concrete containing FA up to 60% are recommended for use in construction in order to prevent confined drying strain. ACI 209R-92 model provided a better prediction of drying shrinkage compared with the other four models. However, a very high predictability with high accuracy was achieved with the ANNM1 model with a mean of 1.004. Moreover, by using ANNM models, it is easy to insert any of factors effecting drying shrinkage to the input parameters to predict drying shrinkage strain of SCC. / Ministry of Higher Education, Libya

yes / Test results of thirty six ground granulated blast-furnace slag (GGBS)-based mortars and eighteen fly ash (FA)-based mortars activated by sodium silicate and/or sodium hydroxide powders are presented. The main variables investigated were the mixing ratio of sodium oxide (Na2O) of the activators to source materials, water-to-binder ratio, and fine aggregate-to-binder ratio. Test results showed that GGBS based alkali-activated (AA) mortars exhibited much higher compressive strength but slightly less flow than FA based AA mortars for the same mixing condition. Feed-forward neural networks and simplified equations developed from nonlinear multiple regression analysis were proposed to evaluate the initial flow and 28-day compressive strength of AA mortars. The training and testing of neural networks, and calibration of the simplified equations were achieved using a comprehensive database of 82 test results of mortars activated by sodium silicate and sodium hydroxide powders. Compressive strength development of GGBS-based alkali-activated mortars was also estimated using the formula specified in ACI 209 calibrated against the collected database. Predictions obtained from the trained neural network or developed simplified equations were in good agreement with test results, though early strength of GGBS-based alkali-activated mortars was slightly overestimated by the proposed simplified equations.

This thesis studies a new more affordable way to build sidewalks in the U.S. Typical sidewalks are often impractical on many roads because of a steep runoff slope and/or close proximity to the drainage ditch. Also, if future road widening is required, the sidewalk must be removed. This thesis proposes a structure called a Lanwalk which is an elevated sidewalk made of precast units. A Lanwalk could simultaneously serve as a sidewalk and potentially as a guardrail. It can be placed over drainage areas if necessary without obstructing the flow of water. Lanwalks can be easily installed and relocated if necessary. This thesis examines the possibility of using high amounts of waste ash as an admixture during the construction of Lanwalks or sidewalks to lower cost and save landfill space. The two waste products examined are municipal solid waste incinerator bottom ash (IBA) and coal fly ash (CFA).

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Yes / The present study investigated long-term drying shrinkage strains of self-compacting concrete (SCCs). For all SCCs mixes, Portland cement was replaced with 0–60% of fly ash (FA), fine and course aggregates were kept constant with 890 kg/m3 and 780 kg/m3, respectively. Two different water binder ratios of 0.44 and 0.33 were examined for both SCCs and normal concrete (NCs). Fresh properties of SCCs such as filling ability, passing ability, viscosity and resistance to segregation and hardened properties such as compressive and flexural strengths, water absorption and density of SCCs and NCs were also determined. Experimental results of drying shrinkage were compared to five existing models, namely the ACI 209R-92 model, BSEN-92 model, ACI 209R-92 (Huo) model, B3 model, and GL2000. To assess the quality of predictive models, the influence of various parameters (compressive strength, cement content, water content and relative humidity) effecting on the drying shrinkage strain as considered by the models are studied. The results showed that, using up to 60% of FA as cement replacement can produce SCC with a compressive strength as high as 30 MPa and low drying shrinkage strain. SCCs long-term drying shrinkage from 356 to 1000 days was higher than NCs. ACI 209R-92 model provided a better prediction of drying shrinkage compared with the other models. / Financial support of Higher Education of Libya (469/2009).